Fuzzy Neural Networks (FNNs) Training Algorithm with Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS)
Wang,Jing1; Chen,Philip2; Ma,Zhenyuan1; Xiao,Zhenghong1
Source Publication2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018
AbstractFuzzy neural network (FNN) often suffers from overfitting problem, especially when FNN has large number of parameters. In the FNN system, there are two types of adjustable parameters, one is control parameters, and the other is link weights of consequent part. To improve convergent rate, Dropout technique is first adopted for Fuzzy neural network. A new training algorithm with dropout technique for FNN is proposed via its equivalent F-CONFIS. Illustrative examples are provided for checking the validity of the proposed method. Simulation attained satisfactory results. Proposed method for Fuzzy neural network via F-CONFIS has its rising values in all practical applications, such as system identification, expert System and image information processing system..., etc.
KeywordAdaptive Neural-Fuzzy Inference Systems(ANFIS) Fuzzy Inference Systems Fuzzy Neural Networks Gradient Descent Neural Networks
URLView the original
Scopus ID2-s2.0-85079199805
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.College of Computer Science,Guangdong Polytechnic Normal University,Guangdong,China
2.Faculty of Science and Technology,University of Macau,MSAR,Macao
Recommended Citation
GB/T 7714
Wang,Jing,Chen,Philip,Ma,Zhenyuan,et al. Fuzzy Neural Networks (FNNs) Training Algorithm with Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS)[C],2018:80-84.
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