A PSO with quantum infusion algorithm for training simultaneous recurrent neural networks

Authors:
Bipul Luitel;Ganesh Kumar Venayagamoorthy
Affiliations:
Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science and Technology, Rolla, MO;Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science and Technology, Rolla, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 2
Cited 1

2007 Special Issue: Online design of an echo state network based wide area monitor for a multimachine power system

Neural Networks
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

IEEE Transactions on Evolutionary Computation

Particle swarm optimization with quantum infusion for system identification

Engineering Applications of Artificial Intelligence

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Abstract

Simultaneous Recurrent Neural Network (SRN) is one of the most powerful neural network architectures well suited for estimation and control of complex time varying nonlinear dynamic systems. SRN training is a difficult problem especially if multiple inputs and multiple outputs (MIMO) are involved. Particle swarm optimization with quantum infusion (PSO-QI) is introduced in this paper for training such SRNs. In order to illustrate the capability of the PSO-QI training algorithm, a wide area monitor (WAM) for a power system is developed using a multiple inputs multiple outputs Elman SRN. The SRN estimates speed deviations of four generators in a multimachine power system. Since MIMO structured SRNs are hard to train, a two step approach for training is presented with PSO-QI. The performance of PSO-QI is compared to that of the standard PSO algorithm. Results demonstrate that the SRN trained with the PSO-QI in the two step approach tracks the speed deviations of the generators with the minimum error.