期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 26, 期 2, 页码 685-696出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2012.09.011
关键词
Neural network; Convex programming; Dynamic model; Convergent; Stability
In this paper, a neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle to solve general convex nonlinear programming (GCNLP) problems. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the GCNLP problem. By employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient. (C) 2012 Elsevier Ltd. All rights reserved.
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