In this paper, we propose a species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms (SEMBP) for an interval type-2 fuzzy neural system with asymmetric membership functions (aIT2FNS). The proposed SEMBP combines the advantages of EM and BP algorithms to obtain the faster convergence and lower computational complexity. In addition, SEMBP uses the uniform method to have the initial solution agents scatter over the feasible solution region evenly and the notion of species which can locate multiple optima to provide bigger possibility of finding the global optimum. The proposed aIT2FNS system uses type-2 asymmetric fuzzy membership functions and the TSK type consequence part. Finally, the chaotic system identification problem is presented to show the performance and effectiveness of the proposed aIT2FNS with SEMBP algorithm.