Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2005.12D.6.869

Construction of MATLAB API for Fuzzy Expert System Determining Automobile Warranty Coverage  

Lee, Sang-Hyoun (전남대학교 전산학과)
Kim, Chul-Min (호남대학교 인터넷소프트웨어학과)
Kim, Byung-Ki (전남대학교 전자컴퓨터정보통신공학부)
Abstract
In the recent years there has been an increase of service competition in the activity of product selling, especially in the extension of warranty coverage and qualify. The variables in connection with the service competition are not crisp, and required the expertise of the production line. It thus becomes all the more necessary to use subtler tools as decision supports. These problems are typical not only of product companies but also of financial organizations, credit institutions, insurance, which need predictions of credibility for firms or persons in which they have any kind of interest. A suitable approach for minimizing the risk is to use a knowledge-based system. Most often expert systems are not standalone programs, but are embedded into a larger application. The aim of this paper is to discuss an approach for developing an embedded fuzzy expert system with respect to the product selling policy, especially to present the decision system of automobile selling activity around the extension of warranty coverage and quality. We use the MATLAB tools which integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Also, we present the API functions embedding into the existing application.
Keywords
MATLAB; API; Embedded; Fuzzy Expert Systems; Warranty Coverage; Quality;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Zimmermann H. J (1997). 'Operators in models of decision making', In 'Fuzzy Information Engineering' pp.471-496
2 Zadeh, L.A. (1965). 'Fuzzy sets' Information and Control, 8, pp.338- 353   DOI
3 Zimmermann, H. J. Zysno P.(1980). 'Latent Connectives in Human Decision Making', Fuzzy Sets and Systems, 4, pp.37-51   DOI   ScienceOn
4 Zimmermann H. J. (1996). 'Fuzzy Sets Theory and its Applications.' 3rd revised edition, Kluver Academic Publisher, Boston and Dordrecht
5 Simon, D. (2004). 'H-infinity estimation for fuzzy membership function optimization,' submitted for publication
6 Simon, D. (2002). 'Training fuzzy systems with the extended Kalman filter,' Fuzzy Sets and Systems, Vol. 132, pp. 189-199   DOI   ScienceOn
7 Messier, W.F.: Hansen, J.V, (1988). 'Including rules for expert systems development: An example using default and bankruptcy data'. Management Sciences 34, 12, pp. 1403-1415   DOI   ScienceOn
8 Ohlson, J.A. (1980) 'Financial ratios and probabilistic prediction of bankruptcy' Journal of Accounting Research, Spring, pp. 109-131   DOI   ScienceOn
9 C. von Altrock (1997). 'Fuzzy Logic and neurofuzzy applications in business and finance.' (Prentice Hall)
10 Kasabov, N.K.(1996) 'Foundation of Neural Networks, Fuzzy Systems, and Knoledge Engineering' (MIT Press)
11 Malecot. J.F, (1981). 'Limites des modes de precision de deaillances' Finance 2,4, pag. 291-315
12 Altman, E.I.-Marco, G:- Varetto, F. (1994). 'Corporate distress diagnosis: Comparison using discriminant analysis and neural network (the italian experience)'. Journal of Banking and Finance 18, pp.505-529   DOI   ScienceOn
13 G.Bojadziev, M.Bojadziev (1997). 'Fuzzy Logic for Business, Finance and Management', (World Scientific Publishing co, Singapore), 1997
14 Eisembeis, R.A. (1977). 'Pitfalls in the application of discriminant analysis in business and economics'. The Journal of Finance 32, pp.875-900   DOI
15 Altman, E.I.(I968). 'Financial ratios, discriminant analysis and the prediction of corporate bankruptcy' The Journal of Finance 23, pp.589-609   DOI