Novel Neural Network for Dealing with a Kind of Non-smooth Pseudoconvex Optimization Problems

• Published in: Ji suan ji ke xue Vol. 49; no. 5; pp. 227 - 234
• Main Authors: Yu, Xin, Lin, Zhi-liang
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01-05-2022; Editorial office of Computer Science
• Subjects: Bioinformatics; Convergence; differential inclusion|recurrent neural network(rnn)|nonsmooth pseudoconvex optimization|optimal solution set|penalty parameter; Machine learning; Neural networks; Optimization; Penalty function; Signal processing
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210400179

The research of optimization problem is favored by researchers.Nonsmooth pseudoconvex optimization problems are a special kind of nonconvex optimization problems, which often appear in machine learning, signal processing, bioinformatics and various scientific and engineering fields.Based on the idea of penalty function and differential inclusion, a new neural network me-thod is proposed to solve the non-smooth pseudoconvex optimization problems with inequality constraints and equality constraints.Under given assumptions, the solution of the RNN can enter in the feasible region in finite time and stay there there-after, at last converge to the optimal solution set of the optimization problem.Compared with other neural networks, the RNN has the following advantages: 1)simple structure, it is a single-layer model; 2)it is not need to compute an exact penalty parameter in advance; 3)the initial point is chosed arbitrarily.Under the environment of MATLAB,mathematical simulation experiments show that state solution