Optimización en la elaboración de redes neuronales artifciales adaptativas usando una metodología de algoritmo de poda
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Publicado:
dic 1, 2017
Palabras clave:
hormigón de alto desempeño, inteligencia artifcial, redes neuronales artifciales, optimización en informática, algoritmo de poda, high performance concrete, artifcial intelligence, artifcial neural networks, computer optimization, pruning algorithm
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Resumen
Las redes neuronales artifciales feedforward y multicapa (RNA-MFF) han demostrado ser de gran alcance en la aproximación de funciones; sin embargo, su aplicación en problemas reales a menudo se limita a la experimentación del usuario, ya que la elección de arquitectura adecuada es un proceso que requiere conocimiento y experiencia. En este artículo se demuestra la capacidad de adaptación de la metodología del algoritmo de poda para encontrar el número óptimo de neuronas en la capa oculta de una RNA-MFF. La metodología se probó en dos conjuntos diferentes de problemas de referencia: modelación de la resistencia y del asentamiento, en hormigón de alto desempeño. Ambos conjuntos se utilizaron para analizar el tamaño de la arquitectura inicial de la red neuronal artifcial y para asegurar que el número superior propuesto de neuronas ocultas se pueda evitar en exceso.AbstractFeedforward and multi-layer artifcial neural networks (RNA-MFF) have been shown to be powerful in the approximation of functions; however, its application to real problems is often limited to user experimentation, since choosing the right architecture is a process that requires knowledge and experience. This article demonstrates the adaptability of the pruning algorithm methodology in fnding the optimal number of neurons in the hidden layer of an RNA-MFF. The methodology was tested on two different sets of reference problems: resistance and settling modeling, in high performance concrete. Both sets were used to analyze the initial architecture size of the artifcial neural network and to ensure that the proposed higher number of hidden neurons can be avoided in excess.Resumo As redes neurais artifciais avançadas e multicamadas feedforward (RNA-MFF) têm demostrado ter grande alcance na aproximação de funções; porém, a aplicação em problemas reais geralmente é limitada à experimentação do usuário, dado que escolher a arquitetura adequada é um processo que precisa conhecimento e experiência. Neste artigo foi demonstrada a adaptabilidade da metodologia do algoritmo de poda para encontrar o número ótimo de neurônios na camada oculta de uma RNA-MFF. A metodologia foi testada em dois conjuntos diferentes de problemas de referência: modelagem de resistência e sedimentação, em concreto de alto desempenho. Ambos os conjuntos foram utilizados para analisar o tamanho da arquitetura inicial da rede neural artifcial e garantir que o maior número de neurônios ocultos propostos possa ser evitado em excesso.
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González-Salcedo, L. O., Gotay-Sardiñas, J., Roodschild, M., Ernesto-Will, A. L., & Rodríguez, S. (2017). Optimización en la elaboración de redes neuronales artifciales adaptativas usando una metodología de algoritmo de poda. Ingenio Magno, 8(1), 44-56. Recuperado a partir de http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1388
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Artículos Vol. 8-1
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Por medio del presente documento, certifico(amos) que el artículo que se presenta para posible publicación en la revista institucional INGENIO MAGNO del Centro de Investigaciones de Ingeniería Alberto Magno CIIAM de la Universidad Santo Tomás, seccional Tunja, es de mi (nuestra) entera autoría, siendo su contenido producto de mi (nuestra) directa contribución intelectual y aporte al conocimiento.
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Bhadeshia, H., Dimitriu, R., Forsik, S., Pak, J. y Ryu, J. (2009). Performance of neural networks in materials science. Materials Science and Technology, 25(4), 504-510.
Castellano, G., Fanelli, A. y Pelillo, P. (1997). An iterative pruning algorithm for feedforward neural networks. IEEE Transactions on Neural
Networks, 8(3), 519-531.
Chaouachi, A., Kamel, R., Ichikawa, R., Hayashi, H. y Nagasaka, K.
