计算智能在土壤数据融合中的应用研究
|
摘要
多传感器数据融合技术首先是从军事领域发展起来的,并且己经发展成为一个新的学科方向和研究领域。本文介绍了数据融合系统的基本理论、发展及研究现状,研究了数据融合中的不确定方法,结合在研的国家“863”重大项目——车载农田土壤信息快速采集关键技术与产品研发,构建土壤参数融合模型,在智能计算理论的基础上,对多传感器数据融合算法提出了新的思路,并进行了深入的研究。
对基于神经网络与信任度函数的多传感器数据融合算法进行了研究,构造信任度函数矩阵,采用该矩阵度量各传感器测得数据之间的综合信任程度,合理分配测得数据在融合过程中的权重,作为BP神经网络的输入,经过训练好的网络,取得良好的融合效果,以削弱白噪声对测量精度的影响。
提出了两种基于粒子群优化算法(PSO)的传感器融合方法。一种方法是对粒子群优化算法中的固定惯性权重进行改进,分析了惯性权值因子在粒子群优化算法中的作用,在现有线性递减权值方法上,提出一种非线性权值递减策略,并将其尝试性的运用到多传感器数据融合的领域,运用该算法对加权因子进行调整,得到良好的融合效果。另一种是基于量子空间的粒子群算法(QDPSO)和BP神经网络的多传感器融合算法,经过QDPSO训练的BP神经网络具有较好的稳定性和收敛性,将其运用于多传感器数据融合中,通过仿真取得了比常规算法更高的精度,是一种较有潜力的融合算法。
研究小波变换的特性,提出了基于小波包变换的多分辨率多传感器信息融合模型。利用小波变换的理论,研究小波在像素级、特征级融合中的新算法。根据已经建立好的土壤信息融合模型,结合测量得到的各种农田参数,将研究的融合算法运用于具体的数据处理中,对土壤含水率、电导率等参数进行实际分析,从海量传感器信息中,提取有价值的农作物生物物理和生物化学参数,以指导实地农业生产和管理,从而提高农作物的产量和质量,为进一步实现精准农业提供理论依据和实践参考。
Multi-sensor data fusion technology was developed firstly from the military area, and had been developed into a new subject direction and research field.In this paper, the definition and the model of data fusion were introduced together with its development and research status.Studied the uncertain methods of the data fusion.Combined with the research at the National“863”major projects—the key information and product development of express collection of soil information, Constructed the model of soil parameters.On the base of the theory of computational intelligence, New ideas were put forward based on multi-sensor data fusion algorithm and the thorough research had been done.
Do some research on data fusion model and algorithm combined with BP and belief function.Build belief function matrix and use it to measure the belief degree of sensor data.Distribute the weight of the fusion process reasonably which is the input of BP neural network.The fusion effect is good through the best trained network which weaks the white noise of measurement accuracy while at the same time a number of sensor observation value were compressed into an optimal fusioned data.
Two sensor fusion algorithms were given based on particle swarm optimization (PSO).One method was to improve the inertia weight of PSO where the role of the inertia weight factor in PSO was analysed. A nonlinear strategy for DIW was put forward to the field of the multi-sensor fusion which can estimate the weight factor of the data fusion algorithm and get closer to the ture value. The other method is to use multi-sensor data fusion algorithm based on QDPSO-BP network, throgh which trained BP neural network and got good stability and convergence.Higher accuracy would be gotten by using it in the simulation.It is a potential data fusion method for multi-sensors.
Research the character of wavelet transform.The multi-resolution and multi-sensor data fusion model was proposed based on wavelet packet.Research the new multi-sensor data fusion algorithm in the pixel level, feature level by using the wavelet theory.Apply the data fusion algorithm to specific data processing combined the measurement parameters of the various fields. Analysis the soil moisture content, conductivity and other parameters actually. Extract the biochemistry and biophysics parameters from valuable crops and guide the agricultural production and management so as to enhance crop yield and quality.Provide a theoretical base on precision agriculture for the further implementation.
