A nonlinear projection neural network for solving interval quadratic programming problems and its stability analysis
type: Item
instance of: scholarly article
title: A nonlinear projection neural network for solving interval quadratic programming problems and its stability analysis
zbMATH Open document ID: 1195.90072
DOI: 10.1155/2010/403749
author: Huaiqin Wu
author: Rui Shi
author: Leijie Qin
author: Feng Tao
author: Lijun He
published in: Mathematical Problems in Engineering
publication date: 2010-09-30T00:00:00Z
full work available at URL: https://eudml.org/doc/225665
review text: Summary: This paper presents a nonlinear projection neural network for solving interval quadratic programs subject to box-set constraints in engineering applications. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the interval quadratic optimization problems. By employing Lyapunov function approach, the global exponential stability of the proposed neural network is analyzed. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 5793745
MaRDI profile type: Publication
Wikidata QID: Q58653064
OpenAlex ID: W2053576936
cites work: Q3134873
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cites work: A new class of interval projection neural networks for solving interval quadratic program
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description: scientific article; zbMATH DE number 5793745