农业机械化生产专家系统设计与开发
|
摘要
农业信息化建设是解决“三农”问题和新农村建设的重要手段和途径。农业机械化作为农业的主要生产力,农业机械化信息化问题也倍受关注。近几年,相关农机部门及农机经营者有条件购置和使用计算机,而且涌现了很多农业机械化信息网络以及相关技术,使农业机械化信息化水平得以提高。尽管如此,目前农业机械化生产中适用的软件相对很少,设计、开发适用软件可以有效提高农业机械化技术的应用水平及效果,进而提升农业生产的效益,提高农民的收入,同时促进农村经济的发展,同时也可以有效提高农业机械化信息化水平。本论文就是在此思想指导下,结合农业机械化生产实践要求、结合国家“863”项目及黑龙江省科技厅科技攻关项目的具体要求而设计开发“农业机械化生产专家系统(AMPES)”。
设计开发AMPES不但可以满足目前农业机械化生产领域信息化发展的需求,而且可以促进农业信息化、发展数字农业、建设社会主义新农村的重要内容之一。探讨农业机械化生产专家系统设计开发的基本理论、研究农业机械化生产专家系统的功能以及设计和开发方法,又可以为农业机械化其他领域专家系统的设计与开发探明道路和方向,为进一步发展农业信息化奠定基础。本课题研究具有重要的理论和现实意义。
作者查阅了大量的相关文献,掌握国内外农业机械化生产领域专家系统研究、应用动态及发展趋势,提出我国农业机械化生产专家系统存在的具体问题,并详细叙述了专家系统的概念、结构、特点以及开发方法。以此为基础,设计开发了单机版的农业机械化生产专家系统。本论文的主要研究内容包括农业机械化生产专家系统分析和总体功能设计、各模块详细设计、系统数据库设计、知识获取方式研究、知识表示方法及知识库设计、系统推理策略研究和推理机设计、系统开发与实现等几个部分。
研究过程中,力求理论与实践相结合。采用自上而下的方法进行系统分析和总体功能设计、各模块详细设计;采取深入生产实际调查研究、走访领域专家、自行研究设计并摄制录像等方法,获取大量领域知识;按照层次原则设计数据库和知识库,并设计开发数据库/知识库管理系统。本论文设计和开发了人工知识获取、半自动知识获取与自动知识获取相结合的知识获取机制,采用产生式规则和语义网络方法表示本系统领域知识,从而使专家系统设计和开发过程中的“瓶颈”问题得到很好地解决。以此为基础,对推理策略进行深入分析与探讨,采用正向推理和广度优先搜索策略,引用基于实例的推理思想,设计基于产生式规则的推理机和基于语义网络的推理机;采用启发式思想,依据知识库知识组织顺序以及知识的解释说明进行冲突消解。本论文采用Visual BASIC 6.0程序开发语言和Microsoft Access数据库系统,采用原型法、自上而下、自下而上相结合的方法,开发实现了单机版的“农业机械化生产专家系统(AMPES)”。
在研究过程中,取得的主要成果有:
(1)通过系统分析,明确了系统的总体功能,设计了系统总体架构。进行了系统功能模块划分,并详细设计了各模块功能、工作原理及运行流程,设计了各模块数据库、知识库,为系统详细设计和开发工作奠定基础。
(2)首次设计并开发了包括农业机械化作业工艺过程及规范、农业机器选型配备和农业机器技术保养三个主要功能模块的单机版“农业机械化生产专家系统”。该系统可以实现不同地区、不同种植结构和种植方式条件下农业机械化作业工艺过程制定、作业工艺规范咨询、农业机器选型与配备、农业机器技术保养规范咨询、保养计划制定以及农业机器信息浏览等功能。
(3)根据系统要求,深入生产实际进行大量调查研究,获取大量的文字、图片及音像等资料,并对其进行了详细整理和归纳。同时,自行研究设计脚本、摄制并制作部分型号拖拉机技术保养操作过程的录像、拍摄大量照片,为系统设计和开发提供必备的知识,增强了AMPES知识表达直观性,提高了系统界面友好程度。
(4)对AMPES知识获取问题进行了重点研究,确定的人工获取与半自动获取、自动获取相结合的知识获取机制,设计并实现了领域知识的半自动获取和自动获取功能。有效解决了专家系统设计和开发过程中知识获取这一“瓶颈”问题。提高了知识的可信度,缩短了知识获取的周期,提高了系统效率和咨询效果。
(5)根据AMPES及各模块功能要求,分层次设计数据库,对综合数据库和各模块数据库的关系、层次结构、数据库中所含表的数量及表的具体结构进行了详细设计。设计开发基于自身的三层数据库的数据库/知识库管理系统(DBMS/KBMS),不但可以实现数据库/知识库的常规维护功能,而且该模块依据自身的数据库结构及内容自动加载AMPES系统所有数据库和知识库,使DBMS/KBMS程序代码完全脱离AMPES数据库而存在,提高了系统的适应性,也简化了代码维护工作。
(6)根据AMPES所涉及知识的特点,采用常规的产生式规则表示农业机械化作业工艺过程、农业机器选型等领域知识;根据源作业工艺过程知识特点,探讨并采用语义网络方法表示源机械化作业工艺过程的专家知识。实际应用证明,系统研究设计的知识表示方法有效实用,便于实现系统功能。
(7)研究了AMPES推理控制策略,设计了基于产生式规则的推理机,包括基本工艺过程推理机、专家选型推理机、普通用户选型推理机等;设计了基于语义网络知识的源工艺过程推理机。同时,引用基于实例的推理思想,恰当地解决了基本工艺过程推理问题,也提高了基本工艺过程知识库知识自动获取能力。
(8)采用正向推理方式、广度优先搜索策略设计AMPES推理过程;采用启发式思想,依据知识库知识组织顺序以及知识的解释说明进行冲突消解。针对工艺过程制定和农业机器选型等比较复杂的推理问题,采用相关推理机串联或并联使用、逐层推理的推理策略完成推理过程设计,有效地实现了系统功能。
(9)结合系统需要,设计开发了生产率法、能量法农业机器配备程序,实现了计算机自动划分作业阶段、各作业阶段负荷值的计算以及作业负荷的调整等功能,同时设计开发了基于计算法的农业机器技术保养计划制定程序。
(10)本论文开发“农业机械化生产专家系统”实现了系统代码与数据库相对独立、知识库与推理机相分离。在系统数据库、知识库变化后,不影响系统功能的实现,系统适应性强。
Agricultural informationization construction is a important approach to solve problems about agriculture, ruralareas and peasantry, is a important way to construct new countrysides in China. As the main agricultural productivity, agricultural mechanization information was paid much attention. In resent years, agricultural machinery departments and operators have abilities to buy and use computers. Many information websites and correlative technology have been putting into use. All of these can enhance the level of agricultural mechanization information. Even though, the amount of the effective softwares for agricultural mechanization cannot satisfy the demand. Under these circumstances, to design and develop the effective software can promote the applying level and effect of agricultural mechanization technology, can promote the agricultural benefits, rise the peasantry's revenue, meanwhile, can promote agricultural economic development. The agricultural mechanization production expert system (AMPES) was designed and developed under the guidance of this thought and according to requests of the national "863" research plan and key projects of science and technology office of Heilongjiang province.
To design and develop AMPES can satisfy the demand of the field of agricultural mechanizing production, but is also one of the important content of promoting agricultural information, developing digital agriculture and constructing new countryside. To discuss the basic theory, function and methods of designing and developing AMPES can lead the direction for designing and developing other expert system in the field of agricultural mechanizing production, can make a foundation for developing agricultural information. This research has important significance both in theory and practice.
