Neural networks are used in a wide number of fields including signal and image processing, modeling and control and pattern recognition. Some of the most common type of neural networks is the multilayer perceptrons and the recurrent neural networks. Most of these networks consist of large number of neurons and hidden layers, which results in a longer training time. A Generalized Neuron (GN) has a compact structure and overcomes the problem of long training time. Due to its simple structure and lesser memory requirements, the GN is attractive for hardware implementations. This paper presents the online training of a GN with the Particle Swarm Optimization (PSO) algorithm. A comparative study of the GN and the MLP online trained with PSO is presented for function approximations. The GN based identification of the Static VAR Compensator (SVC) dynamics in a 12 bus FACTS benchmark power system trained online with the PSO is also presented.
R. Kiran et al., "Online Training of a Generalized Neuron with Particle Swarm Optimization," Proceedings of the IEEE International Joint Conference on Neural Network, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/IJCNN.2006.247237
IEEE International Joint Conference on Neural Network, 2006
Electrical and Computer Engineering
Article - Conference proceedings
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.