A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing
This paper proposes a recurrent self-evolving fuzzy neural network with local feedbacks (RSEFNN-LF) for dynamic system processing A RSEFNN-LF is composed of zero-order or first-order Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy if-then rules The recurrent structure in a RSEFNN-LF comes from locally feeding the tiring strength of a fuzzy rule back to itself. A RSEFNN-LF is constructed on-line via simultaneous structure and parameter learning In structure learning, an efficient rule and fuzzy set generation algorithm is proposed to generate fuzzy rules on-line and reduce the number of fuzzy sets in each dimension In parameter learning, the consequent part parameters are learned (hi nth a varying-dimensional Kalman filter algorithm whose input dimension varies with structure learning The antecedent part and feedback loop parameters are learned using a gradient descent algorithm The RSEFNN-LF is applied to dynamic system identification, chaotic sequence prediction, and speech recognition problems. This paper also compares the performance of the RSEFNN-LF with other recurrent fuzzy neural networks. (C) 2010 Elsevier B.V. All rights reserved.
dynamic system identification speech recognition fuzzy system models neuro-fuzzy systems recurrent fuzzy systems dynamic sequence prediction