Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44329
標題: A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning
作者: Juang, C.F.
莊家峰
Tsao, Y.W.
關鍵字: Evolving system
fuzzy neural networks (FNNs)
online fuzzy clustering
structure learning
type-2 fuzzy systems
logic systems
inference system
particle swarm
identification
prediction
algorithm
filter
interpretability
convergence
stability
期刊/報告no:: Ieee Transactions on Fuzzy Systems, Volume 16, Issue 6, Page(s) 1411-1424.
摘要: This paper proposes a self-evolving interval type-2 fuzzy neural network (SEIT2FNN) with online structure and parameter learning. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. The initial rule base in the SEIT2FNN is empty, and the online clustering method is proposed to generate fuzzy rules that flexibly partition the input space. To avoid generating highly overlapping fuzzy sets in each input variable, an efficient fuzzy set reduction method is also proposed. This method independently determines whether a corresponding fuzzy set should be generated in each input variable when a new fuzzy rule is generated. For parameter learning, the consequent part parameters are tuned by the rule-ordered. Kalman filter algorithm for high-accuracy learning performance. Detailed learning equations on applying the rule-ordered Kalman filter algorithm to the SEIT2FNN consequent part learning, with rules being generated online, are derived. The antecedent part parameters are learned by gradient descent algorithms. The SEIT2FNN is applied to simulations on nonlinear plant modeling, adaptive noise cancellation, and chaotic signal prediction. Comparisons with other type-1 and type-2 fuzzy systems in these examples verify the performance of the SEIT2FNN.
URI: http://hdl.handle.net/11455/44329
ISSN: 1063-6706
文章連結: http://dx.doi.org/10.1109/tfuzz.2008.925907
Appears in Collections:電機工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.