Application of Radial Basis Function Neural Network for Sales Forecasting

This paper proposes a hybrid evolutionary algorithm based radial basis function neural network (RBFnn) for sales forecasting. The proposed hybrid of particle swarm and genetic algorithm based optimization (HPSGO) algorithm gathers virtues of particle swarm optimization (PSO) and genetic algorithm (GA) to improve the learning performance of RBFnn. The diversity of chromosomes results in higher chance to search in the direction of global minimum instead of being confined to local minimum. Experimental results of papaya milk sales data show that the proposed HPSGO algorithm outperforms PSO, GA and Box-Jenkins model in accuracy.