A neural network approach for global optimization with applications

Title:

A neural network approach for global optimization with applications

Creator:

Li, Leong - Kwan and Shao, S.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:e2ffcda8-accc-46ba-9d3c-a259f3053646
uuid:e2ffcda8-accc-46ba-9d3c-a259f3053646

Subject:

Global optimization, nonlinear least square problem, and state space search algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

We propose a neural network approach for global optimization with applications to nonlinear least square problems. The center idea is defined by the algorithm that is developed from neural network learning. By searching in the neighborhood of the target trajectory in the state space, the algorithm provides the best feasible solution to the optimization problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. Our examples show that the method is effective and accurate. The simplicity of this new approach would provide a good alternative in addition to statistics methods for power regression models with large data.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:5

Harvested from:

CDK

Metadata only:

false