A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design |
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Authors: | Ching-Hung Lee Fu-Kai Chang Che-Ting Kuo Hao-Hang Chang |
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Affiliation: | 1. Department of Electrical Engineering , Yuan Ze University , Taiwan, R.O.C. chlee@saturn.yzu.edu.tw;3. Department of Electrical Engineering , Yuan Ze University , Taiwan, R.O.C. |
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Abstract: | This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the ‘attraction’ and ‘repulsion’ of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP. |
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Keywords: | electromagnetism-like algorithm neural fuzzy system nonlinear control identification mobile robot |
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