Abstract
Multilayer Perceptron Artificial Neural Networks (MLP-NN) have been widely used to tackle forecasting problems. The most used algorithm for training MLP-NN is called Backpropagation (BP). Since the BP presents a high chance to be trapped in local minima during the training process for forecasting, we propose in this paper to assess some recently proposed variations of the Particle Swarm Optimization algorithm (PSO) applied for this purpose. We tested the standard PSO, the APSO, the ClanPSO and the ClanAPSO in five benchmark data sets. Although the standard version of the PSO presented worse results when compared to the BP algorithm, we observed that the ClanAPSO outperformed the BP algorithm in most of cases.
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
Preview
Unable to display preview. Download preview PDF.
References
Junyou, B.: Stock price forecasting using pso-trained neural networks. In: Evolutionary Computation, CEC 2007, pp. 2879–2885 (2007)
Sun, W., Zang, Y., Li, F.: The neural network model based on pso for short-term load forecasting. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 3069–3072 (2006)
Valença, M., Ludermir, T.: Neural networks vs. parma modelling: Case studies of river flow prediction. In: Proceedings Sixth Brazilian Symposium on Neural Networks, pp. 113–116 (November 2000)
Braga, A., Carvalho, A., Ludermir, T.: Redes Neurais Artificiais: Teoria e Aplicações. In: LTC (2000)
Valença, M.: Fundamentos das Redes Neurais. Livro Rápido (2009)
Carvalho, M.: Uma análise de otimização de redes neurais mlp por enxame de partículas. Master’s thesis, Universidade Federal de Pernambuco (2007)
Pham, D.T., Koç, E., Ghanbarzadeh, A., Otri, S.: Optimisation of the weights of multi-layered perceptrons using bees algorithm. In: Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, pp. 38–46 (2006)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks, vol. 4 (1995)
Pontes, M.R., Lima-Neto, F.B., Bastos-Filho, C.J.A.: Adaptive clan swarm optimization. In: IEEE Symposium Series on Computational Intelligence, SSCI, pp. 17–22 (2011)
Zhan, Z., Zhang, J., Li, Y., Chung, H.: Adaptive particle swarm optimization. IEEE Transactions on Systems Man, and Cybernetics-Part B: Cybernetics 39(6), 1362–1381 (2009)
Kennedy, J., Mendes, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 38–44
Bastos-Filho, C.J.A., Carvalho, D.F., Caraciolo, M.P., Miranda, P.B.C., Figueiredo, E.M.N.: Multi-ring particle swarm optimization. In: 10th Brazilian Symposium on Neural Networks, p. 572 (2008)
Carvalho, D.F., Bastos-Filho, C.J.A.: Clan particle swarm optimization. In: IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3044–3051 (June 2008)
Aha, D., Asuncion, A., Newman, D.: The uci machine learning repository (2007)
FCT: The laboratory of artificial intelligence and decision support (liaad) (1988)
Chambers, J.: The r project for statistical computing
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Santos, S.M., Valença, M.J.S., Bastos-Filho, C.J.A. (2012). Comparing Particle Swarm Optimization Approaches for Training Multi-Layer Perceptron Neural Networks for Forecasting. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)