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Erschienen in:
Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

verfasst von : Daniela Sánchez, Patricia Melin

Erschienen in: Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Verlag: Springer International Publishing

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Abstract

In this chapter a new method of hierarchical genetic algorithm for fuzzy inference systems optimization is proposed. This method was used in two applications, the first was to perform the combination of responses of modular neural networks for human recognition based on face, iris, ear and voice, and the second one for fuzzy control of temperature in the shower benchmark problem. The results obtained by non-optimized type-2 fuzzy inference system can be improved using the proposed hierarchical genetic algorithm as can be verified by the simulations.

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Vorheriges Kapitel Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers
Nächstes Kapitel Designing Type-2 Fuzzy Systems Using the Interval Type-2 Fuzzy C-Means Algorithm
Literatur
1.
Azamm, F.: Biologically inspired modular neural networks. Ph.D. thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, May 2000
2.
Castillo, O., Melin, P.: Soft Computing for Control of Non-linear Dynamical Systems. Springer, Heidelberg (2001)CrossRefMATH
3.
Castillo, O., Melin, P.: Type-2 Fuzzy Logic Theory and Applications, pp. 29–43. Springer, Berlin (2008)CrossRefMATH
4.
Castro, J.R., Castillo, O., Melin, P., Mendoza O., Rodríguez Díaz A.: An interval type-2 fuzzy neural network for chaotic time series prediction with cross-validation and akaike test. In: Soft Computing for Intelligent Control and Mobile Robotics, pp. 269–285. Springer, Berlin (2011)
5.
6.
7.
8.
Haupt, R., Haupt, S.: Practical Genetic Algorithms, 2nd edn, pp. 42–43. Wiley-Interscience, New York (2004)MATH
9.
Hidalgo, D., Castillo, O., Melin, P.: An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms. Expert Syst. Appl. 39(4), 4590–4598 (2012)CrossRef
10.
Hidalgo, D., Castillo, O., Melin, P.: Optimization with genetic algorithms of modular neural networks using interval type-2 fuzzy logic for response integration: The case of multimodal biometry. In: International Joint Conference on Neural Networks (IJCNN), pp. 738–745 (2008)
11.
Hidalgo, D., Castillo, O., Melin, P.: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms. Inf. Sci. 179(13), 2123–2145 (2009)CrossRef
12.
Hidalgo, D., Castillo, O., Melin, P.: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms. In: Soft Computing for Hybrid Intelligent Systems, pp. 89–114, 1st edn. Springer, Berlin (2008)
13.
Hidalgo, D., Melin, P., Licea, G., Castillo, O.: Optimization of type-2 fuzzy integration in modular neural networks using an evolutionary method with applications in multimodal biometry. In: Mexican International Conference on Artificial Intelligence (MICAI), pp. 454–465 (2009)
14.
Khan, A., Bandopadhyaya, T., Sharma, S.: Classification of stocks using self organizing map. Int. J. Soft Comput. Appl. 4, 19–24 (2009)
15.
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn, pp. 119–122. Springer, Berlin (2005)
16.
Melin, P., Kacprzyk, J., Pedrycz, W.: Bioinspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Springer, Berlin (2009)CrossRef
17.
Melin, P., Sánchez, D., Castillo, O.: Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)
18.
Melin, P., Sánchez, D., Cervantes, L.: Hierarchical genetic algorithms for optimal type-2 fuzzy sys-tem design. In: Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 324–329 (2011)
19.
Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)
20.
Mendoza, O., Melin, P., Licea, G.: A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral. Inf. Sci. 179(13), 2078–2101 (2009)CrossRef
21.
Mitchell, M.: An Introduction to Genetic Algorithms, 3rd edn. A Bradford Book, Cambridge (1998)MATH
22.
Oh, S., Pedrycz, W., Roh, S.: Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and in-formation granulation. Appl. Soft Comput. 9(3), 1068–1089 (2009)CrossRef
23.
Pal, S.K., Mitra, S., Mitra, P.: Rough fuzzy MLP: modular evolution, rule generation and evolution. IEEE Trans. Knowl. Data Eng. 15(1), 14–25 (2003)CrossRef
24.
Sánchez, D., Melin P.: Optimization of modular neural networks and type-2 fuzzy integrators using hierarchical genetic algorithms for human recognition. In: IFSA World Congress and AFSS International Conference, World Congress (IFSA-AFSS), pp. OS-414-1–OS-414-8 (2011)
25.
Sánchez, D., Melin, P., Castillo O.: Hierarchical genetic algorithms for optimal type-2 fuzzy system design in modular neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 1267–1274 (2011)
26.
Segovia, J., Szczepaniak, P.S., Niedzwiedzinski, M.: E-Commerce and Intelligent Methods, 1st edn, p. 181. Physica-Verlag, Berlin (2002)CrossRef
27.
Tang, K.S., Man, K.F., Kwong, S., Liu, Z.F.: Minimal fuzzy memberships and rule using hierarchical genetic algorithms. IEEE Trans. Ind. Electron 45(1), 162–169 (1998)CrossRef
28.
Wang, C., Soh, Y.C., Wang, H.: A hierarchical genetic algorithm for path planning in a static environment with obstacles. In: Canadian Conference on Electrical and Computer Engineering, 2002. IEEE CCECE 2002, vol. 3, pp. 1652–1657 (2002)
29.
Worapradya, K., Pratishthananda, S.: Fuzzy supervisory PI controller using hierarchical genetic algorithms. In: 5th Asian Control Conference, 2004, vol. 3, pp. 1523–1528 (2004)
30.
Zhang, Z., Zhang, C.: An agent-based hybrid intelligent system for financial investment planning. In: 7th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 355–364 (2002)
Metadaten
Titel
Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control
verfasst von
Daniela Sánchez
Patricia Melin
Copyright-Jahr
2014
Buch
Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Print ISBN: 978-3-319-05169-7

Electronic ISBN: 978-3-319-05170-3

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-05170-3_2