Optimal regulation of stochastic cellular neural networks using differential minimax game

In this paper, we present an approach to optimally regulate stochastic cellular neural networks by using differential minimax game. In order to realize the design, we consider the vector of external inputs as a player and that of internal noises as an opposing player. The purpose of this study is to achieve the best rational stabilization in probability for stochastic cellular neural networks, and to attenuate noises to a predefined level with stability margins under an optimal control strategy. A numerical example is given to demonstrate the effectiveness of the proposed approach.