A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

This paper proposes a novel TSK-type interval-valued neural fuzzy system with asymmetric membership functions (TIVNFS-A) which is optimized by a species-based improved electromagnetism-like mechanism (SIEM) algorithm. The proposed TIVNFS-A uses interval-valued asymmetric fuzzy membership functions and the TSK-type consequent part to improve the approximation performance and to reduce the number of fuzzy rules. In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. To train the TIVNFS-As, the SIEM combines the advantages of electromagnetism-like mechanism (EM) algorithm and gradient-descent technique to obtain faster convergence and lower computational complexity. Based on a similarity measurement, the swarm of the SIEM has a dynamic population size, and its population is also divided dynamically into subpopulations for optimization. The gradient-descent method is applied to the dominant particle in each species for a neighborhood search. Finally, the nonlinear system identification and classification problems are shown to demonstrate the performance and effectiveness of the proposed TIVNFS-A with the SIEM.