Title

A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Subject to Linear Equality Constraints

Abstract

In this paper, a one-layer recurrent neural network is presented for solving pseudoconvex optimization problems subject to linear equality constraints. The global convergence of the neural network can be guaranteed even though the objective function is pseudoconvex. The finite-time state convergence to the feasible region defined by the equality constraints is also proved. In addition, global exponential convergence is proved when the objective function is strongly pseudoconvex on the feasible region. Simulation results on illustrative examples and application on chemical process data reconciliation are provided to demonstrate the effectiveness and characteristics of the neural network.

Department(s)

Computer Science

Keywords and Phrases

Global convergence; Linear equality constraints; Pseudoconvex optimization; recurrent neural networks

International Standard Serial Number (ISSN)

1045-9227

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

PubMed ID

22057059

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