Combining Rough Sets and Neural Network Approaches in Pattern Recognition

• Autorzy: Cyran K.
• Języki publikacji: EN

Abstrakty

EN

The paper focuses on problems which arise when two different types of AI methods are combined in one design. The first type is rule based, rough set methodology operating is highly discretized attribute space. The discretization is a consequence of the granular nature of knowledge representation in the theory of rough sets. The second type is neural network working in continuous space. Problems of combining these different types of knowledge processing are illustrated in a system used for recognition of diffraction patterns. The feature extraction is performed with the use of holographic ring wedge detector, generating the continuous feature space. No doubt, this is a feature space natural for application of the neural network. However, the criterion of optimization of the feature extractor uses rough set based knowledge representation. This latter, requires the discretization of conditional attributes generating the feature space. The novel enhanced method of optimization of holographic ring wedge detector is proposed, as a result of modification of indiscernibility relation in the theory of rough sets.

Słowa kluczowe

EN

pattern recognition; neural networks; rough sets; hybrid methods; evolutionary optimization; holographic ring-wedge detector

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2005
• Tom: Vol. 2(6)
• Strony: 7--20
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Cyran K.

• Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

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• 4. George N., Wang S., Venable D.L., 1989: Pattern recognition using the ring-wedge detector and neural network software, Proc. of SPIE, 1134, 96-106.
• 5. Cyran K.A., Mrózek A., 2001: Rough sets in hybrid methods for pattern recognition, International Journal of Intelligent Systems, 16, 149-168.
• 6. Pawlak Z., 1991: Rough sets - theoretical aspects of reasoning about data, Kluwer Academic Publishers.
• 7. Mrózek A., 1992: Rough sets in computer implementation of rule-based control of industrial processes. In Intelligent decision support. Handbook of applications and advances of the rough sets, Słowiński R. (Ed.) Kluwer Academic Publishers, 19-31.
• 8. Mrózek A., 1992: A new method for discovering rules from examples in expert systems, Man-Machine Studies, 36, 127-143.
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• 11. Jaroszewicz L.R., Cyran K.A., Podeszwa T., 2000: Optimized CGH- based pattern recognizer, Optica Applicata, 30, 317-333.
• 12. Cyran K.A., Jaroszewicz L.R., Niedziela T., 2001: Neural network based automatic diffraction pattern recognition, Opto-electronic Rev., 9 (3), 301-307.
• 13. Cyran K.A., Stańczyk U., Jaroszewicz L.R., 2002: Subsurface stress monitoring system based on holographic ring-wedge detector and neural network. In Quality, Reliability and Maintenance, McNulty G. J. (Ed.), Professional Eng. Publishing, 65-68.
• 14. Cyran K.A., Niedziela T., Jaroszewicz L.R., Podeszwa T., 2002: Neural classifiers in diffraction image processing, Proc. of International Conf. On Computer Vision and Graphics, Zakopane, Poland, 223-228.
• 15. Podeszwa T., Jaroszewicz L.R., Cyran K.A., 2003: Fiberscope based engine condition monitoring system, Proc. of SPIE, 5124, 2003, pp. 299-303.
• 16. Jaroszewicz L.R., Merta I., Podeszwa T., Cyran K.A., 2002: Airplane engine condition monitoring system based on artificial neural network. In: Quality, Reliability and Maintenance, McNulty G.J. (Ed.), Professional Engineering Publishing, 179-182.
• 17. Cyran K.A., Jaroszewicz L.R., 2001: Concurrent signal processing in optimized hybrid CGH-ANN system, Optica Applicata, 31, 681-689.
• 18. Cyran K.A., Jaroszewicz L.R., 2000: Rough set based classification of interferometric images. In Interferometry in speckle light. Theory and applications, Jacquot P., Fournier J.M. (Eds.) Springer, 413-420.
• 19. Cyran K.A., 2003: PLD-based rough classifier of Fraunhofer diffraction pattern, Proc. of International Conf. On Computer, Communication and Control Technologies, Orlando, FL, 163-168.
• 20. Cyran K.A., 2005: Combining rule based and connectionist approaches in a diffraction pattern recognition, Proc. of artificial Intelligence Studies, Special Issue, vol. 2(25), 149-157.
• 21. Cyran K.A., 2005: Integration of classifiers working in discrete and real valued feature space applied in two-way optoelectronic image recognition system, Proc. of the fifth IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2005), Benidorm, Spain, 592-597.