The hybrid of artificial neural networks and case-based reasoning for intelligent diagnosis system 


Vol. 15,  No. 1, pp. 45-52, Feb.  2008
10.3745/KIPSTB.2008.15.1.45


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  Abstract

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

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[IEEE Style]

G. J. Lee, C. J. Kim, B. R. Ahn, M. K. Kim, "The hybrid of artificial neural networks and case-based reasoning for intelligent diagnosis system," The KIPS Transactions:PartB , vol. 15, no. 1, pp. 45-52, 2008. DOI: 10.3745/KIPSTB.2008.15.1.45.

[ACM Style]

Gil Jae Lee, Chang Joo Kim, Byung Ryul Ahn, and Moon Kyun Kim. 2008. The hybrid of artificial neural networks and case-based reasoning for intelligent diagnosis system. The KIPS Transactions:PartB , 15, 1, (2008), 45-52. DOI: 10.3745/KIPSTB.2008.15.1.45.

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