Researching and applying status and developing trends of expert systems in internal and external agricultural mechanization production field were studied on the basis of a mass of related literatures. Problems of agricultural mechanization expert systems applied in our country were pointed out. Concepts, the structure, characteristics and developing methods of the expert system were also detailedly discussed in the dissertation. Main contents of this research include general function design of AMPES, detailed design of modules, design of system databases and research of knowledge acquisition strategies, research of knowledge presentation and knowledge bases, design of management systems of databases and knowledge bases(DBMS/KBMS), research of inference strategies and design of inference engines, development of AMPES and so on.
Combining theories with practice is one of the main principles of this research. System analyzing, general designing and modules detailedly designing were accomplished using the top to down method. Great deals of field knowledge had obtained by methods, such as in-depth investigations and studies, visiting field experts, designing and producing videos, and so on. Databases, knowledge bases of AMPES were designed according to hierarchy principle, and DBMS/KBMS were also designed and developed under the same principle. Knowledge acquisition (KA) mechanism had designed and developed which combined artificial KA, semiautomatic KA and automatic KA, and rule-based and semantic network methods were used to represent field knowledge after relative research. These research work effectively solve the bottleneck problem in expect system designing and developing course. On basis that, inference strategies had been analyzed and discussed in depth, forward reasoning and width-first search strategies were adopted, the case-base reasoning thought was also introduced to design inference engines. Conflicts could be resolved according to organizing order and explanation of knowledge with heuristic thought. Visual Basic 6.0 and Microsoft Access were adopted to develop Windows-based AMPES with the methods of prototyping, top-down and down to top.
Main conclusions of the dissertation are as follows:
(1) The general structure of AMPES was designed and the modules were compartmentalized on the basis of the analysis of the system function. Functions, operating thought and process of each modules were designed detailedly. Databases and knowledge bases were designed and their relation to system function is explicit. All of these are the foundation for the further development of AMPES.
(2) AMPES was designed and realized for the first time, which includes three main functional modules which are mechanized technology process and operating criterions module, agricultural machinery selecting and equipping module and agricultural machinery maintenance module. AMPES can realize all functions under different area, different plant structure and sow manner.
(3) Knowledge resources of AMPES were studied. Mass of character, photo and video materials were obtained after consulting and dealing mass of literatures, making many investigations in the practice. Field knowledge of AMPES is abundant and represented in variety type. videos about the some types of tractor technology maintenance were produced on the basis of scripts designed by the item group according to the system requirement and lots of photos were shoot. These videos and photos can make knowledge of AMPES more abundant, can enhance the knowledge presentation more intuitionistic and can make interfaces more friendly.
(4) Knowledge acquisition (KA) of AMPES is one of the important researches of the dissertation. Semiautomatic KA and automatic KA mechanism were designed. The semiautomatic KA can be realized after knowledge engineers manage knowledge bases according to suggestions provided when experts operated AMPES. Knowledge can be added and renewed automatically after experts modify the consulted results directly and then saved in knowledge bases. Before using the system in each area, running the module can effectively avoid shortcomings of the inaccurate, incomplete knowledge and other disadvantages in the course of investigation. The credibility of the knowledge can be improved, the cycle of knowledge acquisition can be shortened and system efficiency and advisory effect can be improved under the KA strategies designed in the dissertation.
(5) Database design and DBMS/KBMS are characteristics of the system. According to AMPES and the function requirements of each module, database were design in hierarchical, and relations between integrated databases and module databases, hierarchical structure, the quantity of tables in database and the specific structure of tables were designed detailedly. and DBMS/KBMS was designed and developed based on the three-layer databases, not only can make the conventional maintenance functions of database / knowledge base come true, but the module can automatically load all databases and knowledge bases of AMPES through the structure and content of its own databases. DBMS / KBMS code existing independently, can enhances the adaptability of the system, and simplifies code maintenance work.
(6) According to the characteristics of field knowledge AMPES involves, rule-based method was adopted to express field knowledge such as technology process of mechanized operating and agricultural machinery selection. According to the characteristics of source technology process, the semantic network method was discussed and adopted to express the expert knowledge of the source technology process. Practical application of the system proved that knowledge representation methods designed are effective and practical, are propitious to realize system function.
(7) Inference strategies of AMPES were studied. Rule-based inference engines were designed such as basic technology process inference engine, expert's agricultural machinery selection inference engine, ordinary user's agricultural machinery selection inference engine. Source technology process inference engine was designed, which is based on semantic network knowledge representing method. Meanwhile, case-base reasoning thought was also introduced to solve basic technology process inference problem appropriately, to improve the ability of automatic knowledge acquisition.
(8) AMPES inference process was designed adopting forward reference and width-first search strategies. Conflicts can be resolved adopting the heuristic thought according to the knowledge organization order and the knowledge explanations. Some complicated inference problems, such as the technology process making and machinery selection, inference processes were realized by means of the strategies using the different inference engines in series in parallel. AMPES functions were realized effectively adopting these strategies.
(9) According to the system demand, programs to calculate the amount of agricultural machinery were designed and realized. Functions, such as operating period compartmentalizing automatically, operating loads calculating in each operating period and operating load adjusting were also realized. At the same time, program for making farm machinery maintenance plan was also design and realized based on the calculation method.
(10) AMPES can well adapt to changes of databases or knowledge bases because it was realized under the principle of independently programming. AMPES has more advantages according to the production tests in practice, such as perfect functions, friendly interfaces, rapid knowledge acquisition, convenient operation and maintenance, applicable knowledge representation strategies and effective inference engines designed reasonably and so on.