对基于神经网络与信任度函数的多传感器数据融合算法进行了研究,构造信任度函数矩阵,采用该矩阵度量各传感器测得数据之间的综合信任程度,合理分配测得数据在融合过程中的权重,作为BP神经网络的输入,经过训练好的网络,取得良好的融合效果,以削弱白噪声对测量精度的影响。
提出了两种基于粒子群优化算法(PSO)的传感器融合方法。一种方法是对粒子群优化算法中的固定惯性权重进行改进,分析了惯性权值因子在粒子群优化算法中的作用,在现有线性递减权值方法上,提出一种非线性权值递减策略,并将其尝试性的运用到多传感器数据融合的领域,运用该算法对加权因子进行调整,得到良好的融合效果。另一种是基于量子空间的粒子群算法(QDPSO)和BP神经网络的多传感器融合算法,经过QDPSO训练的BP神经网络具有较好的稳定性和收敛性,将其运用于多传感器数据融合中,通过仿真取得了比常规算法更高的精度,是一种较有潜力的融合算法。
研究小波变换的特性,提出了基于小波包变换的多分辨率多传感器信息融合模型。利用小波变换的理论,研究小波在像素级、特征级融合中的新算法。根据已经建立好的土壤信息融合模型,结合测量得到的各种农田参数,将研究的融合算法运用于具体的数据处理中,对土壤含水率、电导率等参数进行实际分析,从海量传感器信息中,提取有价值的农作物生物物理和生物化学参数,以指导实地农业生产和管理,从而提高农作物的产量和质量,为进一步实现精准农业提供理论依据和实践参考。
Multi-sensor data fusion technology was developed firstly from the military area, and had been developed into a new subject direction and research field.In this paper, the definition and the model of data fusion were introduced together with its development and research status.Studied the uncertain methods of the data fusion.Combined with the research at the National“863”major projects—the key information and product development of express collection of soil information, Constructed the model of soil parameters.On the base of the theory of computational intelligence, New ideas were put forward based on multi-sensor data fusion algorithm and the thorough research had been done.
Do some research on data fusion model and algorithm combined with BP and belief function.Build belief function matrix and use it to measure the belief degree of sensor data.Distribute the weight of the fusion process reasonably which is the input of BP neural network.The fusion effect is good through the best trained network which weaks the white noise of measurement accuracy while at the same time a number of sensor observation value were compressed into an optimal fusioned data.
Two sensor fusion algorithms were given based on particle swarm optimization (PSO).One method was to improve the inertia weight of PSO where the role of the inertia weight factor in PSO was analysed. A nonlinear strategy for DIW was put forward to the field of the multi-sensor fusion which can estimate the weight factor of the data fusion algorithm and get closer to the ture value. The other method is to use multi-sensor data fusion algorithm based on QDPSO-BP network, throgh which trained BP neural network and got good stability and convergence.Higher accuracy would be gotten by using it in the simulation.It is a potential data fusion method for multi-sensors.
Research the character of wavelet transform.The multi-resolution and multi-sensor data fusion model was proposed based on wavelet packet.Research the new multi-sensor data fusion algorithm in the pixel level, feature level by using the wavelet theory.Apply the data fusion algorithm to specific data processing combined the measurement parameters of the various fields. Analysis the soil moisture content, conductivity and other parameters actually. Extract the biochemistry and biophysics parameters from valuable crops and guide the agricultural production and management so as to enhance crop yield and quality.Provide a theoretical base on precision agriculture for the further implementation.
引文
1. Hall D.L, Llinas J.Handbook of multisensor data fusion [M].New York: CRC Press, 2001.
2. Hall D.L, Llinas J.An introduction to multisensor data fusion [J]. Proceedings of the IEEE, 1997, 85(1): 6-23.
3. Luo RC, Kay MG.Multisensor Integration and Fusion in Intelligent Systems [J].IEEE Trans. Syst., Man Cybern, 1989, 19(5): 901-931.
4. White F. A model for data fusion [A]. Proc. 1. St. National Symposium on Sensor Fusion, 1988.
5. Blackman S. Multiple-target tracking with RADAR application [M].Norwood: Artech House, 1986.
6. Center of Excellence in Command, Control, Communications, Computing and Intelligence, C4I Center. http://c4i.gmu.edu/index.php.
7.刘同明,夏祖勋,解洪成编著.数据融合技术及其应用[M].北京:国防工业出版社,1998,第1版.
8.杨万海编著.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2004,第1版.
9.康耀红著.数据融合理论与应用[M].西安:西安电子科技大学出版社,1997,第1版.
10.戴亚平,刘征,郁光辉译著.多传感器数据融合理论及应用[M].北京:北京理工大学出版社,2004,第1版.
11.徐科军编著.传感器与检测技术[M].北京:电子工业出版社,2004.
12.杨国胜,窦丽华编著.数据融合及其应用[M].北京:兵器工业出版社,2004.
13.罗志增,蒋静坪.机器人感觉与多信息融合[M].北京:机械工业出版社,2002,第1版.
14.张明社,李小昱,雷廷武等.用基于神经网络的数据融合法测量水流泥沙含量[J].农业工程学报,2002,18(4):41-43.
15.杨露菁,郝斌.多传感器目标识别的神经网络与证据理论结合方法[J].探测与控制学报,2006,28(1):40-43.
16.倪国强,李勇量,牛丽红等.基于神经网络的数据融合的新进展[J].北京理工大学学报,2003,23(4):503-508.
17.李芳社,王宝树.神经网络技术在数据融合中的应用[J].西安电子科技大学学报,1998,25(6):790-793.
18.王俊林,张剑云.采用Bayes多传感器数据融合方法进行目标识别[J].传感器技术,2005,24(10):86-88.
19.胡振涛,楚艳萍,刘先省.测量方差自适应的多传感器数据融合算法[J].红外与激光工程,2005,34(6):741-746.
