標題: Simplified Interval Type-2 Fuzzy Neural Networks
作者: Lin, Yang-Yin
Liao, Shih-Hui
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: Fuzzy identification;on-line fuzzy clustering;type-2 fuzzy neural networks (FNNs);type-2 fuzzy systems
公開日期: 1-May-2014
摘要: This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors q(l) and q(r) are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.
URI: http://dx.doi.org/10.1109/TNNLS.2013.2284603
http://hdl.handle.net/11536/24202
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2013.2284603
期刊: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume: 25
Issue: 5
起始頁: 959
結束頁: 969
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