A new fuzzy neural networks model for demand forecasting
Yafeng Yin, Yue Liu, Gao Junjun, Tan Chongli · 2008
Demand forecasting is the basis of business operation in a company and the forecasting accuracy has a great effect on safety inventory, profit and competitive power of the company. In this paper, a novel genetic algorithm (GA) and back propagation (BP) algorithm based fuzzy neural network (GABPFNN) model is proposed for demand forecasting, in which new kinds of fuzzy rule generating and matching algorithms are advanced to deal with the difficulty of fuzzy neural network modeling, then GA and BP are employed to optimize the network. Finally, the model is applied for the demand forecasting of beer retail industry. The final experiment result proves the efficiency of the model.