An Improved Zhang Neural Network Model Solving the Matrix Inverse Online

摘要

In this paper, an optional Moore-Penrose (M-P) inverse error function is investigated and guarantee the global exponential convergence of the neural model and find out its exact inverse of a given time-invarying matrix. This increases the category of Zhang neural network (ZNN) model based on vector-valued matrix-formed errors function and also broadens the field of network. Choosing appropriate ZNN model based on the optional generalized inverse vector-valued matrix-formed error function, which can be applied to the practical problem of matrix inversion with high complexity, it can replace the traditional numerical algorithm. Compared with the numerical algorithm, the improved ZNN model is more efficient, real-time, and accurate in solving the problem, it satisfies the needs of production and life. In addition, the simulative results validate the theoretical analysis and demonstrate the efficacy of the neural model on static matrix inversion.