Title : ( A Novel Dynamic System Model Based on NCP Function for Solving Nonconvex Nonlinear Optimization Problems )
Authors: Mohammad Moghaddas , Sohrab Effati ,
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Abstract
This paper presents a neural network based on NCP function to solve a class of nonconvex nonlinear optimization (NCNO) problems. The proposed neural network is a gradient model, which is constructed with an NCP function and an unconstrained minimization problem. The main feature of this neural network is that its equilibrium point coincides with optimal solution of the original problem. By utilizing a suitable Lyapunov function, it is shown that the proposed neural network is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, an example is given to show the good performance and the applicability of the neural network.
Keywords
, Neural network, Nonconvex optimization, NCP function, p-Power convexification method, Stability, Convergent.
@inproceedings{paperid:1041791,
author = {Moghaddas, Mohammad and Effati, Sohrab},
title = {A Novel Dynamic System Model Based on NCP Function for Solving Nonconvex Nonlinear Optimization Problems},
booktitle = {The 7th International Conference of Iranian Operations Research Society},
year = {2014},
location = {سمنان, IRAN},
keywords = {Neural network; Nonconvex optimization; NCP function; p-Power convexification method; Stability; Convergent.},
}
%0 Conference Proceedings
%T A Novel Dynamic System Model Based on NCP Function for Solving Nonconvex Nonlinear Optimization Problems
%A Moghaddas, Mohammad
%A Effati, Sohrab
%J The 7th International Conference of Iranian Operations Research Society
%D 2014