20.吴小俊,曹奇英,陈保香,刘同明.基于Bayes估计的多传感器数据融合方法研究[J].系统工程理论与实践,2007,7:45-48.
21.钱永兰,杨邦杰,雷廷武.数据融合及其在农情遥感监测中的应用与展望[J].农业工程学报,2004,20(4):286-290.
22.滕召胜,宁乐炜,张海霞等.粮食干燥机水分在线检测系统研究[J].农业工程学报,2004,20(5):130-132.
23.夏胜平,滕召胜,郁文贤.智能粮食收购系统的设计及其水分检测数据融合[J].农业现代化研究,2000,21(1):24-27.
24.陈子丹,李琳,严慕绥.基于GIS多因子分析及其信息融合技术的土壤侵蚀分级制图研究[J].遥感信息,1999,(2):16-19.
25.乔玉良.信息融合技术的山西土壤侵蚀监测与动态分析[J].地球信息科学,2000,(2):33-36.
26.汪懋华.“精细农业”发展与工程技术创新[J].农业工程学报,1999,15(1):1-8.
27.曲晓慧,安钢.数据融合方法综述及展望[J].船舶电子工程,2003,134(3):2-4.
28. Waltz E.,Llinas J. Multisensor data fusion [M]. London: Artech House, Inc., 1990.
29.徐从富,耿卫东,潘云鹤.面向数据融合的D-S算法综述[J] .电子学报,vol.29(3),2001,393-396.
30. Hall D. L. Mathematical techniques in multisensor data fusion [M]. London: Artech House, Inc, 1992.
31.王欣.多传感器数据融合问题的研究[D].吉林大学,2006.
32. Durrant-Whyte H. F. Consistent Integration and Propagation of Disparate Sensor Observations [J]. International Journal of Robotics Research, 1987,vol. 6(3): 3-24
33. Dempster A P. Upper and lower probabilities induced by a multivalued mapping [J]. Annals of Mathematical Statistics. 1967, 38(2):325-339.
34. Shafer G.A mathematical theory of evidence [M]. Princeton, New Jersey: Princeton University Press, 1976.
35. Pawlak Z. Rough sets. International Journal of Computer Information Sciences, 1982, 11:341-359.
36. A.Grossman and J.Morlet. Decomposition of hardy function into square integrable wavelets of constant shape. SIAM J.Math. Anal, 1984, 15(4):723-736.
37.张立明.人工神经网络的模型及其应用[M].复旦大学出版社,1992:1-12.
38. R. C. Eberhart, R. W. Dobbins. Neural Network PC Tools. Academic Press.1990.
39. B. J. Werbos. Backpropagation Troth Time: What It Does and How to Do It. Proc. IE EE.1990:78 (10):1550 -1560.
40. D .R uck, S .Roges. The Multilayer Perceptron as an Approximation to a Bayes Optimal Discriminant Function.IEEE Trans.Neural Networks. 1990, 2:296- 298.
41. F .K anaya,S .M iyaker.Bayes Statistical Behavior and Valid Generalization of Patern C lassifying Neural Networks.IEEE Trans.Neural Networks.1992,3 :471-475.
42. E .Barnard.Comments on Bayes Statistical Behavior and Valid Generalization of Patern Classifying Neural Networks.IEEE Trans.Neural Networks.1993, 4:1026-1027.
43. S. C. Thomopoulos, I. N. Papadakis, H. Sahinoglou, and N. N. Okello.Centralized and Distributed Hypothesis Testing with Structured Adaptive Networks and Perception-Type Neural Networks. Proc. SPIE, 1991, 1611:35-51.
44. K.Halford, D.Kazakkos, D.PadosandP, and Papantoni-Kazakkos.NeuralNetwork with Feed-back Binary Hypothes is Testing.Proc.IEEE Trans.system, cybem, oct.1991:1479-1486.
45. P. Dimitris, K.W. Halford, K. Dimitri and P.P-Kazakos. Distributed Binary Hypothesis Testing with Feedback Neural Network. IEEE Trans. system, cybem, 1995, 25 (1):21-42.
46. A. P. Dimitris. New Nonleast-squares Neural Network Learning Algorithm for Hypothes is Testing.IEEE Trans.Neural Networks.1995, 36 (3):596-609.
47. S.J. Ho, J. J. Hoon.Adaptive Image Sequence Resolution Enhancement Using Multisca le Decomposition Based Image Fusion. Proc. SPIE. 2000, 40 67:1589-1600.
48. J.S .T aur, S .Y .Kung.Fuzzy Decision Neural Network and A pplication to Data Fusion. Information science, 1993, 71:171~180.
49.闻新,宋屹,周露.模糊系统和神经网络的融合技术[J].系统工程与电子技术,vol.21(5), 1999,55-58.
50.张燕军等.基于神经网络与模糊融合的外围故障诊断方法[J].计算机测量与控制,Vol.16(5),2008,604-606.
