Apply Fuzzy Neural Network to Combined Forecasts

• Main Authors: CHIH-CHIANG HUANG, 黃志強
• Other Authors: Fu-Kwun Wang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/05824304000518221804

碩士 === 國立臺灣科技大學 === 工業管理系 === 96 === Demand planning in many industries exists uncertainty. To reduce the costs and increase benefits, the accuracy of demand forecasting becomes an important task. This research investigates the policy of demand planning through many kinds of forecasting methods, it will improve the performance in production schedule and productivity supply and reduce bullwhip effect. Combined forecasts method is to combine different forecasting methods. Many experts point out that combined forecast is more useful than any individual forecasting methods in prediction performance. In addition, nonlinear combined forecast is better than linear combined forecast. We use 11 groups of ATM cash demand at random within the territory of England as the target of prediction, by combining two individual forecasting methods’ predicted value to reach stability and accuracy of carrying on the demand while planning for this industry and prove that the nonlinear combined method is more apparent on the result that is predicted. To estimate the parameters of linear combined forecasts we use adaptive set of weights, k method and linear composite for nonlinear combined forecasts, we use the adaptive fuzzy neural networks to train and study and the results show that this method provides the most suitable weights for combine forecasts.