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

### César Daltoé Berci, Celso Pascoli Bottura

#### 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.

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#### Paper Citation

#### in Harvard Style

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 - Volume 1: Workshop ANNIIP, (ICINCO 2009)* ISBN 978-989-674-002-3, pages 115-123. DOI: 10.5220/0002214501150123

#### in Bibtex Style

@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 - Volume 1: Workshop ANNIIP, (ICINCO 2009)},

year={2009},

pages={115-123},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0002214501150123},

isbn={978-989-674-002-3},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2009)

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