关键词: 非光滑伪凸函数, 神经网络, 收敛, 优化问题

Abstract: Aiming at the nonsmooth pseudoconvex optimization problem with inequality constraints, a new recurrent neural network based on differential inclusion theory is proposed. According to the objective function and constraints, a penalty function is designed, which changes with the change of the state vector, so that the state vector of the neural network always moves in the direction of the feasible region and ensures that the state vector of the neural network can be in finite time. It enters the feasible region and converges to the optimal solution of the original optimization problem. Finally, two simulation experiments are used to verify the validity and accuracy of the neural network. Compared with the existing neural network, it is a new type of neural network model. The model has simple structure. It does not need to calculate the exact penalty factor, and most importantly, and it also does not need the bounded feasible region.

Key words: nonsmooth pseudoconvex functions, neural networks, convergence, optimization problems