Abstract
Neural networks ensembles are powerful tools for solving modeling and time series forecasting problems. This approach is based on cooperative usage of neural networks for problem solving. The two major stages of the neural networks ensemble construction are: design and training of the component networks and combining of the component networks predictions to produce the ensemble output. In this paper developed evolutionary approach for neural networks ensembles automatic design is reviewed briefly. This approach is based on the operators of the well-known evolutionary algorithms and requires fewer parameters to be tuned providing more flexible and adaptive solutions. Results of the neural networks ensemble approach applying for modeling of spacecrafts arrays degradation are discussed.
Keywords
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
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence (12), 993–1001 (1990)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley (2004)
Zhou, Z.H., Wu, J., Tang, W.: Ensembling neural networks: Many could be better than all. Artificial Intelligence 137(1-2), 239–263 (2002)
Perrone, M.P., Cooper, L.N.: When networks disagree: ensemble method for neural networks. In: Mammone, R.J. (ed.) Artificial Neural Networks for Speech and Vision, pp. 126–142. Chapman & Hall, New York (1993)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1990)
Jimenez, D.: Dynamically weighted ensemble neural networks for classification. In: Proceedings of IJCNN 1998, Anchorage, AK, USA, vol. 1, pp. 753–756 (1998)
Sylvester, J., Chawla, N.: Evolutionary ensemble creation and thinning. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 5148–5155 (2006)
Dos Santos, E., Sabourin, R., Maupin, P.: Single and multi-objective genetic algorithms for the selection of ensemble of classifiers. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 3070–3077 (2006)
Santana, L., Silva, L., Canuto, A., Pintro, F., Vale, K.: A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Semenkin, E.S., Sopov, E.A.: Probabilities-based evolutionary algorithms of complex systems optimization. In: Proceedings of Intelligent Systems (AIS 2005) and Intelligent (CAD 2005), vol. 1, pp. 77–79. FIZMATLIT, Moscow (2005)
Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E.: Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, Secaucus (2006)
Bukhtoyarov, V., Semenkina, O.: Comprehensive evolutionary approach for neural network ensemble automatic design. In: Proceedings of the IEEE World Congress on Computational Intelligence, Barcelona, Spain, pp. 1640–1645 (2010)
Koza, J.R.: The genetic programming paradigm: genetically breeding populations of computer programs to solve problems. MIT Press, Cambridge (1992)
Grigorieva, G.M., Kagan, M.B., Letin, V.A., Nadorov, V.P., Evenov, G.D., Hartov, V.V.: Analysis of Geostationary Spacecraft Solar Arrays Degradation from Solar Proton Flares. In: Space Power: Proceedings of the Sixth European Conference, Porto, pp. 725–730 (2002)
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
Bukhtoyarov, V., Semenkin, E., Shabalov, A. (2012). Neural Networks Ensembles Approach for Simulation of Solar Arrays Degradation Process. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-28942-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28941-5
Online ISBN: 978-3-642-28942-2
eBook Packages: Computer ScienceComputer Science (R0)