Comprehensive Study on a Fuzzy Parameter Strategy of Zeroing Neural Network for Time-Variant Complex Sylvester Equation

To amplify the achievements on zeroing neural network (ZNN) and widen the application of fuzzy logic system (FLS), a complex fuzzy parameter zeroing neural network (CFP-ZNN) model is established to address the time-variant complex Sylvester equation problem. Varying from the fixed parameters in conventional ZNN (CZNN) or time-varying parameters in ZNN, the fuzzy parameter generated by the FLS fluctuates according with convergent error and adjusts the convergent rate adaptively. Three different activated functions equipped with the CFP-ZNN model are analyzed and discussed. Finite convergence characteristic of the CFP-ZNN model with sign-bi-power is testified. Furthermore, various membership functions and various fuzzy control output values are studied and compared to exhibit the performance of the CFP-ZNN model. Theoretical analyses and comparable simulation results among different ZNN-based neural network models in dealing with time-variant complex Sylvester equations are welly coincided.