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.

Meeting Name

IEEE Swarm Intelligence Symposium, 2005. SIS 2005


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





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2005