Nonlinear optimal control of stochastic recurrent neural networks with multiple time delays

This paper presents a theoretical design of how a nonlinear optimal control is achieved for multiple time-delayed recurrent neural networks under the influence of random perturbations. Our objective is to build stabilizing control laws to accomplish global asymptotic stability in probability as well as optimality with respect to disturbance attenuation for stochastic delayed recurrent neural networks. The formulation of the nonlinear optimal control is developed by using stochastic Lyapunov technique and solving a Hamilton-Jacobi-Bellman (HJB) equation indirectly. To illustrate the analytical results, a numerical example is given to demonstrate the effectiveness of the proposed approach.