A novel hybrid Zhang neural network model for time-varying matrix inversion

Abstract

A new hybrid Zhang neural network (HZNN) model is formulated to solve time-varying matrix inversion problem. The network is designed such that it encompasses the goodness of both gradient neural network (GNN) and Zhang neural network (ZNN). The stability of the network and convergence results are established theoretically. The rate of convergence of HZNN model is much faster compared to the classical GNN model and ZNN model for time-varying matrix inversion problem. Nonlinear activation functions for HZNN need not have to be odd and monotonically increasing for the network to converge, however with certain restrictions. Simulations are carried out to test the efficiency of HZNN over GNN and ZNN. Also, HZNN with odd and non-odd activation functions are examined for convergence. It is observed that the network converges much faster with the proposed non-odd function 3 (nof3). Furthermore, the HZNN model with odd and non-odd functions are applied to the kinematic control of a two-link planar manipulator for tracking a circular path.

Keywords

• Hybrid Zhang neural network
• Gradient neural network
• Time-varying matrix inversion
• General activation functions