Implementation of General Regression Neural Network into Long-Term and Middle-Term Demand Forecasting Models--------A Case Study for Computer Components
| Autor: | 邱穎聖 |
|---|---|
| Rok vydání: | 2002 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 90 In this thesis, we implement General Regression Neural Network into a demand model to forecast the demand of long-term and middle-term electronic products. The GRNN is a evolution from Probability Neural Network (PNN) and applied in control and forecasting problems for finding the relationship between continuous variables. One of the merits of GRNN is that GRNN can fit either linear or non-linear regression lines without extra efforts. Conventional statistical methods to forecast demands are unvariate time series, exponential smoothing method and regression analysis. In the research, performance evaluation of GRNN is studied and the comparisons among unvariate time series, exponential smoothing method, regression analysis and a weighted combination of several forecasting models are conducted. Mean absolute percent error (MAPE) and mean absolute deviations (MAD) are two performance indices used in the research. Studies show that both MAPE and MAD of the weighted linear combination models are smaller than any other models except GRNN model. There is no significant difference of MAPE and MAD between GRNN and weighted linear combination models. From time consumption viewpoint, GRNN is much shorter than the weighted linear combination models, thus the proposed GRNN model performs well in demand forecasting. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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