Abstract
The design of an Artificial Neural Network (ANN) is a difficult task for it depends on the human experience. Moreover it needs a process of testing and error to select which kind of a transfer function and which algorithm should be used to adjusting the synaptic weights in order to solve a specific problem. In the last years, bio-inspired algorithms have shown their power in different non-linear optimization problems. Due to their efficiency and adaptability, in this paper we explore a new methodology to automatically design an ANN based on the Differential Evolution (DE) algorithm. The proposed method is capable to find the topology, the synaptic weights and the transfer functions to solve a given pattern classification problems.
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References
Goldberg, D.E.: Genetic Algorithms in Search, Optimizationand Machine Learning. Addison-Wesley Publishing Co., Reading (1989)
Schwefel, H.-P.: Evolution and Optimization Seeking. John Wiley & Sons, Chichester (1995)
Mezura-Montes, E., Velazquez-Reyes, J., Coello Coello, C.A.: A Comparative Study of Differential Evolution Variants for Global Optimization. In: GECCO (2006)
Tejen, S., et al.: A Hybrid Artificial Neural Networks and Particle Swarm Optimization for Function Approximation. In: ICIC, vol. 4, pp. 2363–2374 (2008)
Chau, K.W.: Application of a PSO-based neural network in analysis of outcomes of construction claims. Automation in Construction 16, 642–646 (2007)
Chatterjee, A., et al.: A Particle Swarm Optimized Fuzzy-Neural Network for Voice Controlled Robot Systems. IEEE Trans. on Ind. Electronics 52, 1478–1489 (2005)
Wang, Z., et al.: Particle Swarm Optimization and Neural Network Application for QSAR. In: Proceedings 18th Parallel and Distributed Processing Symposium (2004)
Zhao, L., Yang, Y.: PSO-Based Single Multiplicative Neuron Model for Time Series Prediction. Expert Systems with Applications 36, 2805–2812 (2009)
Da, Y., Ge, X.R.: An improved PSO–based ANN with simulated annealing technique. Neurocomput. Lett. 63, 527–533 (2005)
Yu, J., Xiand, L., Wang, S.: An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks. Neural Processing Letters 26, 217–231 (2007)
Garro, B.A., Sossa, H., Vazquez, R.A.: Design of Artificial Neural Networks using a Modified Particle Swarm Optimization Algorithm. In: Proc. IEEE Int. Joint Conf. Neural Networks, pp. 938–945 (2009)
Ilonen, J., Kamarainen, J.-K., Lampinen, J.: Differential Evolution Training Algorithm for Feed-Forward Neural Networks. Neural Processing Letters 17(1), 93–105 (2003)
Guiying, N., Yongquan, Z.: A Modified Differential Evolution Algorithm for Opti-mization Neural Network. In: Proceedings of ISKE (2007)
Abdul –Kader, H.M.: Neural Networks Training Based on Differential Evolution Algorithm Compared with Other Architectures for Weather Forecasting34. IJCSNS 9(3) (March 2009)
Anderson, J.A.: Introduction to Neural Networks. MIT Press, Cambridge (1995)
Werbos, P.J.: Backpropagation through time: What it does and how to do it. Proc. IEEE 78, 1550–1560 (1990)
Storn, R., Price, K.: Differential Evolution - a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, TR-95-012, ICSI (1995)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution: a practical approach to global optimization. Springer, Heidelberg (2005)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Dept. Inf. Comput. Sci., Univ. California, Irvine, CA (1994)
Mendis, B.S.U., Gedeon, T.D.: A comparison: Fuzzy Signatures and Choquet Integral. IEEE Congress on Computational Intelligence, 1464–1471 (2008)
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Garro, B.A., Sossa, H., Vázquez, R.A. (2010). Design of Artificial Neural Networks Using Differential Evolution Algorithm. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_25
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DOI: https://doi.org/10.1007/978-3-642-17534-3_25
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