Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and its Optimization with Genetic Algorithms

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

Abstrakty

EN

We describe in this paper a comparative study between Fuzzy Inference Systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.

Słowa kluczowe

EN

modular neural networks; type-2 fuzzy logic; pattern recognition; genetic algorithms

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2008
• Tom: Vol. 2, No. 1
• Strony: 59--73
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Hidalgo D.

autor

Castillo O.

autor

Melin P.

• Tijuana Institute of Technology, Tijuana BC. Mexico

Bibliografia

• [1] Jérica Urias Penuelas, Desarrollo de un nuevo Metodo de Integración utilizando Lógica Difusa Tipo-2para Sistemas Biometricos, Instituto Tecnológico de Tijuana, 2006.
• [2] J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.
• [3] Juan Ramón Castro Rodriguez, Tutorial Type-2 Fuzzy Logic: theory and applications, Universidad Autónoma de Baja California, Instituto Tecnológico de Tijuana. 9th October 2006. Available at: www.hafsamx.org/cis-chmexico/seminar06/tutorial.pdf
• [4] K.F. Man, K.S. Tang, S. Kwong, GeneticAlgorithms, Concepts and Designs, Springer, 1999.
• [5] Algoritmos Genéticos. Available at: http://eddyalfaro.galeon.com/geneticos.html
• [6] http://sci2s.ugr.es/publications/ficheros/estylf-2004-l-8.pdf. February, 2007.
• [7] Algoritmos geneticos en la construcción de funciones de membrecfa difusa, February 2007. http://wotan.liu.edu/docis/dbl/iariia/2003^18_25-. AGELCD.htm
• [8] Juan A. Siguenza, Merino Tapiador Mateos (Ra-ma), Tecnologias Biometricas Aplicadas a [a Seguridad. ISBN: 8478976361. ISBN-13: 9788478976362. March 2007. http://www.agapea.com/Tecnologias-biometricas-aplicadas-a-la-seguridad-n214440i.htm
• [9] Soludones Biometricas, Grupo INSYS, Torreón Coah. MEXICO, February 2007. Available at: http://www.insys.com.mx/biometria/biometria.html
• [10] Michael Bronstein, Biometria. Biolynx, National Geographic, Instituto de Investigación Tecnológica de Georgia, April 2007. http://www.tecnociencia.es/ monograficos/biometria/biometrial.htm
• [11] Ouiliano Isaac Moro Sancho, Universidad de Valladolid, Spain; L Alonso Romero. Universidad de Salamanca. Spain, Sistemas Modulares, Mezcla de Expertos y Sistemas Hibridos. February 2007. Available at: http://lisisu02.usal.es/~airene/capit7.pdf
• [12] L. Alonso Romero, Aplicadones e Implementadones de las Redes Neuronales en reconodmiento de Patrones (AIRENE) 30/06/99, Universidad de Salamanca. Spain, March 2007. Available at: http://lisisu02.usal.es/-airene/airene.html
• [13] L. A. Zadeh, "Knowledge representation in Fuzzy Logic", IEEE Transactions on knowledge data engineering, vol. l, 1989,pp.89-100.
• [14] L. A. Zadeh, "Fuzzy Sets", Information and Control, vol 8,1975, pp. 338-353.
• [15] G. Chen, T.T. Phan, Introduction to Fuzzy Sets, Fuzzy Logic and Fuzzy Control Systems, CRC Press, EEUU, 2000.
• [16] L. A. Zadeh, "Fuzzy Logic - Computing with Words", IEEE Transactions on Fuzzy Systems, vol 4, no. 2, May 1996,pp.103-111.
• [17] 0. M. Mendel, UNCERTAIN Rule-Based Fuzzy Logic Systems, Introduction and New Directions, Prentice Hall, 2001.
• [18] J. M. Mendel, "Why We Need Type-2 Fuzzy Logic Systems?", Article is provided courtesy of Prentice Hall, May 11, 2001. Available at: http://www.informit.com/ articles/article.asp?p-21312&rl-l,
• [19] J. M, Mendel, "Uncertainty: General Discussions", Article is provided courtesy of Prentice Hall, May 11, 2001. Available at: http://www.informit.com/ articles/artide.asp?p-21313
• [20] 3. M. Mendel, R. L Bob John, "Type-2 Fuzzy Sets Made Simple", IEEE Transactions on Fuzzy Systems, vol 10, no.2, April 2002,pp.117-127.
• [21] N.N. Karnik, 0. M. Mendel, "Operations on type-2 fuzzy sets", Fuzzy Sets and Systems, vol. 122, no. 2, 2001, pp. 327-348.
• [22] L. A. Zadeh, "Fuzzy Logic", Computer, vol. l, no, 4, 1998, pp.83-93.
• [23] N. N. Karnik, J. M. Mendel, Qilian Liang, "Type-2 Fuzzy Logic Systems", IEEE Transactions on Fuzzy Systems, vol. 7, no. 6, December 1999, pp. 643-658.
• [24] 0. Ramos Gaxiola, Redes Neuronales Apticadas a la Identificación de Locutor Mediante Voz Utilizando Extracdón de Caractensticas, Instituto Tecnológico de Tijuana, 2006.
• [25] J. Manuel Alvarado Verdugo, "Reconocimiento de la persona por medio de su rostro y huella utilizando redes neuronales modularesy la transformada wavele", Instituto Tecnológico de Tijuana, 2006.
• [26] P. Melin, O. Castillo, E. Gómez Ramirez, J.Kacprzyk, W. Pedrycz, Analysis and Design of Intelligent Systems Using Soft Computing Techniques. Series: Advances in Soft Computing,vol. 41, Springer, 2007.
• [27] The 2007 International Joint Conference on Neural Networks, I3CNN 2007 Conference Proceedings. Renais-sance Orlando Resort, Orlando, Florida, USA. 12th-17th August, 2007.