A high performance neural network for solving nonlinear programming problems with hybrid constraints |
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| Institution: | 1. Department of Automation, University of Science and Technology of China, Hefei 230027, PR China;2. Department of Applied Mathematics, Southeast University, Nanjing 210096, PR China;3. Department of Manufacture Engineering and Engineering Management, City University of Hong Kong, Hong Kong, PR China;1. Universidade Tecnolôgica Federal do Paraná, Campus Cornélio Procòpio Avenida Alberto Carazzai, 1640, CEP 86300-000 Cornélio Procópio, PR, Brazil;2. Department of Electrical Engineering, Londrina State University, Rod. Celso Garcia Cid - PR445, PO. Box 10.011, CEP: 86057-970 Londrina, PR, Brazil;1. Universidade Tecnológica Federal do Paraná, Campus Cornélio Procópio, Avenida Alberto Carazzai, 1640, CEP 86300-000 Cornélio Procópio, PR, Brazil;2. Department of Electrical Engineering, Londrina State University, Rod. Celso Garcia Cid – PR445, Po. Box 10.011, CEP: 86057-970 Londrina, PR, Brazil |
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| Abstract: | A continuous neural network is proposed in this Letter for solving optimization problems. It not only can solve nonlinear programming problems with the constraints of equality and inequality, but also has a higher performance. The main advantage of the network is that it is an extension of Newton's gradient method for constrained problems, the dynamic behavior of the network under special constraints and the convergence rate can be investigated. Furthermore, the proposed network is simpler than the existing networks even for solving positive definite quadratic programming problems. The network considered is constrained by a projection operator on a convex set. The advanced performance of the proposed network is demonstrated by means of simulation of several numerical examples. |
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