Neural networks based on Lagrange multiplier for nonlinear programming

Yuancan Huang; Shenghe Sun; Jingqing Han

Neural networks based on Lagrange multiplier for nonlinear programming

Yuancan Huang^*
, Shenghe Sun
, Jingqing Han

^*Corresponding author for this work

CAS - Academy of Mathematics and System Sciences

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

A novel class of neural networks for nonlinear programming is presented. The convergence and stability of these neural networks are analyzed, and the implementation of their circuits is discussed. Simulation demonstrates that these neural networks can effectively solve nonlinear programming problems.

Original language	English
Pages (from-to)	24-28
Number of pages	5
Journal	Tien Tzu Hsueh Pao/Acta Electronica Sinica
Volume	26
Issue number	1
Publication status	Published - 1998
Externally published	Yes

Cite this

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title = "Neural networks based on Lagrange multiplier for nonlinear programming",

abstract = "A novel class of neural networks for nonlinear programming is presented. The convergence and stability of these neural networks are analyzed, and the implementation of their circuits is discussed. Simulation demonstrates that these neural networks can effectively solve nonlinear programming problems.",

author = "Yuancan Huang and Shenghe Sun and Jingqing Han",

year = "1998",

language = "English",

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pages = "24--28",

journal = "Tien Tzu Hsueh Pao/Acta Electronica Sinica",

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T1 - Neural networks based on Lagrange multiplier for nonlinear programming

AU - Huang, Yuancan

AU - Sun, Shenghe

AU - Han, Jingqing

PY - 1998

Y1 - 1998

N2 - A novel class of neural networks for nonlinear programming is presented. The convergence and stability of these neural networks are analyzed, and the implementation of their circuits is discussed. Simulation demonstrates that these neural networks can effectively solve nonlinear programming problems.

AB - A novel class of neural networks for nonlinear programming is presented. The convergence and stability of these neural networks are analyzed, and the implementation of their circuits is discussed. Simulation demonstrates that these neural networks can effectively solve nonlinear programming problems.

UR - https://www.scopus.com/pages/publications/0032280818

M3 - Article

AN - SCOPUS:0032280818

SN - 0372-2112

VL - 26

SP - 24

EP - 28

JO - Tien Tzu Hsueh Pao/Acta Electronica Sinica

JF - Tien Tzu Hsueh Pao/Acta Electronica Sinica

IS - 1

ER -

Neural networks based on Lagrange multiplier for nonlinear programming

Abstract

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