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 radial basis function neural network (RBFN) 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., "A Comparison of PSO and Backpropagation for Training RBF Neural Networks for Identification of a Power System with STATCOM," Proceedings of the IEEE Swarm Intelligence Symposium, 2005. SIS 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/SIS.2005.1501646
Meeting Name
IEEE Swarm Intelligence Symposium, 2005. SIS 2005
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
PSO; RBF Neural Network Training; STATCOM; Static Compensator; Backpropagation; Backpropagation Algorithm; Particle Swarm Optimization; Power System Control; Power System Identification; Radial Basis Function Networks; Radial Basis Function Neural Network
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2005
