Abstract
In this paper a new Modular Neural Network (MNN) optimization is proposed, where a particle swarm optimization with a fuzzy dynamic parameter adaptation designs optimal MNNs architectures. This design consists in to find the number of hidden layers for each sub module with their respective number of neurons, learning method, error goal and the percentage of data used for the training phase. The proposed method is applied to pattern recognition based on the iris biometrics and has as objective function to minimize the error of recognition. The proposed fuzzy adaptation seeks to avoid stagnation of error of recognition during iterations updating some PSO parameters such as w, C 1 and C 2 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abraham, A.: Hybrid intelligent systems: evolving intelligence in hierarchical layers. In: Studies in Fuzziness and Soft Computing, vol. 173, pp. 159–179. Springer, Heidelberg (2015)
Amato, F., López, A., Peña-Méndez, E.M., Vaňhara, P., Hampl, A., Havel, J.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11, 47–58 (2013)
Castillo, O., Neyoy, H., Soria, J., Melin, P., Valdez, F.: A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Appl. Soft Comput. 28, 150–159 (2015)
Ch’Ng, S.I., Seng, K.P., Ang, L.: Modular dynamic RBF neural network for face recognition. In: 2012 IEEE Conference on Open Systems (ICOS), pp. 1–6 (2012)
Chakri, A., Khelif, R., Benouaret, M., Yang, X.S.: New directional bat algorithm for continuous optimization problems. Expert Syst. Appl. 69, 159–175 (2017)
Choubey, D.K., Pau, S.: GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int. J. Intell. Syst. Appl. 1, 49–59 (2016)
Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp. Accessed 12 Nov 2015
Farooq, M.: Genetic algorithm technique. Int. J. Innov. Res. Sci. Eng. Technol. 4(4), 1891–1898 (2015)
Hicham, A., Mohammed, B., Anas, S.: Hybrid intelligent system for sale forecasting using Delphi and adaptive fuzzy back-propagation neural networks. Int. J. Adv. Comput. Sci. Appl. 3(11), 122–130 (2012)
Jain, K., Nandakumarb, K., Ross, A.: 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn. Lett. 79, 80–105 (2016)
Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, New Jersey (1997)
Kareem, M.H., Jassim, J.M., Al-Hareeb, N.K.: Mathematical modelling of particle swarm optimization algorithm. Int. J. Adv. Multi. Res. (IJAMR) 3(4), 54–59 (2016)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Joint Conference on Neuronal Network (1995)
Landassuri-Moreno, V.M., Bullinaria, J.A.: Biasing the evolution of modular neural networks. In: IEEE Congress on Evolutionary Computation, New Orleans, LA, USA, pp. 1958–1965 (2011)
Maia, D., Trindade, R.: Face detection and recognition in color images under matlab. Int. J. Sig. Process. Image Process. Pattern R 9(2), 13–24 (2016)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, pp. 119–122. Springer, Heidelberg (2005)
Mustafa, D., Osman, U., Tayyab, W.: Washing machine using fuzzy logic. Autom. Control Intell. Syst. 2(3), 27–32 (2014)
Rini, D.P., Shamsuddin, S.M., Yuhaniz, S.S.: Particle swarm optimization: technique, system and challenges. Int. J. Comput. Appl. 14(1), 19–27 (2011)
Sánchez, D., Melin, P., Castillo, O.: Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Inf. Sci. 309, 73–101 (2015)
Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. Artif. Intell. 27, 41–56 (2014)
Seera, M., Lim, C.P.: A hybrid intelligent system for medical data classification. Expert Syst. Appl. 41, 2239–2249 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Zadeh, L.A.: Fuzzy Sets and Information Granulation. North Holland Publishing, New York (1979)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zurada, J.M.: Introduction to Artificial Neural Systems. West Group, Eagan (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Sánchez, D., Melin, P., Castillo, O. (2018). Fuzzy Adaptation for Particle Swarm Optimization for Modular Neural Networks Applied to Iris Recognition. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-67137-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67136-9
Online ISBN: 978-3-319-67137-6
eBook Packages: EngineeringEngineering (R0)