Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology

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• 作者：Majdi Al-Mahasneh ; Mohannad Aljarrah ; Taha Rababah…
• 刊名：Food Engineering Reviews
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：8
• 期：3
• 页码：351-366
• 全文大小：667 KB
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Food Science
  Chemistry
  
• 出版者：Springer New York
• ISSN：1866-7929
• 卷排序：8

文摘

Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic hybrid intelligent system. It combines the human-like reasoning style of fuzzy logic system (FLS) with the learning and computational capabilities of artificial neural networks (ANNs). ANFIS has several applications related to food processing and technology. The first part of this review provides a brief overview and discussion of ANFIS including: the general structure and topology, computational considerations, model development and testing. In the second part, two detailed examples are explained to demonstrate the capabilities of ANFIS in comparison with other modeling methods, followed by a brief but comprehensive discussion of ANFIS applications in different food processing and technology areas. The applications are divided into five main categories: food drying, prediction of food properties, microbial growth and thermal process modeling, applications in food quality control and food rheology. In all applications, the performance of ANFIS is compared to other methods such as ANNs, FLS and multiple regressions when available. It is concluded that, in most applications, ANFIS outperforms other modeling tools such as ANNs, FIS or multiple linear regression. Finally, some application guidelines, advantages and disadvantages of ANFIS are discussed.KeywordsANFISANNsFLSMLRFood process modelingQuality control