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A Family Cars- Life Cycle Cost (LCC)-Oriented Hybrid Modelling Approach Combining ANN and CBR
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chen, Xiaochuan Yang, Jianguo Li, Beizhi |
| Copyright Year | 2011 |
| Abstract | for cost (DFC) is a method that reduces life cycle cost (LCC) from the angle of designers. Multiple domain features mapping (MDFM) methodology was given in DFC. Using MDFM, we can use design features to estimate the LCC. From the angle of DFC, the design features of family cars were obtained, such as all dimensions, engine power and emission volume. At the conceptual design stage, cars' LCC were estimated using back propagation (BP) artificial neural networks (ANN) method and case-based reasoning (CBR). Hamming space was used to measure the similarity among cases in CBR method. Levenberg-Marquardt (LM) algorithm and genetic algorithm (GA) were used in ANN. The differences of LCC estimation model between CBR and artificial neural networks (ANN) were provided. ANN and CBR separately each method has its shortcomings. By combining ANN and CBR improved results accuracy was obtained. Firstly, using ANN selected some design features that affect LCC. Then using LCC estimation results of ANN could raise the accuracy of LCC estimation in CBR method. Thirdly, using ANN estimate LCC errors and correct errors in CBR's estimation results if the accuracy is not enough accurate. Finally, economically family cars and sport utility vehicle (SUV) was given as LCC estimation cases using this hybrid approach combining ANN and CBR. |
| Starting Page | 1463 |
| Ending Page | 1471 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| Volume Number | 5 |
| Alternate Webpage(s) | http://waset.org/publications/13473/a-family-cars-life-cycle-cost-lcc-oriented-hybrid-modelling-approach-combining-ann-and-cbr |
| Alternate Webpage(s) | http://www.waset.org/journals/waset/v55/v55-164.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