设计开发AMPES不但可以满足目前农业机械化生产领域信息化发展的需求,而且可以促进农业信息化、发展数字农业、建设社会主义新农村的重要内容之一。探讨农业机械化生产专家系统设计开发的基本理论、研究农业机械化生产专家系统的功能以及设计和开发方法,又可以为农业机械化其他领域专家系统的设计与开发探明道路和方向,为进一步发展农业信息化奠定基础。本课题研究具有重要的理论和现实意义。
作者查阅了大量的相关文献,掌握国内外农业机械化生产领域专家系统研究、应用动态及发展趋势,提出我国农业机械化生产专家系统存在的具体问题,并详细叙述了专家系统的概念、结构、特点以及开发方法。以此为基础,设计开发了单机版的农业机械化生产专家系统。本论文的主要研究内容包括农业机械化生产专家系统分析和总体功能设计、各模块详细设计、系统数据库设计、知识获取方式研究、知识表示方法及知识库设计、系统推理策略研究和推理机设计、系统开发与实现等几个部分。
研究过程中,力求理论与实践相结合。采用自上而下的方法进行系统分析和总体功能设计、各模块详细设计;采取深入生产实际调查研究、走访领域专家、自行研究设计并摄制录像等方法,获取大量领域知识;按照层次原则设计数据库和知识库,并设计开发数据库/知识库管理系统。本论文设计和开发了人工知识获取、半自动知识获取与自动知识获取相结合的知识获取机制,采用产生式规则和语义网络方法表示本系统领域知识,从而使专家系统设计和开发过程中的“瓶颈”问题得到很好地解决。以此为基础,对推理策略进行深入分析与探讨,采用正向推理和广度优先搜索策略,引用基于实例的推理思想,设计基于产生式规则的推理机和基于语义网络的推理机;采用启发式思想,依据知识库知识组织顺序以及知识的解释说明进行冲突消解。本论文采用Visual BASIC 6.0程序开发语言和Microsoft Access数据库系统,采用原型法、自上而下、自下而上相结合的方法,开发实现了单机版的“农业机械化生产专家系统(AMPES)”。
在研究过程中,取得的主要成果有:
(1)通过系统分析,明确了系统的总体功能,设计了系统总体架构。进行了系统功能模块划分,并详细设计了各模块功能、工作原理及运行流程,设计了各模块数据库、知识库,为系统详细设计和开发工作奠定基础。
(2)首次设计并开发了包括农业机械化作业工艺过程及规范、农业机器选型配备和农业机器技术保养三个主要功能模块的单机版“农业机械化生产专家系统”。该系统可以实现不同地区、不同种植结构和种植方式条件下农业机械化作业工艺过程制定、作业工艺规范咨询、农业机器选型与配备、农业机器技术保养规范咨询、保养计划制定以及农业机器信息浏览等功能。
(3)根据系统要求,深入生产实际进行大量调查研究,获取大量的文字、图片及音像等资料,并对其进行了详细整理和归纳。同时,自行研究设计脚本、摄制并制作部分型号拖拉机技术保养操作过程的录像、拍摄大量照片,为系统设计和开发提供必备的知识,增强了AMPES知识表达直观性,提高了系统界面友好程度。
(4)对AMPES知识获取问题进行了重点研究,确定的人工获取与半自动获取、自动获取相结合的知识获取机制,设计并实现了领域知识的半自动获取和自动获取功能。有效解决了专家系统设计和开发过程中知识获取这一“瓶颈”问题。提高了知识的可信度,缩短了知识获取的周期,提高了系统效率和咨询效果。
(5)根据AMPES及各模块功能要求,分层次设计数据库,对综合数据库和各模块数据库的关系、层次结构、数据库中所含表的数量及表的具体结构进行了详细设计。设计开发基于自身的三层数据库的数据库/知识库管理系统(DBMS/KBMS),不但可以实现数据库/知识库的常规维护功能,而且该模块依据自身的数据库结构及内容自动加载AMPES系统所有数据库和知识库,使DBMS/KBMS程序代码完全脱离AMPES数据库而存在,提高了系统的适应性,也简化了代码维护工作。
(6)根据AMPES所涉及知识的特点,采用常规的产生式规则表示农业机械化作业工艺过程、农业机器选型等领域知识;根据源作业工艺过程知识特点,探讨并采用语义网络方法表示源机械化作业工艺过程的专家知识。实际应用证明,系统研究设计的知识表示方法有效实用,便于实现系统功能。
(7)研究了AMPES推理控制策略,设计了基于产生式规则的推理机,包括基本工艺过程推理机、专家选型推理机、普通用户选型推理机等;设计了基于语义网络知识的源工艺过程推理机。同时,引用基于实例的推理思想,恰当地解决了基本工艺过程推理问题,也提高了基本工艺过程知识库知识自动获取能力。
(8)采用正向推理方式、广度优先搜索策略设计AMPES推理过程;采用启发式思想,依据知识库知识组织顺序以及知识的解释说明进行冲突消解。针对工艺过程制定和农业机器选型等比较复杂的推理问题,采用相关推理机串联或并联使用、逐层推理的推理策略完成推理过程设计,有效地实现了系统功能。
(9)结合系统需要,设计开发了生产率法、能量法农业机器配备程序,实现了计算机自动划分作业阶段、各作业阶段负荷值的计算以及作业负荷的调整等功能,同时设计开发了基于计算法的农业机器技术保养计划制定程序。
(10)本论文开发“农业机械化生产专家系统”实现了系统代码与数据库相对独立、知识库与推理机相分离。在系统数据库、知识库变化后,不影响系统功能的实现,系统适应性强。
Agricultural informationization construction is a important approach to solve problems about agriculture, ruralareas and peasantry, is a important way to construct new countrysides in China. As the main agricultural productivity, agricultural mechanization information was paid much attention. In resent years, agricultural machinery departments and operators have abilities to buy and use computers. Many information websites and correlative technology have been putting into use. All of these can enhance the level of agricultural mechanization information. Even though, the amount of the effective softwares for agricultural mechanization cannot satisfy the demand. Under these circumstances, to design and develop the effective software can promote the applying level and effect of agricultural mechanization technology, can promote the agricultural benefits, rise the peasantry's revenue, meanwhile, can promote agricultural economic development. The agricultural mechanization production expert system (AMPES) was designed and developed under the guidance of this thought and according to requests of the national "863" research plan and key projects of science and technology office of Heilongjiang province.
To design and develop AMPES can satisfy the demand of the field of agricultural mechanizing production, but is also one of the important content of promoting agricultural information, developing digital agriculture and constructing new countryside. To discuss the basic theory, function and methods of designing and developing AMPES can lead the direction for designing and developing other expert system in the field of agricultural mechanizing production, can make a foundation for developing agricultural information. This research has important significance both in theory and practice.
Researching and applying status and developing trends of expert systems in internal and external agricultural mechanization production field were studied on the basis of a mass of related literatures. Problems of agricultural mechanization expert systems applied in our country were pointed out. Concepts, the structure, characteristics and developing methods of the expert system were also detailedly discussed in the dissertation. Main contents of this research include general function design of AMPES, detailed design of modules, design of system databases and research of knowledge acquisition strategies, research of knowledge presentation and knowledge bases, design of management systems of databases and knowledge bases(DBMS/KBMS), research of inference strategies and design of inference engines, development of AMPES and so on.
Combining theories with practice is one of the main principles of this research. System analyzing, general designing and modules detailedly designing were accomplished using the top to down method. Great deals of field knowledge had obtained by methods, such as in-depth investigations and studies, visiting field experts, designing and producing videos, and so on. Databases, knowledge bases of AMPES were designed according to hierarchy principle, and DBMS/KBMS were also designed and developed under the same principle. Knowledge acquisition (KA) mechanism had designed and developed which combined artificial KA, semiautomatic KA and automatic KA, and rule-based and semantic network methods were used to represent field knowledge after relative research. These research work effectively solve the bottleneck problem in expect system designing and developing course. On basis that, inference strategies had been analyzed and discussed in depth, forward reasoning and width-first search strategies were adopted, the case-base reasoning thought was also introduced to design inference engines. Conflicts could be resolved according to organizing order and explanation of knowledge with heuristic thought. Visual Basic 6.0 and Microsoft Access were adopted to develop Windows-based AMPES with the methods of prototyping, top-down and down to top.
Main conclusions of the dissertation are as follows:
(1) The general structure of AMPES was designed and the modules were compartmentalized on the basis of the analysis of the system function. Functions, operating thought and process of each modules were designed detailedly. Databases and knowledge bases were designed and their relation to system function is explicit. All of these are the foundation for the further development of AMPES.
(2) AMPES was designed and realized for the first time, which includes three main functional modules which are mechanized technology process and operating criterions module, agricultural machinery selecting and equipping module and agricultural machinery maintenance module. AMPES can realize all functions under different area, different plant structure and sow manner.
(3) Knowledge resources of AMPES were studied. Mass of character, photo and video materials were obtained after consulting and dealing mass of literatures, making many investigations in the practice. Field knowledge of AMPES is abundant and represented in variety type. videos about the some types of tractor technology maintenance were produced on the basis of scripts designed by the item group according to the system requirement and lots of photos were shoot. These videos and photos can make knowledge of AMPES more abundant, can enhance the knowledge presentation more intuitionistic and can make interfaces more friendly.
