EVOLUTIONARY ALGORITHM-BASED RADIAL BASIS FUNCTION NEURAL NETWORK TRAINING FOR INDUSTRIAL PERSONAL COMPUTER SALES FORECASTING

摘要

Forecasting is one of the crucial factors in applications because it ensures the effective allocation of capacity and proper amount of inventory. Because Box-Jenkins models using linear forecasting have their constraint to predict complexity in the real world, other nonlinear approaches are developed to conquer the challenge of nonlinear forecasting. With the same goal, we are proposing a hybrid of genetic algorithm and artificial immune system (HGAI) algorithm with radial basis function neural network learning for function approximation and further applying it to conduct an industrial personal computer sales forecasting exercise. In addition, five well-known benchmark problems were used to evaluate the results in the experiment, and the newly proposed HGAI algorithm has returned better results than the Box-Jenkins models and other algorithms.