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A PSO with quantum infusion algorithm for training Simultaneous Recurrent Neural Networks

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2 Author(s)
Luitel, B. ; Real-Time Power & Intell. Syst. Lab., Missouri Univ. of Sci. & Technol., Rolla, MO, USA ; Venayagamoorthy, G.K.

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.

Published in:

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Date of Conference:

14-19 June 2009