Particles Gradient: A New Approach to Perform MLP Neural Networks Training based on Particles Swarm Optimization

César Daltoé Berci; Celso Pascoli Bottura

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Particles Gradient: A New Approach to Perform MLP Neural Networks Training based on Particles Swarm Optimization

In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 0ICINCO, 115-123, 2009 , Milan, Italy

Authors: César Daltoé Berci and Celso Pascoli Bottura

Affiliation: Universidade Estadual de Campinas, Brazil

Keyword(s): Particle Swarm Optimization, Gradient Descent, Neural Networks Training. Abstract:

Abstract: The use of heuristic algorithms in neural networks training is not a new subject. Several works have already accomplished good results, however not competitive with procedural methods for problems where the gradient of the error is well defined. The present document proposes an alternative for neural networks training using PSO(Particle Swarm Optimization) to evolve the training process itself and not to evolve directly the network parameters as usually. This way we get quite superior results and obtain a method clearly faster than others known methods for training neural networks using heuristic algorithms.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 18.191.211.66

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Berci, C. and Bottura, C. (2009). Particles Gradient: A New Approach to Perform MLP Neural Networks Training based on Particles Swarm Optimization. In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP; ISBN 978-989-674-002-3, SciTePress, pages 115-123. DOI: 10.5220/0002214501150123

@conference{workshop anniip09,
author={César Daltoé Berci. and Celso Pascoli Bottura.},
title={Particles Gradient: A New Approach to Perform MLP Neural Networks Training based on Particles Swarm Optimization},
booktitle={Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP},
year={2009},
pages={115-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002214501150123},
isbn={978-989-674-002-3},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2009) - Workshop ANNIIP
TI - Particles Gradient: A New Approach to Perform MLP Neural Networks Training based on Particles Swarm Optimization
SN - 978-989-674-002-3
AU - Berci, C.
AU - Bottura, C.
PY - 2009
SP - 115
EP - 123
DO - 10.5220/0002214501150123
PB - SciTePress