The development of a weighted evolving fuzzy neural network for PCB sales forecasting

Abstract This research develops a weighted evolving fuzzy neural network for PCB sales forecasting and it includes four major steps: first of all, collecting 15 factors among macroeconomic data, downstream production demand and total industrial production outputs and then using the Grey Relation Analysis (GRA) to select a combination of key factors which have the greatest influence on PCB sales. Secondly, taking the time serial factor into consideration, the Winter’s Exponential Smoothing method is applied to predict the tendency of PCB sales and the influences of seasonal effects. Thirdly, applying historical data to proceed the training of Weighted Evolving Fuzzy Neural Network (WEFuNN) and then forecasts the future PCB sales by the WEFuNN. Finally, compare three kinds of performance measurements for each model. The experimental results reveal that the MAPE for WEFuNN model is 2.11% which is the best compared to others. In summary, the WEFuNN model can be applied practically as a sales forecasting tool in the PCB industry.

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