標題: A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications
作者: Lin, Yang-Yin
Chang, Jyh-Yeong
Lin, Chin-Teng
電控工程研究所
腦科學研究中心
Institute of Electrical and Control Engineering
Brain Research Center
關鍵字: Compensatory operation;fuzzy identification;online fuzzy clustering;type-2 fuzzy systems
公開日期: 1-一月-2014
摘要: In this paper, a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN) is proposed for system modeling and noise cancellation problems. A TSCIT2FNN uses type-2 fuzzy sets in an FNN in order to handle the uncertainties associated with information or data in the knowledge base. The antecedent part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the TSCIT2FNN, where compensatory-based fuzzy reasoning uses adaptive fuzzy operation of a neural fuzzy system to make the fuzzy logic system effective and adaptive, and the consequent part is of the TSK type. The TSK-type consequent part is a linear combination of exogenous input variables. Initially, the rule base in the TSCIT2FNN is empty. All rules are derived according to online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by a variable-expansive Kalman filter algorithm to the reinforce parameter learning ability. The antecedent type-2 fuzzy sets and compensatory weights are learned by a gradient descent algorithm to improve the learning performance. The performance of TSCIT2FNN for the identification is validated and compared with several type-1 and type-2 FNNs. Simulation results show that our approach produces smaller root-mean-square errors and converges more quickly.
URI: http://dx.doi.org/10.1109/TIE.2013.2248332
http://hdl.handle.net/11536/22505
ISSN: 0278-0046
DOI: 10.1109/TIE.2013.2248332
期刊: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume: 61
Issue: 1
起始頁: 447
結束頁: 459
顯示於類別:期刊論文


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  1. 000323490100041.pdf