(4) Knowledge acquisition (KA) of AMPES is one of the important researches of the dissertation. Semiautomatic KA and automatic KA mechanism were designed. The semiautomatic KA can be realized after knowledge engineers manage knowledge bases according to suggestions provided when experts operated AMPES. Knowledge can be added and renewed automatically after experts modify the consulted results directly and then saved in knowledge bases. Before using the system in each area, running the module can effectively avoid shortcomings of the inaccurate, incomplete knowledge and other disadvantages in the course of investigation. The credibility of the knowledge can be improved, the cycle of knowledge acquisition can be shortened and system efficiency and advisory effect can be improved under the KA strategies designed in the dissertation.
(5) Database design and DBMS/KBMS are characteristics of the system. According to AMPES and the function requirements of each module, database were design in hierarchical, and relations between integrated databases and module databases, hierarchical structure, the quantity of tables in database and the specific structure of tables were designed detailedly. and DBMS/KBMS was designed and developed based on the three-layer databases, not only can make the conventional maintenance functions of database / knowledge base come true, but the module can automatically load all databases and knowledge bases of AMPES through the structure and content of its own databases. DBMS / KBMS code existing independently, can enhances the adaptability of the system, and simplifies code maintenance work.
(6) According to the characteristics of field knowledge AMPES involves, rule-based method was adopted to express field knowledge such as technology process of mechanized operating and agricultural machinery selection. According to the characteristics of source technology process, the semantic network method was discussed and adopted to express the expert knowledge of the source technology process. Practical application of the system proved that knowledge representation methods designed are effective and practical, are propitious to realize system function.
(7) Inference strategies of AMPES were studied. Rule-based inference engines were designed such as basic technology process inference engine, expert's agricultural machinery selection inference engine, ordinary user's agricultural machinery selection inference engine. Source technology process inference engine was designed, which is based on semantic network knowledge representing method. Meanwhile, case-base reasoning thought was also introduced to solve basic technology process inference problem appropriately, to improve the ability of automatic knowledge acquisition.
(8) AMPES inference process was designed adopting forward reference and width-first search strategies. Conflicts can be resolved adopting the heuristic thought according to the knowledge organization order and the knowledge explanations. Some complicated inference problems, such as the technology process making and machinery selection, inference processes were realized by means of the strategies using the different inference engines in series in parallel. AMPES functions were realized effectively adopting these strategies.
(9) According to the system demand, programs to calculate the amount of agricultural machinery were designed and realized. Functions, such as operating period compartmentalizing automatically, operating loads calculating in each operating period and operating load adjusting were also realized. At the same time, program for making farm machinery maintenance plan was also design and realized based on the calculation method.
(10) AMPES can well adapt to changes of databases or knowledge bases because it was realized under the principle of independently programming. AMPES has more advantages according to the production tests in practice, such as perfect functions, friendly interfaces, rapid knowledge acquisition, convenient operation and maintenance, applicable knowledge representation strategies and effective inference engines designed reasonably and so on.
引文
敖志刚.2002.人工智能与专家系统[M].中国科学技术大学出版社
鲍一丹,何勇.2001.农业机械多媒体决策支持系统的研究[J].浙江大学学报(农业与生命科学版).(2):187-190
蔡自兴,徐光佑.2003.人工智能及其应用[M].北京:清华大学出版社
蔡自兴,约翰·德尔金[美],龚涛.2005.高级专家系统:原理、设计及应用[M].北京:科学出版社
陈海涛,刘宏新,乔金友等.2000.J.D.-4450拖拉机技术经济性指标随机龄的变化规律[J].农机化研究,(2):118-121
陈济勤.2001.农业机器运用管理学[M].北京:中国农业出版社
陈清云,李鸿.2001.黄瓜温室栽培管理专家系统研究[J].农业工程学报,(6):142-146
陈立辉.2003.农机作业委托决策支持系统(CHSDSS)设计与开发[D].吉林大学硕士学位论文
陈雁青,王立才,刘强.1987.微型计算机在农机信息管理上的应用[J].农机化研究,(2)
程耀.1984.友谊农场五分场一队田间作业机器选型配备优化设计[D].东北农学院博士学位论文
迟媛.2002.面向用户的农机液压系统故障诊断专家系统的设计与实现[D].东北农业大学硕士学位论文
杜璟.2003.基于Web的农业机械化发展决策支持系统[D].中国农业大学硕士学位论文
段韶芬,李福超,郑国清.2003.农业专家系统研究进展及展望[J].农业图书情报学刊,(5):15-18.
段韶芬,郑国清.2003.农业专家系统研究之管见[J].计算机与农业,(8):3-5
高洪伟.2004.稻麦收获机械选型及其决策支持系统的研究[D].南京农业大学硕士学位论文
高焕文.1998.高等农业机械化管理学[M].北京:中国农业大学出版社
高济,朱淼良.2002.人工智能基础[M].北京:高等教育出版社
高明亮,王雪珍,吴顺章.2001.农业专家系统存在的问题与对策[J].洛阳农业高等专科学校学报,(2):88-90
郭志达.2004.农机液压故障模糊诊断专家系统的研究[D].东北农业大学硕士学位论文
韩聚奎,张敏.1999.农业机械液压系统故障诊断专家系统的研究[J].农业机械学报,(3):67-70
韩宽襟,万鹤群,高焕文.1990.我国农业机器配备的特点及典型模型研究[J].北京农业工程大学学报,(4):1-6
韩宽襟,高焕文,万鹤群.1991.微机用农业机器作业系统优化大型软件系统FMS的研制[J].北京农业工程大学学报,(1)
何勇,鲍一丹.2001.基于神经网络的发动机故障多媒体诊断专家系统[J].内燃机学报,(3):245-248
何勇,鲍一丹,洪晓莉等.1995.农业机械化数据库管理系统研究[J].科技通报,(3):147-151
黑龙江八一农垦大学.1983.农业机器运用学[M].北京:中国农业出版社
洪国彬.1995.专家系统中的知识获取问题[J].华侨大学学报(自然科学版),16(4):451-454
胡明晖.2005.基于知识源的专家系统知识获取技术[J].成组技术与生产现代,(2):25-28
胡文斌.2001.现代农业企业机器选型配套系统的开发与研究[D].华中农业大学硕士学位论文
黄贵平,杨林,任明见等.2003.专家系统及其在农业上的应用[J].种子,(1):54-57
黄梯云,李一军.2000.管理信息系统[M].北京:高等教育出版社
黄志勇,杜会清,黄魏等.2003.网络化复合肥优化配料专家系统的开发[J].计算机与农业,(3):10-12
金炯官,金锡禧.1984.用微型电子计算机合理选配农业机械的基本方法[J].延边大学农学学报,(1)
金秀满.1989.田间作业机组耗油指标的模拟与分析[J].农机化研究,(4)
金秀满,张吉营.1990.铁牛-55拖拉机运输作业耗油量计算机模拟方法的试验研究[J].农机化研究,(4)
李道亮,傅泽田,马莉等.2000.智能化水产养殖信息系统与初步实现[J].农业工程学报,(4):135-138
李道亮,傅泽田,田东.2004.智能系统:基础、方法及其在农业中的应用[M].北京:清华大学出版社
李军政.2004.洞庭湖区农田动力机器系统的优化配备研究[D].湖南农业大学硕士学位论文
李晓勤.2004.固定资产折旧方法及模型研究[D].东北农业大学硕士学位论文
李小燕,雷勇辉,张建华等.2004.新疆棉花病虫害管理专家系统[J].中国棉花(5):32.
