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|標題:||A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing||作者:||Juang, C.F.
|關鍵字:||Dynamic system identification;online fuzzy clustering;recurrent fuzzy;neural networks (RFNNs);recurrent fuzzy systems;type-2 fuzzy systems;logic systems;inference network;identification;time;recognition;models;sets||Project:||Ieee Transactions on Fuzzy Systems||期刊/報告no：:||Ieee Transactions on Fuzzy Systems, Volume 17, Issue 5, Page(s) 1092-1105.||摘要:||
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.
|Appears in Collections:||電機工程學系所|
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