A high performance neural network model for solving chance constrained optimization problems

This paper presents a neural network model to solve chance constrained optimization (CCO) problems. The main idea is to convert the chance constrained problem into an equivalent convex second order cone programming (CSOCP) problem. A neural network model is then constructed for solving the obtained CSOCP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient.