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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