Strong Convergence of Neuro-Fuzzy Learning With Adaptive Momentum for Complex System

This paper studies a split-complex-valued neuro-fuzzy algorithm for fuzzy inference system, which realizes a frequently used zero-order Takagi–Sugeno–Kang system. Here, adaptive momentum is utilized to speed up the learning convergence. Some strong convergence results are demonstrated based on the weak convergence results, which expresses that the weight sequence of fuzzy parameters converges to a fixed point. Simulation results support the theoretical findings.

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