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Abstract:
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.
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Source :
IEEE TRANSACTIONS ON CYBERNETICS
ISSN: 2168-2267
Year: 2014
Issue: 4
Volume: 44
Page: 554-564
1 1 . 8 0 0
JCR@2022
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:188
JCR Journal Grade:1
CAS Journal Grade:1
Cited Count:
WoS CC Cited Count: 80
SCOPUS Cited Count: 93
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 12