Stable Learning Mechanism for Novel Takagi - Sugeno - Kang Type Interval - valued Fuzzy Systems

In this paper, we propose a novel Takagi-SugenoKang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propagation (BP) algorithm having an optimal learning rate (adaptive learning rate) to guarantee the stability and faster convergence. Finally, the TIVNFS-A with the optimal BP algorithm is applied in nonlinear system identification to demonstrate the effectiveness and performance.