51.沈景悦,吴秀清,刘严岩.一种基于模糊融合的遥感图像目标识别方法[J].计算机工程与应用,2005,16:83-85.
52. T.Kohonen.Self-Organized Formation of Feature Maps.Cybern. Syst.Recognit.Learn, 19 84:3-12.
53. T. Kohonen. The Set Organizing Map. Proceeding of the IEEE. 1990, 78(1):1464-1480
54. T.Kohonen.Fast Evolutionary Learning with Batch-type Self-Organizing Maps.Neural Pr ocessing Letters.1999, 9(2):153-162.
55. Pongsak Ajjimarange.Neural Network Model for Fusion of Visible and Infrared Sensor O utputs.SPIE.1988, 1003:153-160.
56. A. Chiuderi, S. Fini and V. Cappellini. An Application of Data Fusion to Landcover C lassification of Remote Sensed Imagery: A Neural Network Approach.Proc.Int.Conf.MFI.1994:756-762.
57. S. C. A. Thomopoulos, D. K. Bougoulias. DIGNET: A Self-Organizing Neural Network for Automatic Patern Recognition, Classification and Data Fusion.SPIE .1991, 1611:478-495.
58. C. Wann, S. C. A. Thomopoulos. Unsupervised Learning Neural Network with Application to Data Fusion.Proc.A merica Control Conf.1994:1361-1365.
59. S.C .A .T homopoulos, C .Wann.Clustering with Unsupervised Learning Neural Network with Application to Data Fusion.SPIE.1994, 2093:621-632.
60.吴今培,孙德山.现代数据分析[M].北京:机械工业出版社,2006.
61.焦竹青,熊伟丽,张林等.基于信任度函数的多传感器数据融合及其应用[J].东南大学学报,2008,38(S1):253-257.
62.胡振涛,刘先省.基于相对距离的一种多传感器数据融合方法[J].系统工程与电子技术, 2006, 28(2):196-198.
63.邢文训,谢金星.现代优化计算方法[M].北京:清华大学出版社,1999.
64.杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,6(5):87-94,
65.柯晶,钱积新,乔谊正.一种改进粒子群优化算法[J].电路与系统学报,2003,8(5):87-91.
66. Kannan, S. Slochanal, S.Mary Raja. Application of particle swarm optimization technique and its variants to generation expansion planning problem [J]. Electric Power Systems Research, Volume: 70, Issue: 3, August, 2004, pp.203-210.
67. Sugisaka, Masanori; Fan, Xinjian. An effective search method for neural network based face detection using particle swarm optimization [J]. IEICE Transactions on Information and Systems vE88n 22005,pp.214-222.
68. Ghoshal, S.P. Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control [J]. Electric Power Systems Research, Volume: 72, Issue: 3, December 15, 2004, pp.203-212.
69. Abido, M.A. Optimal power flow using particle swarm optimization [J]. International Journal of Electrical Power and Energy Systems, Volume: 24, Issue: 7, October, 2002,pp.563-571.
70. Chen, Yifan; Dubey, Vimal K. Ultrawideband source localization using a particle-swarm-optimized capon estimator [J]. 2005 IEEE International Conference on Communications, ICC 2005, pp.173-179.
71. Shi Y,Eberhart R. Fuzzy Adaptive Particle Swarm Optimization[C].In:Proc Congress on Evolutionary Computation,Seoul,Korea,2001.
72. Angeline P J. Evolutionary Optimization versus Particle Swarm Optimization:Philosophy and Performance Differences[C]. In:Evolutionary Programming VII,1998:601-610.
73. Lovbjerg M, Rasmussen T K, Krink T. Hybird Particle Swarm Optimiser with Breeding and Subpopulations[C].In: Proc of the third Genetic and Evolutionary Computation Conference, 2001.
74. Van den Bergh F, Engelbrecht a P. Training Product Uint Networks Using Cooperative Particle Swarm Optimizations[C].In: Proc of the third Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, 2001.
75. Van den Bergh F, Engelbrecht a P. Effects of Swarm Size on Cooperative Particle Swarm Optimizers[C].In: Proc of the third Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, 2001.
76. Kennedy J, Eberhart R. A Discrete Binary Version of the Particle Swarm Algorithm[C]. IEEE Int Conf on Computational Cybernetics and Simulation, 1997:4104-4108.
77. Clerc M. Discrete Particle Swarm Optimization Illustrated by the Traveling Salesman Problem[C]. Protein Expression & purification 2000(14), pp.246-251.
78. Van den Bergh F, Engelbrecht A P. A new locally convergent particle swarm optimizer [A]. Proceedings of IEEE Conference on Systems,Man,and Cybernetics[C].Hammamet,Tunisia,2002:96-101.
79. Shi Y H, etal.Experimental Study of Particle Swarm Optimization[R].Proceedings of SCI Conference, Orlando, FL, 2000.
80. Eberhart R C,Shi Y H. Evolving Artificial Neural Networks[R].Proceedings of Int Conference on Neural Networks and Brain,Beijing,1998(a).
