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Solving a class of geometric programming problems by an efficient dynamic model
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DOI：10.1016/j.cnsns.2012.07.016.png)
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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 geometric programming (GP) problems. The main idea is to convert the GP problem into an equivalent convex optimization problem. A neural network model is then constructed for solving the obtained convex programming 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 B.V. All rights reserved.
Keyword:
Neural network
Geometric programming
Convex programming
Convergent
Stability
期刊
IF:
3.8
论文数:
9.3K
被引数:
1.8W
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