李骅.2003.基于Web数据库的农机化统计管理信息设计[D].南京农业大学硕士学位论文
李骅,陈红星,陈鹏等.2004.农机管理信息系统的现状与展望[J].农机化研究,(5):56-58.
李增芳.2004.基于人工智能和虚拟仪器技术的发动机故障诊断专家系统研究[D].浙江大学博士学位论文
李中才.2005.农业机器系统状态仿真及更新决策的研究[D].东北农业大学博士学位论文
刘继芬,聂凤,英王平等.2004.世界主要国家农业专家系统、计算机决策系统和计算 机模拟模型技术的应用现状[J].农业网络信息,(5):42-43
刘健.1994.农业机械维修制度辅助决策专家系统[J].河南农业大学学报,(1):72-78
刘晓燕.1997.回顾与展望.专家系统在我国农业上应用情况概述[J].计算机与农业,(1):1-3
刘兆友,何方,夏敏.2001.耕地适宜性评价专家系统的建立与应用[J].安徽农业大学学报,28(3):326-330
刘滨,位绍文.2003.我国农业专家系统研究应用与展望[J].莱阳农学院学报,(3):229-231
刘炳文,许蔓舒.2000.Visual Basic程序设计教程[M].北京:清华大学出版社
刘文涛.2006.Visual BASIC+ACCESS数据库开发与实例[M].北京:清华大学出版社
马文杰,贺立源.2005.专家系统在我国农业中的应用进展.山地农业生物学报,(4)343-346
闵联营.1999.知识获取技术探析[J].交通与计算机,(10):32-35
潘大丰,程季珍,李群等.2000.蔬菜施肥专家系统[J].华北农学报,(3):118-121
潘文聪,郭志民,陈永宝.2002.水土保持规划专家系统初探[J].福建水土保持,(4):29-31
裴新国,丛群滋.2003.Web网站上果树专家系统开发与应用[J].山东农业科学,(3):45-48
齐新丹.2000.农机统计管理计算机软件的开发[J].南京化工大学学报(自然科学版),(6)
钱宇华.1997.论专家系统中的知识获取问题[J].北京第二外国语学院学报,(3):122-126
乔金友,王福林,丛昕等.2007.水田耕地机组技术经济性能评价方法与应用[J].东北农业大学学报,(1):78-81
秦来寿.2005.农业专家系统发展概况及趋势分析[J].世界农业,(12):45-48
邱建军,肖荧南,戴逸民等.2002.基于模拟模型的棉花生产管理系统研究[J].农业工程学报,(6):161-164
邱立春.1991.农机化管理信息系统数据库设计[J].农机化研究,(2)
求是科技.2004.Visual BASIC 6.0程序设计与开发技术大全[M].北京:人民邮电出版社
上官周平.1994.农业专家系统及其应用[J].农业现代化研究,(5):298-301
宋兆华,杨宇姝,周大帅.2002.垦区智能化农业专家系统的应用与探讨[J].现代化农业,(1):33-35
孙赴.1988.概述计算机在农业上的应用[J].黑龙江省农业科学,(5)
孙旭武,李唯,闫丽娟.2002.网络农业专家系统的设计与开发[J].计算机与农业,(6):6-7
孙永厚,张志彦,刘志鸿.1999.农机液压系统故障诊断专家系统知识获取的研究[J].农机化研究,(2):39-42
孙永厚,刘志鸿,郝德刚等.1999.农机液压系统故障诊断专家系统中推理控制策略的研究[J].农机化研究,(4):27-30
孙曰波,赵波秀,李寿冰.2006.农业专家系统发展的概况与前景[J].潍坊高等职业教育,(2):39-41
唐晓敏,徐立鸿,恽源世.2005.基于实例推理及其在农业病虫害诊断与防治中的应用研究[J].中国农机化,(1):56-59
谭文学.2003.基于启发式规则推理的多媒体农业专家系统开平台的开发与研究[J].计算机与农业,(7)
韦于莉.2004.知识获取研究[J].情报杂志,(4):41-43
王福林.1988.黑龙江省国营农场麦豆收获机械化系统分析[D].东北农学院硕士学位论文
王福林.1993.农业机器经济寿命与更新换代经济效果及其评价问题的研究[D].东北农业大学博士学位论文
王立舒,戚国强,杨宇姝.2003.产生式系统在农业专家系统中的应用[J].农机化研究,(1):132-134
王茂辉,郑彦波,娄琳.2005.农机液压系统故障诊断专家系统的设计[J].黑龙江八一农垦大学学报,(1):43-46
王庆成,刘开昌,张秀清等.2002.日光温室黄瓜栽培管理专家系统的开发[J].山东农业科学,(4):14-16
王文娥,汪志农,马孝义.2000.农业专家系统及其在灌溉管理中的应用[J].计算机与农业,(8):5-7
王小军,周昌盛,吕汉兴.2005.一种基于关系数据库的知识表示和推理方法[J].控制工程,(1):40-43
王永庆,上官周平.1995.专家系统在农业生产管理中的应用[J].微机发展,(1):3-6
王子跃,王丽娟,李照美等.1999.陶志祥汽车发动机故障诊断专家系统[J].河南农业大学学报,(3):293-295
吴玺,谭宏,夏建国等.2000.试论我国农业专家系统的应用与发展[J].计算机与农业,(8):1-4
武波,马玉祥.2001.专家系统[M].北京:北京理工大学出版社
武华,孙永厚,刘贤喜等.1999.农机液压系统故障诊断专家系统的研究[J].农业工程学报,(1):183-187
项利国.2004.基于网络的农业机械装备管理信息系统的研究与实现[D].浙江大学硕士学位论文
辛惠娟,戴庆辉.2005.汽车发动机故障诊断专家系统研究[J].煤矿机械,2005(2)
辛惠娟,钱东平,李志芳.2006.基于ASP网络开发汽车发动机故障诊断专家系统[J].农机化研究,(2)
熊范纶.1999.农业专家系统及开发工具[M].北京:清华大学出版社
徐安东,李飞,邢晓怡.2006.Visual BASIC数据库应用开发教程[M].北京:清华大 学出版社
晏国生.1998.计算机在农机化管理中的应用[M].北京:清华大学出版社
燕学智,钱耀义.2001.基于人工神经网络技术的发动机故障诊断系统[J].内燃机工程,(1):78-81
杨保祝,赵春江,孙想等.2002.节水灌溉专家决策系统的研究与应用[J].节水灌溉,(2):17-19
杨国强,王双喜,杜伟.2004.我国农业专家系统的研究进展[J].山西农业大学学报,(3):303-305
杨宇姝,宋兆华,丁华福等.2002.农业专家系统知识获取组织的研究与应用[J].农业系统科学与综合研究,(2):152-154
姚彤.2000.农机销售管理信息系统的设计与开发[D].东北农业大学硕士学位论文
叶继昌,王登峰,杨印生等.1995.农业机械性能评价及选型专家系统的设计[J].农业机械学报,(1):11-14
尹朝庆,尹皓.2001.人工智能与专家系统[M].北京:中国水利水电出版社
余心杰.2004.基于Web GIS的农机化信息管理系统研究[D].浙江大学硕士学位论文
袁春新,郭建华,魏亚凤等.2003.农业专家系统及其在水稻栽培中的应用[J].西南农业学报,(4):130-133
袁黎.2003.农机监理信息系统的研制与开发[D].河南农业大学硕士学位论文
袁泉,胡晓卫,何勇.1998.神经网络技术用于发动机故障诊断的研究[J].农业机械学报,(3):104-107
曾路,苗新明,张兴亚等.2004.农业信息技术应用现状及发展趋势[J].信息技术应用,(2):42-44
张海云.2003.农牧业机械多媒体管理信息系统研究[J].河北农业大学学报,(2):97-100
张清华,陈佰军.1994.机务工作计划计算机管理系统研究[J].黑龙江八一农垦大学学报,(4):68-73
张清华,郭仁华,蒋涛.1995.联合收获机计算机辅助选型配备[J].农机化研究,(1)
张涌钢.2001.汽车发动机性能检测与故障诊断专家系统的研究[D].浙江大学硕士学位论文
张堪,侯世国,所玉君等.1999.Access2000中文版入门与提高[M].北京:清华大学出版社
张选平,高晖,赵仲孟.2002.数据库型知识的产生式表示[J].计算机工程与应用,(1)
张优良.2002.农业专家系统及发展方向探讨[J].青海农技推广,(3):17,28
张玉宝.2003.基于网络的农业机械化管理信息系统的研究与开发[D].西北农林科技大学硕士研究生论文
郑国明.1996.县级农机化管理数据库管理系统的建立及应用[J].中国农机化,(3):32-33
郑珂晖.2004.基于C++builder的农机设备维护信息系统[J].农业网络信息,(12): 10-11
