Immunological Algorithm-based Neural Network Learning for Sales Forecasting

Abstract

This study proposes a variation immunological system (VIS) algorithm with radial basis function neural network (RBFN) learning for function approximation and the exercise of industrial computer (IC) sales forecasting. The proposed VIS algorithm was applied to the RBFN to execute the learning process for adjusting the network parameters involved. To compare the performance of relevant algorithms, three benchmark problems were used to justify the results of the experiment. With better accuracy in forecasting, the trained RBFN can be practically utilized in the IC sales forecasting exercise to make predictions and could enhance business profit.