A recurrent neural network for solving a class of generalized convex optimization problems

• Published in: Neural networks Vol. 44; pp. 78 - 86
• Main Authors: Hosseini, Alireza, Wang, Jun, Hosseini, S. Mohammad
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Differential inclusion; Exact sciences and technology; Generalized convex; Mathematical analysis; Mathematics; Neural Networks (Computer); Nonsmooth optimization; Ordinary differential equations; Problem Solving; Pseudoconvexity; Recurrent neural networks; Sciences and techniques of general use; Time Factors; Recurrent neural networks; Generalized convex; Nonsmooth optimization; Differential inclusion; Pseudoconvexity; Recurrent neural nets; Generalized function; Neural network; Modeling; Convex programming; Constrained optimization; Nonsmooth analysis; Optimal solution; Inequality constraint; Equality constraint; Convex function; Objective function; Regularity; Inequality; Mathematical programming

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...