Finite-time recurrent neural networks for solving nonlinear optimization problems and their application

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• 作者：Peng Miao^a ; ^bAuthor Vitae ; Yanjun Shen^a ; ^{shenyj@ctgu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yujiao Li^cAuthor Vitae ; Lei Bao^aAuthor Vitae
• 关键词：Finite-time stable ; Recurrent neural network ; Nonlinear optimization problems ; Continuous but non-smooth ; Hydrothermal scheduling problem
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 February 2016
• 年：2016
• 卷：177
• 期：Complete
• 页码：120-129
• 全文大小：802 K

文摘

This paper focuses on finite-time recurrent neural networks with continuous but non-smooth activation function solving nonlinearly constrained optimization problems. Firstly, definition of finite-time stability and finite-time convergence criteria are reviewed. Secondly, a finite-time recurrent neural network is proposed to solve the nonlinear optimization problem. It is shown that the proposed recurrent neural network is globally finite-time stable under the condition that the Hessian matrix of the associated Lagrangian function is positive definite. Its output converges to a minimum solution globally and finite-time, which means that the actual minimum solution can be derived in finite-time period. In addition, our recurrent neural network is applied to a hydrothermal scheduling problem. Compared with other methods, a lower consumption scheme can be derived in finite-time interval. At last, numerical simulations demonstrate the superiority and effectiveness of our proposed neural networks by solving nonlinear optimization problems with inequality constraints.