Comprehensive Study on a Fuzzy Parameter Strategy of Zeroing Neural Network for Time-Variant Complex Sylvester Equation
Ying Kong, Xuxiang Zeng, Yunliang Jiang, Danfeng Sun
Abstract
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 (CFPZNN) 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 (TVP-ZNN), the fuzzy parameter generated by the FLS fluctuates according with convergent error and adjusts the convergent rate adaptively. Three different activated functions (AFs) equipped with the CFP-ZNN model are analyzed and discussed. Finite convergence characteristic of the CFP-ZNN model with Signbi-power (SBP) is testified. Furthermore, various membership functions (MFs) and various fuzzy control output values are studied and compared to exhibit the performance of the CFPZNN 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.