A Nonpenalty Neurodynamic Model for Complex-Variable Optimization

In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in pract...

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Bibliographic Details
Published inDiscrete dynamics in nature and society Vol. 2021; pp. 1 - 10
Main Authors Liu, Bao, Mei, Xuehui, Jiang, Haijun, Wu, Lijun
Format Journal Article
LanguageEnglish
Published New York Hindawi 16.02.2021
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Algorithms
Design
Design parameters
Equality
Equilibrium
Lagrange multiplier
Linear programming
Machine learning
Neural networks
Optimization
System theory
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Summary:In this paper, a complex-variable neural network model is obtained for solving complex-variable optimization problems described by differential inclusion. Based on the nonpenalty idea, the constructed algorithm does not need to design penalty parameters, that is, it is easier to be designed in practical applications. And some theorems for the convergence of the proposed model are given under suitable conditions. Finally, two numerical examples are shown to illustrate the correctness and effectiveness of the proposed optimization model.
ISSN:1026-0226
1607-887X
DOI:10.1155/2021/6632257