Hybrid intelligent system for pattern recognition

• Autorzy: Melin P. , Castillo O.
• Języki publikacji: EN

Abstrakty

EN

We describe in this paper a general overview oj the analysis and design of hybrid intelligent systems for pattern recognition applications. Hybrid intelligent systems can be developed by a careful combination of several soft-computing techniques. The combination of soft computing techniques has to take advantage of the capabilities of each technique in solving port of the pattern recognition problem. We review the problems of face, fingerprint and mice recognition and their soiution using hybrid intelligent systems. Recognition rates achieved with the hybrid approaches are comparable with the best approaches known for solving these recognition problems.

Słowa kluczowe

EN

soft computing; intelligent system; algorithms; fuzzy logic; neural networks

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2007
• Tom: Vol. 1, No. 2
• Strony: 13--19
• Opis fizyczny: Bibliogr. 38 poz.

Twórcy

autor

Melin P.

autor

Castillo O.

• Professor of Computer Science in the Graduate Division Tijuana Institute of Technology, Tijuana, Mexico,

Bibliografia

• [1] Akamatsu S., Saski T., Fukamachi H., Masui N. & Suenaga Y.,"Robust Face Identification Scheme", Proceedings of SPIE, 1992,pp.71-84.
• [2] Atanassov K., "Intuitionistic Fuzzy Sets: Theory and Applications", Springer-Verlag, Heidelberg, Germany, 1999.
• [3] Barna G. & Kaski K., "Choosing optimal network structure", Proceedings of the International Neural NetworkConference (INNC90), 1990, pp. 890-893.
• [4] Bezdek J.C., Pattern Recognition with Fuzzy Objective FunctionAlgorithms, New York Plenum Press, 1981.
• [5] Castillo 0. & Melin P., "Soft Computing for Control of Non-Linear Dynamical Systems", Springer-Verlag, Heidelberg, Germany, 2001.
• [6] Feldman J. "Neural representation of conceptual knowledge", in Neural connections, mental computation (Nadeland et al. eds.), MIT Press, 1989.
• [7] Fu H.-C., Lee Y.-P., Chiang C.-C. & Pao H.-T. (2001).“Divide-and-Conquer Learning and Modular Perceptron Networks", IEEE Transactions on Neural Networks, 2001, vol. 12, no. 2, pp. 250-263.
• [8] Furui S.,"An overview of speaker recognition technology", in: Proceedings of the ESCA Workshop on Automatic  Speaker  Recognition, Identification and Verification, 1994, pp. 1-9.
• [9] Goldberg D.E., Genetic Algonthms in Search, Optimization and Machine Learning, Addison Wesley Publishing, 1989.
• [10] Happel. B. & Murre, J., "Design and evolution of modular neural network architectures", Neural Networks, 1994, vol. 7, pp. 985-1004.
• [11] Jacobs R. A., Jordan M. L, Nowlan S. J. & Hinton G. E., "Adaptive Mixtures of Local Experts", Neural Computation, 1991, vol.3, pp. 79-87.
• [12] Jamshidi M., "Large-Scale Systems: Modelling, Control and Fuzzy Logic", NJ: Prentice-Hall, 1997
• [13] Jang J.-S. R. Sun, C.-T. & Mizutani E., Neurofuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, 1997.
• [14] Kandei A., Fuzzy Expert Systems, CRC Press Inc., 1992.
• [15] Karnik N.N & Mendel J.M., An Introduction to Type-2 Fuzzy Logic Systems, Technical Report, University of Southern California, 1998.
• [16] Karr C. L. & Gentry E. J., "Fuzzy Control of pH using Genetic Algorithms", IEEE Transactions on Fuzzy Systems, 1993, vol. 1, no. 1, pp. 46-53.
• [17] Kohonen T., "Improved Version of Learning Vector Quantization", in: Proceedings of the IJCNN'90, San Diego, CA, USA, 1990, vol. l, pp. 545-550.
• [18] Kosko B., Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, NJ: Prentice-Hall, 1992.
• [19] Lu B.-L. & Ito M., "Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification", IEEE Transactions on Neural Networks, 1999, vol. 10, no, 5, pp, 1244-1256.
• [20] Man K. F., Tang K.S. & Kwong S., Genetic Algorithms, Springer-Verlag, 1999.
• [21] Melin P. & Castillo 0., Modelling Simulation and Control of Non-Linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory, London: Taylor and Francis Publishers, United Kingdom, 2002.
• [22] Melin P. & Castillo 0., Hybrid Intelligent Systems for Pattern Recognition, Heidelberg: Springer-Veralg, Germany, 2005.
• [23] Melin P., Acosta, M. L. & Felix C., "Pattern Recognition Using Fuzzy Logic and Neural Networks", Proceedings of IC-AI'03, Las Vegas, USA, 2003, pp. 221-227.
• [24] Melin P., Gonzalez F. & Martinez G. "Pattern Recognition Using Modular Neural Networks and Genetic Algorithms", Proceedings of IC-AI'04, Las Vegas, USA, 2004, 83.
• [25] Melin P., Mancilla, A., Gonzalez, C. & Bravo D. "Modular Neural Networks with Fuzzy Sugeno- Integral Response for Face and Fingerprint Recognition", Proceedings of IC-AI'04, Las Vegas, USA, 2004, pp. 91-97.
• [26] Mendel J., Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, New Jersey: Prentice-Hall, USA, 2001
• [27] Michalewicz Z.” Genetic Algorithms + Data Structures =Evolution program, 3rd Edition, Springer-Verlag, 1996.
• [28] Miller W. T., Sutton R. S. & Werbos P. J., Neural Networks for Control, MIT Press, Cambridge, MA, 1995.
• [29] Mitchell M., An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA, USA, 1996.
• [30] Rao R. & Ballard D., "Natural Basis Functions and Topographics Memory for Face Recognition", Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 20-25 August, 1995, pp. 10-17.
• [31] Reisfeld D., Generalizad Symmetry Transforms: Attentional Mechanisms and Face Recognition, Ph.D.Thesis, Tel-Aviv University, Israel, 1994.
• [32] Rosenberg A.E. & Soong F. K. "Recent research in automatic speaker recognition", In: S. Furui and M. M.Sondhi, editors, Advances in Speech Signal Processing,Marcel Dekker, New York, 1991, pp. 701-737.
• [33] Sharkey A., Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, Springer-Verlag, London, UK, 1999,
• [33] Sugeno M. & Kang G. T., "Structure Identification of Fuzzy Model", Journal of Fuzzy Sets and Systems, 1988, vol. 28, issue 1 (October), pp. 15-33.
• [34] Szmidt E. & Kacprzyk J., "Analysis of Agreement in a Group of experts via Distances between Intuitionistic Preferences",Proceedings of IPMU'2002, Universite Savoie, France, 2002, pp. 1859-1865.
• [35] Yager R. R., "A General Approach to Criteria Aggregation Using Fuzzy Measures", International Journal of Man-Machine Studies, 1993, vol. 1, no. 2, pp. 187-213.
• [36] Yager R. R. & Filev D.P. (1994). "Generation of Fuzzy Rules by Mountain Clustering", Journal of Intelligent and Fuzzy Systems, 1994, vol. 2, no. 3, pp. 209-219.
• [37] Zadeh L.A., "Fuzzy Sets", Journal of Information and Control, 1965, vol. 8, pp. 338-353.
• [38] Zadeh L.A., "The Concept of a Linguistic Variable and its Application to Approximate Reasoning1", Journal of Information Sciences, 1975, vol. 8, pp. 199-249.