Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
404135 | Neural Networks | 2013 | 9 Pages |
Abstract
In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Alireza Hosseini, Jun Wang, S. Mohammad Hosseini,