81.俞欢军,张丽平,陈德钊,宋晓峰,胡上序.复合粒子群优化算法在模型参数估计中的应用[J].高校化学工程学报,2005(05):284-287.
82. Claudia O.Ourique, Evaristo C. Biscaia. The use of particle swarm optimization for dynamical analysis in chemical processes [J]. Computers and Chemical Engineering 26 (2002) 1783-1793.
83. Ayed Salman, Imtiaz Ahmad, Sabah Al-Madani. Particle swarm optimization for taskassignment problem [J]. Microprocessors and Microsystems 26 (2002) 363-371.
84.王俊伟,汪定伟.一种带有梯度加速的粒子群算法[J].控制与决策,2004,(11):632-637.
85.刘益剑,张建明.微粒群算法在自动控制系统设计中的应用[J].高技术通讯,2005,(7):427-431.
86.王介生,王金城,王伟.基于粒子群算法的PID控制器参数自整定[J].控制与决策,2005,(01):54-58.
87.张宇林,蒋鼎国,黄翀鹏等.基于粒子群算法的多传感器数据融合[J].化工学报,2008,59(7):1703-1706.
88. Dujmovi?, J., Weighted conjunctive and disjunctive means and their application in system evaluation. Journal of the University of Belgrade, EE Dept., Series Mathematics and Physics.1974, v483. 147-158.
89. Harald Dyckhoff and Witold Pedrycz. Generalized means as model of compensative connectives [J]. Fuzzy Sets and Systems, 1984, 14(2), 143-154.
90. J.Sun etal. Particle swarm optimization with particles having quantum behavior. Proceedings of the congress on Evolutionary compution, 2004. pp: 325-331.
91. J.Sun etal. A global search strategy of Quantum behaved particle swarm optimization. Proceedings of the congress on Cybernetics and Intelligent systems.
92.熊伟丽,徐保国,孙俊等.改进QDPSO算法在BP网络训练中的应用[J].系统仿真学报,2005,17(9).
93.冯斌.群体智能优化算法和智能生化变量软测量模型的研究[J].江南大学博士论文,2005.
94.张宇林,蒋鼎国,朱小六等.基于QDPSO-BP网络的多传感器融合算法[J].传感器与微系统,2008,27(3):21:23.
95. Zhang Q H, Benveniste A Wavelet networks [J].IEEE Trans on NN, 1992, 3(6):889-898.
96. Cho S.Y.Chow TWS.A Neural learning based Reflectance Model for 3-D Shape Reconstruction.IEEE Transactions on Indusial Electronics, 2000, 47(6):1346-1349.
97. S.G.Mallat. Multiresolution approximation and wavelet orthogonal. American Math. Society, 1989, 315(1): 69-87.
98. S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machina Intelligence, 1989, 11(7): 674-693.
99. Yulin Zhang,Baoguo Xu,Xia zhu Wenbo Xu.Wavelet Analysis and Processing Algorithm for Images Compressing and Data Fusion.In:2008 International Symposium on DistributedComputing and Applications for Business Engineering and Science, Dalian, China, July, 2008:290-294
100.张宇林,朱小六,徐保国.基于WNN的BP算法的多传感器数据融合[J].传感器与微系统,2009.
101.Delyon B, Juditsky A, Benveniste A. Accuracy analysis for wavelet approximations [J].IEEE Trans on NN, 1995, 6(2):332-348.
102.裴浩东.基于神经网络的稳态优化和控制研究[D].杭州:浙江大学,2001.
103.吕伟杰.小波网络与多模型自适应控制理论的研究[D].天津:天津大学,2002.
104刘广明,杨劲松,鞠茂森,聂杰.电磁感应土地测量技术及其在农业领域的应用[J].土壤,2003(1):27-29.
2. Hall D.L, Llinas J.An introduction to multisensor data fusion [J]. Proceedings of the IEEE, 1997, 85(1): 6-23.
3. Luo RC, Kay MG.Multisensor Integration and Fusion in Intelligent Systems [J].IEEE Trans. Syst., Man Cybern, 1989, 19(5): 901-931.
4. White F. A model for data fusion [A]. Proc. 1. St. National Symposium on Sensor Fusion, 1988.
5. Blackman S. Multiple-target tracking with RADAR application [M].Norwood: Artech House, 1986.
6. Center of Excellence in Command, Control, Communications, Computing and Intelligence, C4I Center. http://c4i.gmu.edu/index.php.
7.刘同明,夏祖勋,解洪成编著.数据融合技术及其应用[M].北京:国防工业出版社,1998,第1版.
8.杨万海编著.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2004,第1版.
9.康耀红著.数据融合理论与应用[M].西安:西安电子科技大学出版社,1997,第1版.
10.戴亚平,刘征,郁光辉译著.多传感器数据融合理论及应用[M].北京:北京理工大学出版社,2004,第1版.
11.徐科军编著.传感器与检测技术[M].北京:电子工业出版社,2004.
