A survey of recent advances on stability analysis, state estimation and synchronization control for neural networks

Abstract

Nowadays, neural networks have been widely applied in many fields such as pattern recognition, signal and image processing and control theory. Over the past two decades or so, the analysis and synthesis for neural networks have received significant research attention. This paper provides a survey on the analysis and synthesis for neural networks, which is mainly concerned with the recent advances on stability analysis, state estimation and synchronization control for neural networks. First of all, the paper summarizes the recent results on the stability analysis for delayed neural networks, especially for neural networks with multiple discrete delays, neural networks with distributed delays, and discrete-time delayed neural networks. Then, the paper reviews the recent advances regarding the state estimation for neural networks with the emphasis on the network-based state estimation. Subsequently, the paper provides an overview on the synchronization control for neural networks. Finally, the conclusions and further research directions are given.