New error function designs for finite-time ZNN models with application to dynamic matrix inversion

Abstract

The zeroing neural network (ZNN), as a special kind of recurrent neural network (RNN), is often utilized to solve dynamic matrix inversion problems in the many fields recently. In this work, two finite-time ZNN (termed as ZNN-A and ZNN-B) models with the sign-bi-power (SBP) activation function are proposed by designing two novel error functions. The theoretical analysis shows the superior stability and finite-time convergence properties of the ZNN-A and ZNN-B models. Furthermore, three simulative examples show the effectiveness of the proposed ZNN-A and ZNN-B models for finding dynamic matrix inversion and the correctness of the corresponding theorems. To reveal the superior performance of the proposed ZNN-A and ZNN-B models with SBP activation function, the standard ZNN model with the linear activation function is comparatively applied in experiments.