A projection neural network for circular cone programming
type: Item
instance of: scholarly article
title: A projection neural network for circular cone programming
zbMATH Open document ID: 1427.90226
DOI: 10.1155/2018/4607853
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: A projection neural network method for circular cone programming is proposed. In the KKT condition for the circular cone programming, the complementary slack equation is transformed into an equivalent projection equation. The energy function is constructed by the distance function and the dynamic differential equation is given by the descent direction of the energy function. Since the projection on the circular cone is simple and costs less computation time, the proposed neural network requires less state variables and leads to low complexity. We prove that the proposed neural network is stable in the sense of Lyapunov and globally convergent. The simulation experiments show our method is efficient and effective.
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 49J52
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 90C33
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 7018991
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/4607853
OpenAlex ID: W2806242234
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description: scientific article; zbMATH DE number 7018991