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 $(\mathit{nof}3)$. 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.

制定了一种新的混合张神经网络 (HZNN) 模型来解决时变矩阵求逆问题。该网络的设计使其包含梯度神经网络 (GNN) 和张神经网络 (ZNN) 的优点。理论上建立了网络的稳定性和收敛结果。对于时变矩阵求逆问题，HZNN 模型的收敛速度比经典 GNN 模型和 ZNN 模型快得多。HZNN 的非线性激活函数不必是奇数和单调递增的网络才能收敛，但有一定的限制。进行模拟以测试 HZNN 对 GNN 和 ZNN 的效率。此外，检查具有奇数和非奇数激活函数的 HZNN 的收敛性。$(\mathit{没有}3)$. 此外，将具有奇函数和非奇函数的 HZNN 模型应用于跟踪圆形路径的双连杆平面机械手的运动学控制。