A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design

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.

Keywords:

• electromagnetism-like algorithm
• neural fuzzy system
• nonlinear control
• identification
• mobile robot

Acknowledgements

The authors would like to thank the Chief Editor, Associate Editor and anonymous reviewers for their insightful comments and valuable suggestions. This work was partially supported by the National Science Council, Taiwan, R.O.C., under contract No: NSC-97-2221-E-155-033-MY3.