12.杨国胜,窦丽华编著.数据融合及其应用[M].北京:兵器工业出版社,2004.
13.罗志增,蒋静坪.机器人感觉与多信息融合[M].北京:机械工业出版社,2002,第1版.
14.张明社,李小昱,雷廷武等.用基于神经网络的数据融合法测量水流泥沙含量[J].农业工程学报,2002,18(4):41-43.
15.杨露菁,郝斌.多传感器目标识别的神经网络与证据理论结合方法[J].探测与控制学报,2006,28(1):40-43.
16.倪国强,李勇量,牛丽红等.基于神经网络的数据融合的新进展[J].北京理工大学学报,2003,23(4):503-508.
17.李芳社,王宝树.神经网络技术在数据融合中的应用[J].西安电子科技大学学报,1998,25(6):790-793.
18.王俊林,张剑云.采用Bayes多传感器数据融合方法进行目标识别[J].传感器技术,2005,24(10):86-88.
19.胡振涛,楚艳萍,刘先省.测量方差自适应的多传感器数据融合算法[J].红外与激光工程,2005,34(6):741-746.
20.吴小俊,曹奇英,陈保香,刘同明.基于Bayes估计的多传感器数据融合方法研究[J].系统工程理论与实践,2007,7:45-48.
21.钱永兰,杨邦杰,雷廷武.数据融合及其在农情遥感监测中的应用与展望[J].农业工程学报,2004,20(4):286-290.
22.滕召胜,宁乐炜,张海霞等.粮食干燥机水分在线检测系统研究[J].农业工程学报,2004,20(5):130-132.
23.夏胜平,滕召胜,郁文贤.智能粮食收购系统的设计及其水分检测数据融合[J].农业现代化研究,2000,21(1):24-27.
24.陈子丹,李琳,严慕绥.基于GIS多因子分析及其信息融合技术的土壤侵蚀分级制图研究[J].遥感信息,1999,(2):16-19.
25.乔玉良.信息融合技术的山西土壤侵蚀监测与动态分析[J].地球信息科学,2000,(2):33-36.
26.汪懋华.“精细农业”发展与工程技术创新[J].农业工程学报,1999,15(1):1-8.
27.曲晓慧,安钢.数据融合方法综述及展望[J].船舶电子工程,2003,134(3):2-4.
28. Waltz E.,Llinas J. Multisensor data fusion [M]. London: Artech House, Inc., 1990.
29.徐从富,耿卫东,潘云鹤.面向数据融合的D-S算法综述[J] .电子学报,vol.29(3),2001,393-396.
30. Hall D. L. Mathematical techniques in multisensor data fusion [M]. London: Artech House, Inc, 1992.
31.王欣.多传感器数据融合问题的研究[D].吉林大学,2006.
32. Durrant-Whyte H. F. Consistent Integration and Propagation of Disparate Sensor Observations [J]. International Journal of Robotics Research, 1987,vol. 6(3): 3-24
33. Dempster A P. Upper and lower probabilities induced by a multivalued mapping [J]. Annals of Mathematical Statistics. 1967, 38(2):325-339.
34. Shafer G.A mathematical theory of evidence [M]. Princeton, New Jersey: Princeton University Press, 1976.
35. Pawlak Z. Rough sets. International Journal of Computer Information Sciences, 1982, 11:341-359.
36. A.Grossman and J.Morlet. Decomposition of hardy function into square integrable wavelets of constant shape. SIAM J.Math. Anal, 1984, 15(4):723-736.
37.张立明.人工神经网络的模型及其应用[M].复旦大学出版社,1992:1-12.
38. R. C. Eberhart, R. W. Dobbins. Neural Network PC Tools. Academic Press.1990.
39. B. J. Werbos. Backpropagation Troth Time: What It Does and How to Do It. Proc. IE EE.1990:78 (10):1550 -1560.
40. D .R uck, S .Roges. The Multilayer Perceptron as an Approximation to a Bayes Optimal Discriminant Function.IEEE Trans.Neural Networks. 1990, 2:296- 298.
41. F .K anaya,S .M iyaker.Bayes Statistical Behavior and Valid Generalization of Patern C lassifying Neural Networks.IEEE Trans.Neural Networks.1992,3 :471-475.
42. E .Barnard.Comments on Bayes Statistical Behavior and Valid Generalization of Patern Classifying Neural Networks.IEEE Trans.Neural Networks.1993, 4:1026-1027.
43. S. C. Thomopoulos, I. N. Papadakis, H. Sahinoglou, and N. N. Okello.Centralized and Distributed Hypothesis Testing with Structured Adaptive Networks and Perception-Type Neural Networks. Proc. SPIE, 1991, 1611:35-51.
44. K.Halford, D.Kazakkos, D.PadosandP, and Papantoni-Kazakkos.NeuralNetwork with Feed-back Binary Hypothes is Testing.Proc.IEEE Trans.system, cybem, oct.1991:1479-1486.
