Complex Zhang neural networks for complex-variable dynamic quadratic programming

Abstract

In this paper, a neurodynamic approach concerning complex Zhang neural networks (ZNNs) is presented to solve a complex-variable dynamic quadratic programming (QP). The proposed complex ZNNs are activated by various complex activation functions respectively, such as linear function, sign function and Li function. It is proved that ZNNs with different activation functions have different convergence rates involving super exponential convergency and finite time convergency. Furthermore, by introducing an integral term, a noise-tolerated ZNN is proposed to ensure the convergency under constant noise. Compared with existing works, the presented complex ZNNs in this paper have less amount of neurons and superior convergence rate. Some numerical examples are provided to illustrate the effectiveness and validity of the results. In addition, the complex ZNNs also apply well to LCMP beamforming problem.