Title

Trainin MLP Neural Networks for Identification of a Small Power System: Comparison of PSO and Backpropagation, Yamille Del Valle

Abstract

Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a Multilayer Perceptron Neural Network (MLPN) is compared with that of Particle Swarm Optimization, for neural network based identification of a small power system with a Static Compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Backpropagation; Multilayer Perceptron Neural Network; Neuroidentifier; Particle Swarm Optimization; Power System Identification

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2005 International Conference on Power System Operations and Planning (ICPSOP), All rights reserved.


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