(2009). Neural network ensemble-based solar power generation short-term forecasting. International Journal of Engineering and Mathematical Sciences, 3(6), 40-46.
Constantiner, D. y Dackzo, J. (2002). Not all applications are created equal: Selecting the appropriate SCC performance targets. First North American Conference on the Design and Use of Self Consolidating Concrete. Chicago.
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Sigma and Systems, 2(4), 303-314.
Díaz, W., Will, A. y González, S. (2011). Un método para estimación de calidad de datos en redes neuronales y aplicación a problemas de hormigón de alto performance. Mecánica Computacional, 30(43), 3381-3381.
Doukim, C., Dargham, J. y Chekima, A. (2010). Finding the number of hidden neurons for a MLP neural networks using coarse to fne search technique. Recuperado de https://www.semanticscholar.org/paper/Finding-the-number-of-hidden-neurons-for-an-MLP-neDoukim-Dargham/c442c3ac2589e1f048d-
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González, S., Guerrero, Z., Delvasto, A. y Will, A. (2014). Estimación del Índice de Tenacidad Flexural I5 en concretos fbroreforzados, usando redes neuronales artifciales. Revista Colombiana de Materiales, 5, 24-29.
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Hornik, K., Stinchcombe, M. y White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366.
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Koza, J. y Rice, J. (1991). Genetic generation of both the weigths and arquitecture for neural networks. Proceedings of IEEE, IJCNN, Seattle
(WA), pp. 397-404.
Kwok, T. y Yeung, D. (1997). Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3), 630-645.
MacKay, D. (1992). A practical bayesian framework for backpropagation networks. Neural Computation, 4(3), 448-472.
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Panchal, G., Ganatra, A., Kosta, Y. y Panchal, D. (2011). Behaviour analysis of multilayer perceptrons with multiple hidden neurons and
hidden layers. International Journal of Computer Theory and Engineering, 3(2), 332-337.
Prechelt, L. (1998). Automatic early stopping using cross validation: quantifying the criteria. Neural Networks, 11(4), 761-767.
Reed, R. (1993). Pruning algorithms-a survey. Transations of Neural Networks, 4(5), 740-747.
Sánchez, D. (2000). Tecnología del Concreto y Mortero. Bogotá: Bhandar.
Sharma, S. y Chandra, P. (2010a). Constructive neural networks: A review. International Journal of Engineering Science and Technology, 2(12), 7847-7855.
Sharma, S. y Chandra, P. (2010b). An adaptive slope sigmoidal function cascading neural networks algorithm. Proceedings of the IEEE, Third International Conference on Emerging Trends in Engineering and Technology.
Sharma, S. y Chandra, P. (2010c). An adaptive slope basic dynamic node creation algorithm for single hidden layer neural networks. Proceedings of the IEEE, International Conference on Computational Intelligence and Communication Systems.
Subirats, J., Franco, L. y Jerez, J. (2012). C-MANTEC: A novel constructive neural network algorithm incorporating competition between neurons. Neural Networks, 26, 130-140.
Tompkins, C. (1992). Using and interpreting linear regression and correlation analyses: Some cautions and considerations. Clinical
Aphasiology Paper, 21, 35-46.
Wei, G. (2008). Evolutionary neural network based on new ant colony algorithm. International Symposium on Computational Intelligence and Design IEEE.
Yao, X. y Liu, Y. (1997). A new revolutionary system for evolving artificial neural networks. Transactions on Neural Networks, 8(3), 694-713.
Yeh, I. (1998). Modeling of strength of high-performance concrete using artifcial neural networks. Cement and Concrete Research, 28(12), 1797-1808.
Yeh, I. (2007). Modeling slump flow of concrete using second-order regressions and artifcial neural networks. Cement and Concrete Composites, 29(6), 474-480.
Yuan, H., Xiong, F. y Huai, X. (2003). A method for estimating the number of hidden neurons in feed-forward neural networks based on information entropy. Computers and Electronics in Agriculture, 40(1-3), 57-64.