Analysis of Nonlinear Dynamics of Neural Networks

Dynamical neural networks proved to be an important tool to solve some practical engineering problems in the areas such as optimization, image and signal processing, control systems, associative memories, and pattern recognition. When employing neural networks to solve practical engineering problems, it is crucial to be able to completely characterize the dynamical properties of neural networks. There are many various classes of neural network models that can be described in the form of nonlinear systems. Therefore, neural networks may exhibit extremely different complex dynamical behaviors depending on the model and network parameters. Hence, the analysis of nonlinear dynamics of neural networks still possesses new challenges to researchers. The aim of this special issue is to solicit theoretical and application research in the fields of neural networks exploiting their nonlinear dynamics. We believe it provides a good opportunity for reflection on current developments in the nonlinear analysis of dynamical behaviors of neural networks.The papers submitted to this special issue represent a mixture of cross-cutting investigations and provide deep insight into the current developments in the field. The accepted papers in this special issue addressed the following topics: