Generalized convexity of functions and generalized monotonicity of set-valued maps
Journal of Optimization Theory and Applications
Differential Inclusions: Set-Valued Maps and Viability Theory
Differential Inclusions: Set-Valued Maps and Viability Theory
On global exponential stability of standard and full-range CNNs
International Journal of Circuit Theory and Applications - Cellular Wave Computing Architecture
A new projection-based neural network for constrained variational inequalities
IEEE Transactions on Neural Networks
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks - Part 1
A One-Layer Recurrent Neural Network for Constrained Nonsmooth Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, a one-layer recurrent projection neural network is proposed for solving pseudoconvex optimization problems with general convex constraints. The proposed network in this paper deals with the constraints into two parts, which brings the network simpler structure and better properties. By the Tikhonov-like regularization method, the proposed network need not estimate the exact penalty parameter in advance. Moreover, comparing with some existing neural networks, the proposed network can solve more general constrained pseudoconvex optimization problems. When the solution of the proposed network is bounded, it converges to the optimal solution set of considered optimization problem, which may be nonsmooth and nonconvex. Meantime, some sufficient conditions are presented to guarantee the boundedness of the solution of the proposed network. Numerical examples with simulation results are given to illustrate the effectiveness and good characteristics of the proposed network for solving constrained pseudoconvex optimization.