45. P. Dimitris, K.W. Halford, K. Dimitri and P.P-Kazakos. Distributed Binary Hypothesis Testing with Feedback Neural Network. IEEE Trans. system, cybem, 1995, 25 (1):21-42.
46. A. P. Dimitris. New Nonleast-squares Neural Network Learning Algorithm for Hypothes is Testing.IEEE Trans.Neural Networks.1995, 36 (3):596-609.
47. S.J. Ho, J. J. Hoon.Adaptive Image Sequence Resolution Enhancement Using Multisca le Decomposition Based Image Fusion. Proc. SPIE. 2000, 40 67:1589-1600.
48. J.S .T aur, S .Y .Kung.Fuzzy Decision Neural Network and A pplication to Data Fusion. Information science, 1993, 71:171~180.
49.闻新,宋屹,周露.模糊系统和神经网络的融合技术[J].系统工程与电子技术,vol.21(5), 1999,55-58.
50.张燕军等.基于神经网络与模糊融合的外围故障诊断方法[J].计算机测量与控制,Vol.16(5),2008,604-606.
51.沈景悦,吴秀清,刘严岩.一种基于模糊融合的遥感图像目标识别方法[J].计算机工程与应用,2005,16:83-85.
52. T.Kohonen.Self-Organized Formation of Feature Maps.Cybern. Syst.Recognit.Learn, 19 84:3-12.
53. T. Kohonen. The Set Organizing Map. Proceeding of the IEEE. 1990, 78(1):1464-1480
54. T.Kohonen.Fast Evolutionary Learning with Batch-type Self-Organizing Maps.Neural Pr ocessing Letters.1999, 9(2):153-162.
55. Pongsak Ajjimarange.Neural Network Model for Fusion of Visible and Infrared Sensor O utputs.SPIE.1988, 1003:153-160.
56. A. Chiuderi, S. Fini and V. Cappellini. An Application of Data Fusion to Landcover C lassification of Remote Sensed Imagery: A Neural Network Approach.Proc.Int.Conf.MFI.1994:756-762.
57. S. C. A. Thomopoulos, D. K. Bougoulias. DIGNET: A Self-Organizing Neural Network for Automatic Patern Recognition, Classification and Data Fusion.SPIE .1991, 1611:478-495.
58. C. Wann, S. C. A. Thomopoulos. Unsupervised Learning Neural Network with Application to Data Fusion.Proc.A merica Control Conf.1994:1361-1365.
59. S.C .A .T homopoulos, C .Wann.Clustering with Unsupervised Learning Neural Network with Application to Data Fusion.SPIE.1994, 2093:621-632.
60.吴今培,孙德山.现代数据分析[M].北京:机械工业出版社,2006.
61.焦竹青,熊伟丽,张林等.基于信任度函数的多传感器数据融合及其应用[J].东南大学学报,2008,38(S1):253-257.
62.胡振涛,刘先省.基于相对距离的一种多传感器数据融合方法[J].系统工程与电子技术, 2006, 28(2):196-198.
63.邢文训,谢金星.现代优化计算方法[M].北京:清华大学出版社,1999.
64.杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,6(5):87-94,
65.柯晶,钱积新,乔谊正.一种改进粒子群优化算法[J].电路与系统学报,2003,8(5):87-91.
66. Kannan, S. Slochanal, S.Mary Raja. Application of particle swarm optimization technique and its variants to generation expansion planning problem [J]. Electric Power Systems Research, Volume: 70, Issue: 3, August, 2004, pp.203-210.
67. Sugisaka, Masanori; Fan, Xinjian. An effective search method for neural network based face detection using particle swarm optimization [J]. IEICE Transactions on Information and Systems vE88n 22005,pp.214-222.
68. Ghoshal, S.P. Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control [J]. Electric Power Systems Research, Volume: 72, Issue: 3, December 15, 2004, pp.203-212.
69. Abido, M.A. Optimal power flow using particle swarm optimization [J]. International Journal of Electrical Power and Energy Systems, Volume: 24, Issue: 7, October, 2002,pp.563-571.
70. Chen, Yifan; Dubey, Vimal K. Ultrawideband source localization using a particle-swarm-optimized capon estimator [J]. 2005 IEEE International Conference on Communications, ICC 2005, pp.173-179.
71. Shi Y,Eberhart R. Fuzzy Adaptive Particle Swarm Optimization[C].In:Proc Congress on Evolutionary Computation,Seoul,Korea,2001.
72. Angeline P J. Evolutionary Optimization versus Particle Swarm Optimization:Philosophy and Performance Differences[C]. In:Evolutionary Programming VII,1998:601-610.
73. Lovbjerg M, Rasmussen T K, Krink T. Hybird Particle Swarm Optimiser with Breeding and Subpopulations[C].In: Proc of the third Genetic and Evolutionary Computation Conference, 2001.
74. Van den Bergh F, Engelbrecht a P. Training Product Uint Networks Using Cooperative Particle Swarm Optimizations[C].In: Proc of the third Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, 2001.
