Abstract
This paper introduces a Genetic Algorithm (GA) for training Artificial Neural Networks (ANNs) using the electromagnetic spectrum signal of a combustion process for flame pattern classification. Combustion requires identification systems that provide information about the state of the process in order to make combustion more efficient and clean. Combustion is complex to model using conventional deterministic methods thus motivate the use of heuristics in this domain. ANNs have been successfully applied to combustion classification systems; however, traditional ANN training methods get often trapped in local minima of the error function and are inefficient in multimodal and non-differentiable functions. A GA is used here to overcome these problems. The proposed GA finds the weights of an ANN than best fits the training pattern with the highest classification rate.
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References
Mahajan, A., Mahajan, R.: Expert system for flame analysis. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 2, pp. 1160–1165. IEEE (2004)
Xu, L., Yan, Y., Cornwell, S., Riley, G.: On-line fuel identification using digital signal processing and fuzzy inference techniques. IEEE Transactions on Instrumentation and Measurement 53(4), 1316–1320 (2004)
Torres, C.I., Hernández, F., Trejo, A., Ronquillo, G.: Support vector machines applied to a combustion process
Li, X., Sun, D., Lu, G., Krabicka, J., Yan, Y.: Prediction of nox emissions throughflame radical imaging and neural network based soft computing. In: 2012 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 502–505. IEEE (2012)
Hao, Z., Qian, X., Cen, K., Jianren, F.: Optimizing pulverized coal combustion performance based on ann and ga. Fuel Processing Technology 85(2), 113–124 (2004)
Kesgin, U.: Genetic algorithm and artificial neural network for engine optimisation of efficiency and nox emission. Fuel 83(7), 885–895 (2004)
Palmes, P.P., Hayasaka, T., Usui, S.: Mutation-based genetic neural network. IEEE Transactions on Neural Networks 16(3), 587–600 (2005)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Sexton, R.S., Dorsey, R.E., Johnson, J.D.: Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Systems 22(2), 171–185 (1998)
Rivero, D., Dorado, J., Fernández-Blanco, E., Pazos, A.: A genetic algorithm for ANN design, training and simplification. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009, Part I. LNCS, vol. 5517, pp. 391–398. Springer, Heidelberg (2009)
Yao, X., Islam, M.M.: Evolving artificial neural network ensembles. IEEE Computational Intelligence Magazine 3(1), 31–42 (2008)
Ballabio, D., Vasighi, M., Consonni, V., Kompany-Zareh, M.: Genetic algorithms for architecture optimisation of counter-propagation artificial neural networks. Chemometrics and Intelligent Laboratory Systems 105(1), 56–64 (2011)
Annunziato, M., Bertini, I., Lucchetti, M., Pizzuti, S.: Evolving weights and transfer functions in feed forward neural networks. In: Proc. EUNITE 2003, Oulu, Finland (2003)
Tsimpiris, A., Kugiumtzis, D.: Feature selection for classification of oscillating time series. Expert Systems 29(5), 456–477 (2012)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer (1996)
Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4), 525–533 (1993)
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Gómez, J.C., Hernández, F., Coello, C.A.C., Ronquillo, G., Trejo, A. (2013). Flame Classification through the Use of an Artificial Neural Network Trained with a Genetic Algorithm. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_15
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DOI: https://doi.org/10.1007/978-3-642-45111-9_15
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