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.
Recommended Citation
S. Mohagheghi et al., "Trainin MLP Neural Networks for Identification of a Small Power System: Comparison of PSO and Backpropagation, Yamille Del Valle," International Conference on Power Systems Operation and Planning, International Conference on Power System Operations and Planning (ICPSOP), May 2005.
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.
Publication Date
01 May 2005
