PSO and neural networks: Optimal combination to solve non linear complex problems

Mazzei, Maria and Mingo López, Luis Fernando de and Gómez, Nuria and Arteta Albert, Alberto (2011). PSO and neural networks: Optimal combination to solve non linear complex problems. "Computer Research Today", v. 1 (n. 1); pp. 1-27. ISSN 0976-1586.

Description

Title: PSO and neural networks: Optimal combination to solve non linear complex problems
Author/s:
  • Mazzei, Maria
  • Mingo López, Luis Fernando de
  • Gómez, Nuria
  • Arteta Albert, Alberto
Item Type: Article
Título de Revista/Publicación: Computer Research Today
Date: 2011
ISSN: 0976-1586
Volume: 1
Subjects:
Faculty: E.U. de Informática (UPM)
Department: Matemática Aplicada
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.

More information

Item ID: 14182
DC Identifier: http://oa.upm.es/14182/
OAI Identifier: oai:oa.upm.es:14182
Official URL: http://www.mililink.com/issue_content.php?id=65&iId=136
Deposited by: Memoria Investigacion
Deposited on: 08 Feb 2013 11:25
Last Modified: 22 Sep 2014 11:02
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