A One-Layer Recurrent Neural Network for Constrained Nonsmooth Optimization

Authors:
Qingshan Liu;Jun Wang
Affiliations:
School of Automation, Southeast University, Nanjing, China;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2011

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Abstract

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.