Product form feature evolution forecasting based on IGMBPM model
| Autor: | He Min Yan, Chun Jun Liu, Ming Lang Yang, Wei Dong Liu, Qiu Ying Xu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Markov chain business.industry 0211 other engineering and technologies Computational Mechanics 02 engineering and technology Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Back propagation neural network Computational Mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Volatility (finance) business Raw data Algorithm computer Mutual influence 021106 design practice & management |
| Zdroj: | Computer-Aided Design and Applications. 13:431-439 |
| ISSN: | 1686-4360 |
| DOI: | 10.1080/16864360.2015.1131531 |
| Popis: | In view of the problem of generational product consistent form feature is difficult to be predicted quantitatively, this paper presents a novel approach based on grey theory, Back propagation neural network (BP NN) and Markov chain, which is hereafter called the improved Grey-BP model with Markov chain (IGMBPM model). In the process of forecasting, due that the raw sequence consisted of product form feature points’ positions has the characteristics of poor sample, irregular and high volatility, firstly the traditional grey model is improved to be more suitable for the oscillating raw data, and the improved grey model is combined with BP NN for the purpose of enhancing the mutual influence between anterior and posterior data in sequence, in addition Markov chain is used to amend the final prediction results. The radiator grill profile of a certain type of automobile are taken as an example, the results of the IGMBPM model are compared with other models, the former shows better performance, which ve... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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