Design of Risk-Sensitive Optimal Control for Stochastic Recurrent Neural Networks By Using Hamilton-Jacobi-Bellman Equation

摘要

This paper presents a theoretical design for the stabilization of stochastic recurrent neural networks with respect to a risk-sensitive optimality criterion. This approach is developed by using the Hamilton-Jacobi-Bellman equation, Lyapunov technique, and inverse optimality, to obtain a risk-sensitive state feedback controller, which guarantees an achievable meaningful cost for a given risk-sensitivity parameter. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.