Toward Fuzzy Activation Function Activated Zeroing Neural Network for Currents Computing
Jie Jin, Weijie Chen, Aijia Ouyang, Haiyan Liu
Abstract
In order to improve the convergence and noise resistance ability of the ZNN models, a fuzzy activation function (FAF) is designed. Based on the FAF, a fuzzy activation function activated zeroing neural network (FAFZNN) for online fast computing circuit currents is proposed. By introducing the fuzzy logic technique, the convergence and noise resistance ability of the proposed FAFZNN model are further promoted, and it realizes prescribed-time stable, which is irrelevant to its system initial states even in noisy environment. Moreover, the prescribed-time convergence and strong robustness to noises of the proposed FAFZNN model are verified by strict mathematical analysis. The comparable simulation results for static direct currents (DC) and dynamic alternating currents (AC) computing in noiseless and noisy environment further validates its superior effectiveness and robustness for practical applications.