A neural network algorithm for solving quadratic programming based on fibonacci method

Authors:
Jingli Yang;Tingsong Du
Affiliations:
Institute of Nonlinear and Complex System, China Three Gorges University, YiChang, Hubei, China;Institute of Nonlinear and Complex System, China Three Gorges University, YiChang, Hubei, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

Citing 5
Cited 0

Parallel and distributed computation: numerical methods

Parallel and distributed computation: numerical methods
A recurrent neural network for solving linear projection equations

Neural Networks
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)

Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
A dual neural network for kinematic control of redundant robotmanipulators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel neural network algorithm is proposed, which solve the quadratic programming problem with linear constraints based on Fibonacci method Compared with the existing models for solving the quadratic programming problem with linear constraints, it is more universal, since the objective function of the quadratic programming not only can be convex function but also can be quasi convex function Finally, example is provided to show the applicability of the proposed neural network algorithm.