Abstract
The case retrieval model based on neural network is presented to enhance the efficiency and quality of retrieving case in the case-based reasoning system of the motorcycle intelligent design. In the retrieval model, the adaptive resonance theory neural network was used to dynamically cluster the cases in the case base to narrow the searching range. The back propagation neural network was applied to memory the index of cases to retrieve quickly the similar case from the narrowed case base. Thus the efficiency and quality of retrieving case are improved. Finally, an example of the plan selection of motorcycle general design was given. Its result was contrasted with that of case retrieval based on the nearest neighbor method to demonstrate the effectives of the case retrieval model. The research shows that it is practicable and effective using the adaptive resonance theory and BP neural network to modeling the reasoning mechanism.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Watson, I.: Case-based Reasoning Is a Methodology not a Technology. Knowledge-Based Systems 12, 303–308 (1999)
Lee, S., Ryu, J.H., Won, J.S.: Determination and Application of the Weights for Landslide Susceptibility Mapping Using an Artificial Neural Network. Engineering Geology 71, 289–302 (2004)
Kim, K.S., Han, I.: The Cluster-indexing Method for Case-based Reasoning Using Self-organizing Maps and Learning Vector Quantization for Bond Rating Cases. Expert Systems with Applications 21, 147–156 (2001)
Fung, W.K., Liu, Y.H.: Adaptive Categorization of ART Networks in Robot Behavior Learning Using Game-theoretic Formulation. Neural Networks 16, 1403–1420 (2003)
Yang, B.S., Han, T., An, J.L.: ART-KOHONEN Neural Network for Fault Diagnosis of Rotating Machinery. Mechanical Systems and Signal Processing 18, 645–657 (2004)
Wang, X., Wang, H., Wang, W.H.: Artificial Neural Network Principle and Applications. North and East University Press, Shenyang (2000)
Automobile Engineering Manual (Motorcycle Parts). People’s Communication Press, Beijing (2001)
Wang, H., Wang, H., Zhou, X.H.: Use Artificial Neural Network to Facilitate Case-Retrieval Course in Injection Mould Case-based Reasoning Decision-making System. Mechanical Science and Technology 22, 474–479 (2003)
Li, H.G., Gao, G.A.: A Hybrid Intelligent CAPP System Based on Neural Indexing Case and Knowledge. Journal of Computer Aided Design and Computer Graphics 12, 317–320 (2000)
Yuan, F.C., Chiu, C.C.: A Hierarchical Design of Case-based Reasoning in the Balanced Scorecard Application. Expert Systems with Applications 36, 333–342 (2009)
Li, H., Sun, J., Sun, B.L.: Financial Distress Prediction Based on OR-CBR in the Principle of K-nearest Neighbors. Expert Systems with Applications 36, 643–659 (2009)
Li, H., Sun, J.: Ranking-order Case-based Reasoning for Financial Distress Prediction. Knowledge-Based Systems 21, 868–878 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ma, F., He, Y., Li, S., Chen, Y., Liang, S. (2009). Research on Case Retrieval of Case-Based Reasoning of Motorcycle Intelligent Design. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_81
Download citation
DOI: https://doi.org/10.1007/978-3-642-01216-7_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
Online ISBN: 978-3-642-01216-7
eBook Packages: EngineeringEngineering (R0)