郑丽敏.2004.人工智能与专家系统原理及其应用[M].北京:中国农业大学出版社
郑丽敏,任丽梅,任发政.2002.农业专家系统的发展与精确农业[J].中国农业科技导报,(4):62-65
郑文钟.2005.基于数据挖掘和系统集成的农业机械化信息管理系统研究[D].浙江大学博士学位论文
郑文钟,何勇,张玉林.2005.基于GIS的农业机械化决策支持系统的研究[J].浙江大学学报(农业与生命科学版),(3):329-332
周桂霞,张吉军.2005.小型拖拉机常故障智能专家系统的应用[J].中国农机化(2):57-59
周中林.2006.全国农机监理信息系统建设初探[J].农机化研究,(11):46-49
朱凤林,刘景春,邱煜辉等.1998.农业专家系统的应用与发展[J].厦门科技,(5):35
祝荣欣.2006.基于Web的农业机器选型智能决策支持系统的研究[D].东北农业大学硕士学位论文
祝荣欣,乔金友,王福林.2007.农业机械化专家系统研究现状与问题[J].东北农业大学学报,(6)
A.Isik;S.M.Say.2001.An Expert System for Tractor Selection According to Farm Conditions[C].proceedings ⅩⅪⅩ ciosta-cigr V congress
C P Yialouris,A B Sideridis.1996.An expe～system for tomato diseases[J].Computer and Electronic in Agriculture,(14):61-76
D.R.Hunt.1983.Farm Power and Machinery Management[M].Press of Iowa University
Feng Lei,He Yong.1999.Study on Fault Diagnosis expert system of Engines of farm Tractor Based on Artificial Neural Networks[C].International Conference on Agricultural Engineering (99-ICAE)
Freeman,S.A.Ayers,P.D.1989.Expert System for Tractor Selection[J].Applied Engineering in Agriculture,5(2):123-126
Gao Huanwen,Hunt D.R.1985.Optimum Combine Fleet Selection With Power-based Model[J].Transactions of ASAE,28(2):364-368
He Ruiyin,Liu Deying.2001.A Model for Selecting Farm Machinery Based on Crop Production[C].Promoting Global Innovation of Agricultural Science & Sustainable Agricultural Development International Conference on Agricultural Science and Technology
Hugher,J.B.Holtman.1976.Machinery Complement Selection Based on Time Constraints[J].Transaction of ASAE,(5):812-814.
Gajendra Singh,B.K.Pathak.1994.A Decision Support System for Mechanical Harvesting and transportation of Sugarcane in Tailand[J].Computers and Electronics in Agriculture,(11) 173-182.
Joseph C.Giarratano,Gary D.Dirley.2005.Expert System:Principle and Programming[M].机械工业出版社
Kishor M Butani,Gajendra Singh.1994.Decision Support System for Selection of Agricultural Machinery with a Case Study in India [J]. Computer and Electronics in Agriculture, (10)91-104
Kline D. E, Bender D. A, Van Donge C. E, et al. 1988. Machinery Selection Using Expert Systems and Linear Programming[J]. Computers and Electronics in Agriculture, 3(1): 45—61
Kotzabassis, Constantinos. 1991. An Integrated Farm Machinery Selection and Management System for Personal Computers [J]. Dissertation Abstracts International . vol.53-01B.
L.H.Chen,R.W.McClendon. 1984. Selection of Planting Schedule for Soybeans via Simulation [J]. Transactions of ASAE, (1):29-32
Massimo Lazzari, Fabrizio Mazzetto. 1996. A PC model for selection multicroping farm machinery system [J]. Computers and Electronics in Agriculture, (14): 43-59
Nanseki. 1998. A Decision Support system for Agricultural Technology and Farm Planning [J]. Tohoku-Nogyo-shikenjo-Shiryo, 21
Singh, Davindar, J.B.Holtman. 1979. Aheuristic Agricultural Field Machinery Selection Algorithm for Multi-crop Farms [J]. Transactions of ASAE, (4);763-770
William Edwards, Michael Boehlje. 1980. Machinery Selection Considering Timeliness Losses [J]. Transaction of ASAE, :810-832
鲍一丹,何勇.2001.农业机械多媒体决策支持系统的研究[J].浙江大学学报(农业与生命科学版).(2):187-190
蔡自兴,徐光佑.2003.人工智能及其应用[M].北京:清华大学出版社
蔡自兴,约翰·德尔金[美],龚涛.2005.高级专家系统:原理、设计及应用[M].北京:科学出版社
陈海涛,刘宏新,乔金友等.2000.J.D.-4450拖拉机技术经济性指标随机龄的变化规律[J].农机化研究,(2):118-121
陈济勤.2001.农业机器运用管理学[M].北京:中国农业出版社
陈清云,李鸿.2001.黄瓜温室栽培管理专家系统研究[J].农业工程学报,(6):142-146
陈立辉.2003.农机作业委托决策支持系统(CHSDSS)设计与开发[D].吉林大学硕士学位论文
陈雁青,王立才,刘强.1987.微型计算机在农机信息管理上的应用[J].农机化研究,(2)
程耀.1984.友谊农场五分场一队田间作业机器选型配备优化设计[D].东北农学院博士学位论文
迟媛.2002.面向用户的农机液压系统故障诊断专家系统的设计与实现[D].东北农业大学硕士学位论文
杜璟.2003.基于Web的农业机械化发展决策支持系统[D].中国农业大学硕士学位论文
段韶芬,李福超,郑国清.2003.农业专家系统研究进展及展望[J].农业图书情报学刊,(5):15-18.
