Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » Malaysian Journals
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top Journal Cited
 » Top Article Cited
 » Journal Citation Statistics
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Adjustment of Membership Functions, Generation and Reduction of Fuzzy Rule Base From Numerical Data

Ketata, Raouf, and Bellaaj, Hatem, and Mohamed Chtourou, and Amer, Mohamed Ben, (2007) Adjustment of Membership Functions, Generation and Reduction of Fuzzy Rule Base From Numerical Data. Malaysian Journal of Computer Science, 20 (2). ISSN 0127-9084

Full text not available from this repository.

Official URL: http://ejum.fsktm.um.edu.my/ArticleInformation.aspx?ArticleID=551

Affiliations

National Institute of Applied Sciences and Technologies of Tunis, Tunisia
National School of Engineers of Sfax. Research unit on Intelligent Control, design and Optimisation of Complex System
National School of Engineers of Sfax. Research unit on Intelligent Control, design and Optimisation of Complex System
Habib Bourguiba Hospital of Sfax, TUNISIA

Abstract

In this paper we introduce a new approach for adjustment of membership functions, generation, and reduction of fuzzy rule base from data in the same time. The proposed approach consists of five steps: First, generate fuzzy rules from data using Mendel & Wang Method introduced in [1]. Second, calculate the degree of similarity between rules. Third, measure the distance between the numerical values which induces similar rules. Four, if the distance is greater than base value then merge membership functions. Finally, regenerate rules from data with new fuzzy sets. This approach is applied to truck backer-upper control and Liver trauma diagnostic. A comparative study with a simple Mendel Wang method shows the advantages of the developed approach.

Item Type:Journal
Keywords:fuzzy inference system, rule base generation and reduction, similarity, numerical data
Subjects:Q Science
T Technology
ID Code:1613

[1] L.-X.Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples”, IEEE Trans. Syst., Man, Cybern., vol. 22, Nov./Dec. 1992, pp. 1414–1427.

[2] C. W. Tao, “A Reduction Approach for Fuzzy Rule Bases of Fuzzy Controllers”, IEEE Trans. Syst., Man, Cybern., Part B: vol. 32, no. 5, October 2002.

[3] M. Sugeno and G. T. Kang, “Structure identification of fuzzy model”, Fuzzy Sets Syst. vol. 28, 1988, pp. 15–33.

[4] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. J. Man-Mach. Stud., vol. 7, 1975, pp. 1–13.

[5] S.Guillaume & B.Charnomordic, “Generating an Interpretable Family of Fuzzy Partitions From Data”, IEEE Trans on Fuzzy Syst, vol. 12, no. 3, June 2004.

[6] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Trans. Syst., Man, Cybern., vol. SMC-15, Jan. 1985, pp. 116–132.

[7] R. M. Tong, “The evaluation of fuzzy model derived from experimental data”, Fuzzy Sets Syst., vol. 4, 1980, pp. 1–12.

[8] Jan Nissl, “Pulse Measurement”, Web MD Health - June 29, 2004.

[9] S.W.Meng Joo & Y.Gao, “A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks”, IEEE Trans on Fuzzy Syst, vol.. 9, no. 4, August 2001.

[10] A. Homaifar and E. McCormick, “Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms”, IEEE Trans. Fuzzy Syst., vol. 3, 1995, pp. 129–139.

[11] A. H. Tan, “Cascade ARTMAP: Integrating neural computation and symbolic knowledge processing”, IEEE Trans. Neural Networks, vol. 8, 1997, pp. 237–250.

[12] Ali Nawaz Khan, “Liver trauma”, emedecine.com. Inc - 03/01/2003 http://www.emedicine.com/radio/topic397.htm

[13] C. L. Karr and E. J. Gentry, “Fuzzy control of pH using genetic algorithms”, IEEE Trans. Fuzzy Syst., vol. 1, 1993, pp. 46–53.

[14] C. P. Pappis and N. I. Karacapilidis, “A comparative assessment of measures of fuzzy values”, Fuzzy Sets Syst., vol. 56, no. 2, 1993, pp. 171–174.

[15] D. Chakraborty and N. R. Pal, “A Neuro-Fuzzy Scheme for Simultaneous Feature Selection and Fuzzy Rule-Based Classification”, Neural Networks, IEEE Trans on Vol. 15, Issue: 1, Year: Jan. 2004.

[16] D. Park, A. Kandel, and G. Langholz, “Genetic-based new fuzzy reasoning models with application to fuzzy control”, IEEE Trans. Syst. Man, Cybern., vol. 24, Jan. 1994, pp. 39–47.

[17] D.Tikk and P.Baranyi, “Comprehensive Analysis of a New Fuzzy Rule Interpolation Method”, IEEE Trans on Fuzzy Syst, vol. 8, no. 3, June 2000.

[18] D.Spiegel and T.Sudkamp, “Employing Locality in the Evolutionary Generation of Fuzzy Rule Bases”, IEEE Trans. Syst., Man, Cybern, Part B, vol. 32, no. 3, June 2002

[19] D. S. Yeung and E. C. C. Tsang, “Improved fuzzy knowledge representation and rule evaluation using fuzzy Petri nets and degree of subsethood”, Intell. Syst., vol. 9, no. 12, 1994, pp. 1083–1100.

[20] D. S. Yeung and E. C. C. Tsang: “A Comparative Study on Similarity-Based Fuzzy Reasoning Methods”, IEEE Trans. Syst., Man, Cybern., Part B, vol. 27, NO. 2, April 1997.

[21] Gy. Vass, L. Kalmár, and L. T. Kóczy, “Extension of the fuzzy rule interpolation method”, in Proc. Int. Conf. Fuzzy Sets Theory Applications, Liptovský, Czech Republic, Jan. 1992.

[22] H. Lee-Kwang, Y. S. Song, and K. M. Lee, “Similarity measure between fuzzy sets and between elements”, Fuzzy Sets Syst., vol. 62, no. 3, 1994, pp. 291–293.

[23] I. B. Turksen and Z. Zhong, “An approximate analogical reasoning scheme based on similarity measures and interval valued fuzzy sets”, Fuzzy Sets Syst., vol. 34, 1990, pp. 323–346.

[24] I.Rojas, H.Pomares, J.Ortega, and A.Prieto, “Self-Organized Fuzzy System Generation from Training Examples”, IEEE Trans on Fuzzy Syst, vol. 8, no. 1, February 2000.

[25] R. Setiono and H. Liu, “NeuroLinear: From neural networks to oblique decision rules”, Neurocomputation, vol. 17, 1997, pp. 1–24.

[26] R. Zwick, E. Carlstein, and D. Budescu, “Measures of similarity among fuzzy sets: A comparative analysis”, Int. J. Approx. Reas., vol. 1, 1987, pp. 221–242.

[27] Shands HealthCare, “AST”, http://www.shands.org/health/information/003472.htm. Copyright 2004 A.D.A.M.

[28] S. H. Chen, “New methods for subjective mental workload assessment and fuzzy risk analysis”, Cybern. Syst.: Int. J., vol. 27, no. 5, 1996, pp. 449–472.

[29] S. H. Chen, “Operations on fuzzy numbers with function principal”, Tamkang J. Manag. Sci., vol. 6, no. 1, 1985, pp. 13–25.

[30] S. H. Chen , “Ranking generalized fuzzy number with graded mean integration”, in Proc. 8th Int. Fuzzy Syst. Assoc. World Congr., vol. 2, Taipei, Taiwan, Republic of China, 1999, pp. 899–902.

[31] S.J.Chen and S.M.Chen, “Fuzzy Risk Analysis Based on Similarity Measures of Generalized Fuzzy Numbers”, IEEE Trans on Fuzzy Syst, vol. 11, no. 1, February 2003.

[32] S. M. Chen, “A weighted fuzzy reasoning algorithm for medical diagnosis”, Decision Support Syst., vol. 11, no. 1, 1994, pp. 37–43.

[33] S. M. Chen, J. S. Ke, and J. F. Chang, “Techniques for handling multicriteria fuzzy decision-making problems”, in Proc. 4th Int. Symp Computer and Information Sciences, Cesme, Turkey, 1989, pp. 919–925.

[34] S. M. Chen, M. S. Yeh, and P. Y. Hsiao, “A comparison of similarity measures of fuzzy values”, Fuzzy Sets Syst., vol. 72, no. 1, 1995, pp. 79–89.

[35] S.M. Chen, “Similarity Measures Between Vague Sets and Between Elements”, IEEE Trans. Syst., Man, Cybern., Part B, vol. 27, no. 1, February 1997.

[36] S.H. Estourgie, C.V.D. Werken, L.P.H. Leenen, “The Efficacy of Gauze Packing in Liver Trauma: Evaluation of the Management and Treatment of Liver Trauma”, European Journal of Trauma - Eur J Trauma 2002

[37] T. Koczy and K.Hirota, “Size Reduction by Interpolation in Fuzzy Rule Bases”, IEEE SMC, Part B: Cyber, Vol. 27, no. 102, 1997.

[38] T. Kóczy, K. Hirota, and T. D. Gedeon, “Fuzzy rule interpolation by the conservation of relative fuzziness”, Hirota Lab., Dept. Intell.Comput. Syst. Sci., Tokyo Inst. Technol., Yokohama, Japan, Tech. Rep. 97/2, 1997.

[39] Y. Jin, W. von Seelen, and B. Sendhoff, “On generating flexible, complete, consistent and compact fuzzy rule systems from data using evolution strategies”, IEEE Trans. Syst., Man, Cybern.—Part B, vol. 29, Dec. 1999, pp.829–845.

[40] Y.Jin, “Fuzzy Modeling of High-Dimensional Systems: Complexity Reduction and Interpretability Improvement”, IEEE Trans on Fuzzy Syst, vol. 8, no. 2, April 2000.

[41] W. Duch, R. Adamczak, and K. Grabczewski, “Extraction of logical rules from neural networks, Neural Process”, Lett., vol. 7,1998, pp. 211–219.

[42] L.Chen and N.Tokuda , “An Efficient Algorithm for Automatically Generating Multivariable Fuzzy Systems by Fourier Series Method”, IEEE Trans. Syst., Man, Cybern., Part B, vol. 32, no. 5, October 2002.

[43] L.Muresan, “Interpolation in Hierarchical Fuzzy Rule Bases”, Dept. of Autovehicles and Dept. of Telecom. & Telematics, Technical University of Budapest, Sztoczek u. 6, 1111 Budapest, Hungary 03/2001.

[44] J. Yen & L.Wang, “Simplifying fuzzy rule-based models using orthogonal transformation methods”, IEEE Trans. Syst., Man, Cybern., Part B, vol.29, Feb. 1999, pp. 13–24.

[45] M.Setnes & R.Babuska , “Rule Base Reduction: Some Comments on the Use of Orthogonal Transforms”, IEEE Trans. Syst., Man, Cybern., Part C, Applications and Reviews, vol. 31, no. 2, May 2001.

[46] M.Y.Chen and D.A.Linkens, “Interpretable Neural-Fuzzy Models for Mechanical Property Prediction of Microalloyed Steels”, Dept. of Automatic Control and Systems Engineering University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK.

[47] M.Setnes, R.Babu¡ska, U.Kaymak, and H.R.Nauta Lemke, “Similarity Measures in Fuzzy Rule Base Simplification”, IEEE Trans. Syst., Man, Cybern., Part B, vol. 28, NO. 3, JUNE 1998.

[48] L. A. Zadeh, “Fuzzy sets”, Inform. Contr., vol. 8, 1965, pp. 338–356.

[49] M. Jamei, M. Mahfouf., D.A. Linkens, “Elicitation and !ne-tuning of fuzzy control rules using symbiotic evolution”, Fuzzy Sets and Systems, 2004, pp. 57–74.

[50] L.-X.Wang et J. M. Mendel, “Generating fuzzy rules from numerical data, with applications”, USC SIPI Rep N°169 Univ Southern Calif Los Angeles, 1991.

[51] Joseph Castillo, “liver”, http://mripractice.tripod.com/mrpractice/id59.html

[52] N.Ghariani, M.Ben Amer, “Le traitement non opératoire des traumatismes fermées du foie”, Thèse de doctorat en médecine - Faculté de médecine de Sfax – Année 2002 / 2003

[53] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Trans. Systems, Man and Cybernetics 15 (1, 1985), pp: 116 - 132.

Repository Staff Only: item control page
 
 
 
 
 
 
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.