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Mathematical Problems in Engineering
Volume 2016, Article ID 1327235, 16 pages
http://dx.doi.org/10.1155/2016/1327235
Research Article

Model Building and Optimization Analysis of MDF Continuous Hot-Pressing Process by Neural Network

1School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2College of Electromechanical Engineering, Northeast Forestry University, Harbin, China

Received 8 March 2016; Accepted 9 August 2016

Academic Editor: Yakov Strelniker

Copyright © 2016 Qingfa Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a one-layer neural network for solving a class of constrained optimization problems, which is brought forward from the MDF continuous hot-pressing process. The objective function of the optimization problem is the sum of a nonsmooth convex function and a smooth nonconvex pseudoconvex function, and the feasible set consists of two parts, one is a closed convex subset of , and the other is defined by a class of smooth convex functions. By the theories of smoothing techniques, projection, penalty function, and regularization term, the proposed network is modeled by a differential equation, which can be implemented easily. Without any other condition, we prove the global existence of the solutions of the proposed neural network with any initial point in the closed convex subset. We show that any accumulation point of the solutions of the proposed neural network is not only a feasible point, but also an optimal solution of the considered optimization problem though the objective function is not convex. Numerical experiments on the MDF hot-pressing process including the model building and parameter optimization are tested based on the real data set, which indicate the good performance of the proposed neural network in applications.