段韶芬,郑国清.2003.农业专家系统研究之管见[J].计算机与农业,(8):3-5
高洪伟.2004.稻麦收获机械选型及其决策支持系统的研究[D].南京农业大学硕士学位论文
高焕文.1998.高等农业机械化管理学[M].北京:中国农业大学出版社
高济,朱淼良.2002.人工智能基础[M].北京:高等教育出版社
高明亮,王雪珍,吴顺章.2001.农业专家系统存在的问题与对策[J].洛阳农业高等专科学校学报,(2):88-90
郭志达.2004.农机液压故障模糊诊断专家系统的研究[D].东北农业大学硕士学位论文
韩聚奎,张敏.1999.农业机械液压系统故障诊断专家系统的研究[J].农业机械学报,(3):67-70
韩宽襟,万鹤群,高焕文.1990.我国农业机器配备的特点及典型模型研究[J].北京农业工程大学学报,(4):1-6
韩宽襟,高焕文,万鹤群.1991.微机用农业机器作业系统优化大型软件系统FMS的研制[J].北京农业工程大学学报,(1)
何勇,鲍一丹.2001.基于神经网络的发动机故障多媒体诊断专家系统[J].内燃机学报,(3):245-248
何勇,鲍一丹,洪晓莉等.1995.农业机械化数据库管理系统研究[J].科技通报,(3):147-151
黑龙江八一农垦大学.1983.农业机器运用学[M].北京:中国农业出版社
洪国彬.1995.专家系统中的知识获取问题[J].华侨大学学报(自然科学版),16(4):451-454
胡明晖.2005.基于知识源的专家系统知识获取技术[J].成组技术与生产现代,(2):25-28
胡文斌.2001.现代农业企业机器选型配套系统的开发与研究[D].华中农业大学硕士学位论文
黄贵平,杨林,任明见等.2003.专家系统及其在农业上的应用[J].种子,(1):54-57
黄梯云,李一军.2000.管理信息系统[M].北京:高等教育出版社
黄志勇,杜会清,黄魏等.2003.网络化复合肥优化配料专家系统的开发[J].计算机与农业,(3):10-12
金炯官,金锡禧.1984.用微型电子计算机合理选配农业机械的基本方法[J].延边大学农学学报,(1)
金秀满.1989.田间作业机组耗油指标的模拟与分析[J].农机化研究,(4)
金秀满,张吉营.1990.铁牛-55拖拉机运输作业耗油量计算机模拟方法的试验研究[J].农机化研究,(4)
李道亮,傅泽田,马莉等.2000.智能化水产养殖信息系统与初步实现[J].农业工程学报,(4):135-138
李道亮,傅泽田,田东.2004.智能系统:基础、方法及其在农业中的应用[M].北京:清华大学出版社
李军政.2004.洞庭湖区农田动力机器系统的优化配备研究[D].湖南农业大学硕士学位论文
李晓勤.2004.固定资产折旧方法及模型研究[D].东北农业大学硕士学位论文
李小燕,雷勇辉,张建华等.2004.新疆棉花病虫害管理专家系统[J].中国棉花(5):32.
李骅.2003.基于Web数据库的农机化统计管理信息设计[D].南京农业大学硕士学位论文
李骅,陈红星,陈鹏等.2004.农机管理信息系统的现状与展望[J].农机化研究,(5):56-58.
李增芳.2004.基于人工智能和虚拟仪器技术的发动机故障诊断专家系统研究[D].浙江大学博士学位论文
李中才.2005.农业机器系统状态仿真及更新决策的研究[D].东北农业大学博士学位论文
刘继芬,聂凤,英王平等.2004.世界主要国家农业专家系统、计算机决策系统和计算 机模拟模型技术的应用现状[J].农业网络信息,(5):42-43
刘健.1994.农业机械维修制度辅助决策专家系统[J].河南农业大学学报,(1):72-78
刘晓燕.1997.回顾与展望.专家系统在我国农业上应用情况概述[J].计算机与农业,(1):1-3
刘兆友,何方,夏敏.2001.耕地适宜性评价专家系统的建立与应用[J].安徽农业大学学报,28(3):326-330
刘滨,位绍文.2003.我国农业专家系统研究应用与展望[J].莱阳农学院学报,(3):229-231
刘炳文,许蔓舒.2000.Visual Basic程序设计教程[M].北京:清华大学出版社
刘文涛.2006.Visual BASIC+ACCESS数据库开发与实例[M].北京:清华大学出版社
马文杰,贺立源.2005.专家系统在我国农业中的应用进展.山地农业生物学报,(4)343-346
闵联营.1999.知识获取技术探析[J].交通与计算机,(10):32-35
潘大丰,程季珍,李群等.2000.蔬菜施肥专家系统[J].华北农学报,(3):118-121
潘文聪,郭志民,陈永宝.2002.水土保持规划专家系统初探[J].福建水土保持,(4):29-31
裴新国,丛群滋.2003.Web网站上果树专家系统开发与应用[J].山东农业科学,(3):45-48
齐新丹.2000.农机统计管理计算机软件的开发[J].南京化工大学学报(自然科学版),(6)
钱宇华.1997.论专家系统中的知识获取问题[J].北京第二外国语学院学报,(3):122-126
乔金友,王福林,丛昕等.2007.水田耕地机组技术经济性能评价方法与应用[J].东北农业大学学报,(1):78-81
秦来寿.2005.农业专家系统发展概况及趋势分析[J].世界农业,(12):45-48
邱建军,肖荧南,戴逸民等.2002.基于模拟模型的棉花生产管理系统研究[J].农业工程学报,(6):161-164
邱立春.1991.农机化管理信息系统数据库设计[J].农机化研究,(2)
求是科技.2004.Visual BASIC 6.0程序设计与开发技术大全[M].北京:人民邮电出版社
上官周平.1994.农业专家系统及其应用[J].农业现代化研究,(5):298-301
宋兆华,杨宇姝,周大帅.2002.垦区智能化农业专家系统的应用与探讨[J].现代化农业,(1):33-35
孙赴.1988.概述计算机在农业上的应用[J].黑龙江省农业科学,(5)
孙旭武,李唯,闫丽娟.2002.网络农业专家系统的设计与开发[J].计算机与农业,(6):6-7
孙永厚,张志彦,刘志鸿.1999.农机液压系统故障诊断专家系统知识获取的研究[J].农机化研究,(2):39-42
孙永厚,刘志鸿,郝德刚等.1999.农机液压系统故障诊断专家系统中推理控制策略的研究[J].农机化研究,(4):27-30
孙曰波,赵波秀,李寿冰.2006.农业专家系统发展的概况与前景[J].潍坊高等职业教育,(2):39-41
唐晓敏,徐立鸿,恽源世.2005.基于实例推理及其在农业病虫害诊断与防治中的应用研究[J].中国农机化,(1):56-59
谭文学.2003.基于启发式规则推理的多媒体农业专家系统开平台的开发与研究[J].计算机与农业,(7)
韦于莉.2004.知识获取研究[J].情报杂志,(4):41-43
王福林.1988.黑龙江省国营农场麦豆收获机械化系统分析[D].东北农学院硕士学位论文
王福林.1993.农业机器经济寿命与更新换代经济效果及其评价问题的研究[D].东北农业大学博士学位论文
王立舒,戚国强,杨宇姝.2003.产生式系统在农业专家系统中的应用[J].农机化研究,(1):132-134
王茂辉,郑彦波,娄琳.2005.农机液压系统故障诊断专家系统的设计[J].黑龙江八一农垦大学学报,(1):43-46
王庆成,刘开昌,张秀清等.2002.日光温室黄瓜栽培管理专家系统的开发[J].山东农业科学,(4):14-16
王文娥,汪志农,马孝义.2000.农业专家系统及其在灌溉管理中的应用[J].计算机与农业,(8):5-7
王小军,周昌盛,吕汉兴.2005.一种基于关系数据库的知识表示和推理方法[J].控制工程,(1):40-43
王永庆,上官周平.1995.专家系统在农业生产管理中的应用[J].微机发展,(1):3-6
王子跃,王丽娟,李照美等.1999.陶志祥汽车发动机故障诊断专家系统[J].河南农业大学学报,(3):293-295
吴玺,谭宏,夏建国等.2000.试论我国农业专家系统的应用与发展[J].计算机与农业,(8):1-4
武波,马玉祥.2001.专家系统[M].北京:北京理工大学出版社
武华,孙永厚,刘贤喜等.1999.农机液压系统故障诊断专家系统的研究[J].农业工程学报,(1):183-187
项利国.2004.基于网络的农业机械装备管理信息系统的研究与实现[D].浙江大学硕士学位论文
辛惠娟,戴庆辉.2005.汽车发动机故障诊断专家系统研究[J].煤矿机械,2005(2)
辛惠娟,钱东平,李志芳.2006.基于ASP网络开发汽车发动机故障诊断专家系统[J].农机化研究,(2)
熊范纶.1999.农业专家系统及开发工具[M].北京:清华大学出版社
徐安东,李飞,邢晓怡.2006.Visual BASIC数据库应用开发教程[M].北京:清华大 学出版社
晏国生.1998.计算机在农机化管理中的应用[M].北京:清华大学出版社
燕学智,钱耀义.2001.基于人工神经网络技术的发动机故障诊断系统[J].内燃机工程,(1):78-81
杨保祝,赵春江,孙想等.2002.节水灌溉专家决策系统的研究与应用[J].节水灌溉,(2):17-19
杨国强,王双喜,杜伟.2004.我国农业专家系统的研究进展[J].山西农业大学学报,(3):303-305
