Simulative Results of Time-Variant Nonlinear Equation Systems Solved by Integral-Aided Zhang Neuronet with Various Noises Suppressed

To solve time-variant nonlinear equation systems (TVNES), a neuronet model called integral-aided Zhang neuronet (IAZN) model is presented and investigated. For comparison, a traditional Zhang neuronet (TZN) model is presented. In addition, simulative results show that the presented IAZN model possesses superior performance to the TZN model for solving TVNES with various noises suppressed. In detail, the presented IAZN model converges to the theoretical solution of TVNES with arbitrary constant noises, and has excellent ability to suppress bounded random noises and linear-form time-variant noises. Illustrative numerical experiments substantiate the efficacy and superiority of the presented IAZN model for solving TVNES with various noises suppressed.