A PSO with Quantum Infusion Algorithm for Training

Simultaneous Recurrent eural etwork (SR ) is one of the most powerful neural network architectures well suited for estimation and control of complex time varying nonlinear dynamic systems. SR 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 SR s. 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 SR . The SR estimates speed deviations of four generators in a multimachine power system. Since MIMO structured SR s 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 SR trained with the PSO-QI in the two step approach tracks the speed deviations of the generators with the minimum error.