杨宇姝,宋兆华,丁华福等.2002.农业专家系统知识获取组织的研究与应用[J].农业系统科学与综合研究,(2):152-154
姚彤.2000.农机销售管理信息系统的设计与开发[D].东北农业大学硕士学位论文
叶继昌,王登峰,杨印生等.1995.农业机械性能评价及选型专家系统的设计[J].农业机械学报,(1):11-14
尹朝庆,尹皓.2001.人工智能与专家系统[M].北京:中国水利水电出版社
余心杰.2004.基于Web GIS的农机化信息管理系统研究[D].浙江大学硕士学位论文
袁春新,郭建华,魏亚凤等.2003.农业专家系统及其在水稻栽培中的应用[J].西南农业学报,(4):130-133
袁黎.2003.农机监理信息系统的研制与开发[D].河南农业大学硕士学位论文
袁泉,胡晓卫,何勇.1998.神经网络技术用于发动机故障诊断的研究[J].农业机械学报,(3):104-107
曾路,苗新明,张兴亚等.2004.农业信息技术应用现状及发展趋势[J].信息技术应用,(2):42-44
张海云.2003.农牧业机械多媒体管理信息系统研究[J].河北农业大学学报,(2):97-100
张清华,陈佰军.1994.机务工作计划计算机管理系统研究[J].黑龙江八一农垦大学学报,(4):68-73
张清华,郭仁华,蒋涛.1995.联合收获机计算机辅助选型配备[J].农机化研究,(1)
张涌钢.2001.汽车发动机性能检测与故障诊断专家系统的研究[D].浙江大学硕士学位论文
张堪,侯世国,所玉君等.1999.Access2000中文版入门与提高[M].北京:清华大学出版社
张选平,高晖,赵仲孟.2002.数据库型知识的产生式表示[J].计算机工程与应用,(1)
张优良.2002.农业专家系统及发展方向探讨[J].青海农技推广,(3):17,28
张玉宝.2003.基于网络的农业机械化管理信息系统的研究与开发[D].西北农林科技大学硕士研究生论文
郑国明.1996.县级农机化管理数据库管理系统的建立及应用[J].中国农机化,(3):32-33
郑珂晖.2004.基于C++builder的农机设备维护信息系统[J].农业网络信息,(12): 10-11
郑丽敏.2004.人工智能与专家系统原理及其应用[M].北京:中国农业大学出版社
郑丽敏,任丽梅,任发政.2002.农业专家系统的发展与精确农业[J].中国农业科技导报,(4):62-65
郑文钟.2005.基于数据挖掘和系统集成的农业机械化信息管理系统研究[D].浙江大学博士学位论文
郑文钟,何勇,张玉林.2005.基于GIS的农业机械化决策支持系统的研究[J].浙江大学学报(农业与生命科学版),(3):329-332
周桂霞,张吉军.2005.小型拖拉机常故障智能专家系统的应用[J].中国农机化(2):57-59
周中林.2006.全国农机监理信息系统建设初探[J].农机化研究,(11):46-49
朱凤林,刘景春,邱煜辉等.1998.农业专家系统的应用与发展[J].厦门科技,(5):35
祝荣欣.2006.基于Web的农业机器选型智能决策支持系统的研究[D].东北农业大学硕士学位论文
祝荣欣,乔金友,王福林.2007.农业机械化专家系统研究现状与问题[J].东北农业大学学报,(6)
A.Isik;S.M.Say.2001.An Expert System for Tractor Selection According to Farm Conditions[C].proceedings ⅩⅪⅩ ciosta-cigr V congress
C P Yialouris,A B Sideridis.1996.An expe～system for tomato diseases[J].Computer and Electronic in Agriculture,(14):61-76
D.R.Hunt.1983.Farm Power and Machinery Management[M].Press of Iowa University
Feng Lei,He Yong.1999.Study on Fault Diagnosis expert system of Engines of farm Tractor Based on Artificial Neural Networks[C].International Conference on Agricultural Engineering (99-ICAE)
Freeman,S.A.Ayers,P.D.1989.Expert System for Tractor Selection[J].Applied Engineering in Agriculture,5(2):123-126
Gao Huanwen,Hunt D.R.1985.Optimum Combine Fleet Selection With Power-based Model[J].Transactions of ASAE,28(2):364-368
He Ruiyin,Liu Deying.2001.A Model for Selecting Farm Machinery Based on Crop Production[C].Promoting Global Innovation of Agricultural Science & Sustainable Agricultural Development International Conference on Agricultural Science and Technology
Hugher,J.B.Holtman.1976.Machinery Complement Selection Based on Time Constraints[J].Transaction of ASAE,(5):812-814.
Gajendra Singh,B.K.Pathak.1994.A Decision Support System for Mechanical Harvesting and transportation of Sugarcane in Tailand[J].Computers and Electronics in Agriculture,(11) 173-182.
Joseph C.Giarratano,Gary D.Dirley.2005.Expert System:Principle and Programming[M].机械工业出版社
Kishor M Butani,Gajendra Singh.1994.Decision Support System for Selection of Agricultural Machinery with a Case Study in India [J]. Computer and Electronics in Agriculture, (10)91-104
Kline D. E, Bender D. A, Van Donge C. E, et al. 1988. Machinery Selection Using Expert Systems and Linear Programming[J]. Computers and Electronics in Agriculture, 3(1): 45—61
Kotzabassis, Constantinos. 1991. An Integrated Farm Machinery Selection and Management System for Personal Computers [J]. Dissertation Abstracts International . vol.53-01B.
L.H.Chen,R.W.McClendon. 1984. Selection of Planting Schedule for Soybeans via Simulation [J]. Transactions of ASAE, (1):29-32
Massimo Lazzari, Fabrizio Mazzetto. 1996. A PC model for selection multicroping farm machinery system [J]. Computers and Electronics in Agriculture, (14): 43-59
Nanseki. 1998. A Decision Support system for Agricultural Technology and Farm Planning [J]. Tohoku-Nogyo-shikenjo-Shiryo, 21
Singh, Davindar, J.B.Holtman. 1979. Aheuristic Agricultural Field Machinery Selection Algorithm for Multi-crop Farms [J]. Transactions of ASAE, (4);763-770
William Edwards, Michael Boehlje. 1980. Machinery Selection Considering Timeliness Losses [J]. Transaction of ASAE, :810-832