کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5081515 | 1477604 | 2010 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
In the evaluation of supply chain process improvements, the question of how to predict product demand quantity and prepare material flows in order to reduce cycle time has emerged as an important issue, especially in the 3C (computer, communication, and consumer electronic) market. This paper constructs a predicting model to deal with the product demand forecast problem with the aid of a growing hierarchical self-organizing maps and independent component analysis. Independent component analysis method is used to detect and remove the noise of data and further improve the performance of predicting model, then growing hierarchical self-organizing maps is used to classify the data, and after the classification, support vector regression is applied to construct the product demand forecasting model. In the experimental results, the model proposed in this paper can be successfully applied in the forecasting problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Production Economics - Volume 128, Issue 2, December 2010, Pages 603-613
Journal: International Journal of Production Economics - Volume 128, Issue 2, December 2010, Pages 603-613
نویسندگان
Chi-Jie Lu, Yen-Wen Wang,