Improving neural networks prediction accuracy using particle swarm optimization combiner.
This paper proposes a technique to improve Artificial Neural Network (ANN) prediction accuracy using Particle Swarm Optimization (PSO) combiner. A hybrid system consists of two stages with the first stage containing two ANNs. The first ANN predictor is a multi-layer feed-forward network trained with error back-propagation and the second predictor is a functional link network. These two predictors are combined in the second stage using PSO combiner in a linear and non-linear fashion. The proposed method is applied to problem of predicting daily natural gas consumption. The performance of ANN predictors and combination methods are tested on real data from four different gas utilities. The experimental results show that the proposed particle swarm optimization combiners results in more accurate prediction compared to using single ANN predictor. Prediction accuracy improvement of the proposed PSO combiners have been shown using hypothesis testing.
Elragal
Hassan M
HM
Electrical Engineering Department, University of Bahrain, Bahrain. helragal@eng.uob.bh
D000465:Algorithms; D003198:Computer Simulation; D005544:Forecasting / Q000379:methods*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods