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A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 3، دوره 1، شماره 1، تیر 2004، صفحه 5-15 اصل مقاله (330.16 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2004.489 | ||
| نویسندگان | ||
| R. A. ALIEV* 1؛ B. G. GUIRIMOV2؛ R. R. ALIEV3 | ||
| 1MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL ACADEMY, BAKU, AZERBAIJAN | ||
| 2DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL ACADEMY, BAKU, AZERBAIJAN | ||
| 3EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS | ||
| چکیده | ||
| The paper analyses issues leading to errors in graphic object classifiers. The distance measures suggested in literature and used as a basis in traditional, fuzzy, and Neuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized or fuzzy objects in which the features of classes are much more difficult to recognize because of significant uncertainties in their location and gray-levels. The authors suggest a neurofuzzy graphic object classifier with modified distance measure that gives better performance indices than systems based on traditional ordinary and cumulative distance measures. Simulation has shown that the quality of recognition significantly improves when using the suggested method. | ||
| کلیدواژه‌ها | ||
| Neuro-Fuzzy technology؛ Fuzzy logic؛ IF-THEN rules؛ Neural Network | ||
| مراجع | ||
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[1] J. Bezdek and S. Pal (ed.), Fuzzy models for pattern recognition, New York: IEEE Press (1992). [2] X. Ye, C.Suen and M.Cheriet, A generic system to extract and clean handwritten data from business forms, in Prof. Int. Workshop on Frontiers in handwriting Recognition, Amsterdam (2000) 63-72. [3] R. A. Aliev and R. R. Aliev, Soft Computing and its applications, World Scientific Publishing Co. Pte. Ltd (2001) p. 444. [4] K. Saastamoinen, V. Könönen, and P. Luukka, A classifier based on the fuzzy similarity in the Lukasiewicz structure with different metrics, in proceedings of IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’02, Vol. 1 (2002) 363-367. [5] H. Bandemer and W. Näther, Fuzzy data analysis, Theory and Decision Library, Series B: Mathematical and Statistical Methods, Vol. 20, Cluwer Academic Publishers (1992) 67-71. [6] L. Mascarilla and C. Frélicot, Combining rejection-based pattern classifiers, in 19th International Conference of the North American Fuzzy Information Processing Society – NAFIPS, PeachFuzz 2000 (2000) 114-118. [7] R Aliev and B. Guirimov, Handwritten image recognition by using neural and fuzzy approaches, Intelligent Control and Decision Making Systems, No. 1, Thematic Collected Articles, Baku, Publishing House of Azerb. State Oil Academy (1997) 3-7. [8] R. Aliev, B. Guirimov, K. Bonfig, and Steinmann, A neuro-fuzzy algorithm for recognition of nonstylized images, in proceedings of Fourth International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS’2000, Siegen, Germany (2000) 238-241. [9] S. Halgamuge and M. Glesner, A fuzzy neural approach for pattern classification with generation of rules based on supervised learning, in proceedings of Nuro Nimes 92 (1992) 165-173. [10] V. Uebele, S. Abe, and M. Lan, A neural - network based fuzzy classifier, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3 (1995) 353-361. [11] R. Yager, A general approach to rule aggregation in fuzzy logic control, Appl. Intelligence, 2 (1992) 333-351. [12] R. Yager, On a general class of fuzzy connectives, Fuzzy Sets and Systems, 4 (1980) 235-242. | ||
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