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Neural-Network-Based Fuzzy Logic Control and Decision System
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Evolving neural network for printed circuit board sales forecasting
Expert Systems with Applications: An International Journal
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Combining SOM and fuzzy rule base for sale forecasting in printed circuit board industry
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Knowledge-Based Systems
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Knowledge-Based Systems
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
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Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Applied Soft Computing
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Expert Systems with Applications: An International Journal
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Environmental Modelling & Software
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Knowledge-Based Systems
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Knowledge-Based Systems
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
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Knowledge-Based Systems
A seasonal discrete grey forecasting model for fashion retailing
Knowledge-Based Systems
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In order to obtain a better control of market trend and profit for the company, timely identification of sales is very important for businesses. Upward and downward trends in sales signify new market trends and understanding of sales trends is important for marketing as well as for customer retention. This research develops a hybrid model by integrating K-mean cluster and fuzzy neural network (KFNN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historical data can be classified into different clusters. The accuracy of the forecasted model can be further improved by referring the new data to be forecasted from a more focused region, i.e., a smaller region after clustering. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the hybrid model for future monthly sales forecasted. The experimental results derived from the proposed model show the effectiveness of the hybrid model when compared with other approaches.