A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing
IEEE Transactions on Fuzzy SystemsPublished 4 May 2009
Chia‐Feng Juang, Ren-Bo Huang, Yang-Yin Lin
Citations130
SJR quartileQ1
SJR score3.61
SNIP2.80
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TL;DR
A recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing and is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions.
Abstract
This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.
Keywords
Computer ScienceEngineering
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