75. Van den Bergh F, Engelbrecht a P. Effects of Swarm Size on Cooperative Particle Swarm Optimizers[C].In: Proc of the third Genetic and Evolutionary Computation Conference (GECCO), San Francisco, USA, 2001.
76. Kennedy J, Eberhart R. A Discrete Binary Version of the Particle Swarm Algorithm[C]. IEEE Int Conf on Computational Cybernetics and Simulation, 1997:4104-4108.
77. Clerc M. Discrete Particle Swarm Optimization Illustrated by the Traveling Salesman Problem[C]. Protein Expression & purification 2000(14), pp.246-251.
78. Van den Bergh F, Engelbrecht A P. A new locally convergent particle swarm optimizer [A]. Proceedings of IEEE Conference on Systems,Man,and Cybernetics[C].Hammamet,Tunisia,2002:96-101.
79. Shi Y H, etal.Experimental Study of Particle Swarm Optimization[R].Proceedings of SCI Conference, Orlando, FL, 2000.
80. Eberhart R C,Shi Y H. Evolving Artificial Neural Networks[R].Proceedings of Int Conference on Neural Networks and Brain,Beijing,1998(a).
81.俞欢军,张丽平,陈德钊,宋晓峰,胡上序.复合粒子群优化算法在模型参数估计中的应用[J].高校化学工程学报,2005(05):284-287.
82. Claudia O.Ourique, Evaristo C. Biscaia. The use of particle swarm optimization for dynamical analysis in chemical processes [J]. Computers and Chemical Engineering 26 (2002) 1783-1793.
83. Ayed Salman, Imtiaz Ahmad, Sabah Al-Madani. Particle swarm optimization for taskassignment problem [J]. Microprocessors and Microsystems 26 (2002) 363-371.
84.王俊伟,汪定伟.一种带有梯度加速的粒子群算法[J].控制与决策,2004,(11):632-637.
85.刘益剑,张建明.微粒群算法在自动控制系统设计中的应用[J].高技术通讯,2005,(7):427-431.
86.王介生,王金城,王伟.基于粒子群算法的PID控制器参数自整定[J].控制与决策,2005,(01):54-58.
87.张宇林,蒋鼎国,黄翀鹏等.基于粒子群算法的多传感器数据融合[J].化工学报,2008,59(7):1703-1706.
88. Dujmovi?, J., Weighted conjunctive and disjunctive means and their application in system evaluation. Journal of the University of Belgrade, EE Dept., Series Mathematics and Physics.1974, v483. 147-158.
89. Harald Dyckhoff and Witold Pedrycz. Generalized means as model of compensative connectives [J]. Fuzzy Sets and Systems, 1984, 14(2), 143-154.
90. J.Sun etal. Particle swarm optimization with particles having quantum behavior. Proceedings of the congress on Evolutionary compution, 2004. pp: 325-331.
91. J.Sun etal. A global search strategy of Quantum behaved particle swarm optimization. Proceedings of the congress on Cybernetics and Intelligent systems.
92.熊伟丽,徐保国,孙俊等.改进QDPSO算法在BP网络训练中的应用[J].系统仿真学报,2005,17(9).
93.冯斌.群体智能优化算法和智能生化变量软测量模型的研究[J].江南大学博士论文,2005.
94.张宇林,蒋鼎国,朱小六等.基于QDPSO-BP网络的多传感器融合算法[J].传感器与微系统,2008,27(3):21:23.
95. Zhang Q H, Benveniste A Wavelet networks [J].IEEE Trans on NN, 1992, 3(6):889-898.
96. Cho S.Y.Chow TWS.A Neural learning based Reflectance Model for 3-D Shape Reconstruction.IEEE Transactions on Indusial Electronics, 2000, 47(6):1346-1349.
97. S.G.Mallat. Multiresolution approximation and wavelet orthogonal. American Math. Society, 1989, 315(1): 69-87.
98. S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machina Intelligence, 1989, 11(7): 674-693.
99. Yulin Zhang,Baoguo Xu,Xia zhu Wenbo Xu.Wavelet Analysis and Processing Algorithm for Images Compressing and Data Fusion.In:2008 International Symposium on DistributedComputing and Applications for Business Engineering and Science, Dalian, China, July, 2008:290-294
100.张宇林,朱小六,徐保国.基于WNN的BP算法的多传感器数据融合[J].传感器与微系统,2009.
101.Delyon B, Juditsky A, Benveniste A. Accuracy analysis for wavelet approximations [J].IEEE Trans on NN, 1995, 6(2):332-348.
102.裴浩东.基于神经网络的稳态优化和控制研究[D].杭州:浙江大学,2001.
103.吕伟杰.小波网络与多模型自适应控制理论的研究[D].天津:天津大学,2002.
104刘广明,杨劲松,鞠茂森,聂杰.电磁感应土地测量技术及其在农业领域的应用[J].土壤,2003(1):27-29.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |