Abstract
We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach.
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References
Barna, G., Kaski, K.: Choosing optimal network structure. In: Proceedings of the International Neural Network Conference (INNC90), pp. 890–893 (1990)
Castillo, O., Melin, P.: Hybrid Intelligent Systems for Time Series Prediction using Neural Networks, Fuzzy Logic and Fractal Theory. IEEE Transactions on Neural Networks 13(6), 1395–1408 (2002)
Chalmers, D.J.: The Evolution of Learning: An Experiment in Genetic Connectionism. In: Proceedings of the 1990 Connectionist Models Summer School, Morgan Kaufmann, San Francisco (1990)
Feldman, J.: Neural representation of conceptual knowledge. In: Nadel, L., et al. (eds.) Neural connections, mental computation, MIT Press, Cambridge (1989)
Fogelman-Soulie, F.: Multi-modular neural network-hybrid architectures: a review. In: Proceedings of 1993 International Joint Conference on Neural Networks (1993)
Happel, B., Murre, J.: Design and evolution of modular neural network architectures. Neural Networks 7, 985–1004 (1994)
Jacobs, R.A., et al.: Adaptive Mixtures of Local Experts. Neural Computation 3, 79–87 (1991)
Jenkins, R., Yuhas, B.: A simplified neural network solution through problem decomposition: The case of the truck backer-upper. IEEE Transactions on Neural Networks 4(4), 718–722 (1993)
Jordan, M.I., Jacobs, R.A.: Hierarchical Mixtures of Experts and the EM Algorithm. Neural Computation 6, 181–214 (1994)
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Design. Springer, Heidelberg (1999)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Monrocq, C.: A probabilistic approach which provides and adaptive neural network architecture for discrimination. Proceedings of the International Conference on Artificial Neural Networks, 252–256. Conf.Publ. No.372 (1993)
Montana, D.J., Davis, L.D.: Training Feedforward Networks using Genetic Algorithms. In: Proceedings of the International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco (1989)
Simon, H.: The sciences of the artificial. MIT Press, Cambridge (1981)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition (2005)
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Melin, P., Mancilla, A., Lopez, M., Solano, D., Soto, M., Castillo, O. (2007). Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks. In: Saad, A., Dahal, K., Sarfraz, M., Roy, R. (eds) Soft Computing in Industrial Applications. Advances in Soft Computing, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70706-6_10
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DOI: https://doi.org/10.1007/978-3-540-70706-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70704-2
Online ISBN: 978-3-540-70706-6
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