Spacecraft Solar Arrays Degradation Forecasting with Evolutionary Designed ANN-based Predictors

Maria Semenkina, Shakhnaz Akhmedova, Eugene Semenkin, Ivan Ryzhikov

Abstract

The problem of forecasting the degradation of spacecraft solar arrays is considered. The application of ANN-based predictors is proposed and their automated design with self-adaptive evolutionary and bio-inspired algorithms is suggested. The adaptation of evolutionary algorithms is implemented on the base of the algorithms’ self-configuration. The island model for the bio-inspired algorithms cooperation is used. The performance of four developed algorithms for automated design of ANN-based predictors is estimated on real-world data and the most perspective approach is determined.

References

  1. Akhmedova, Sh., Semenkin, E., 2013. New optimization metaheuristic based on co-operation of biology related algorithms. Vestnik. Bulletine of Siberian State Aerospace University, Vol. 4 (50), 2013, pp. 92-99.
  2. Akhmedova, Sh., Semenkin, E., 2013. Co-Operation of Biology Related Algorithms. In: Proc. of the IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, Mexico, 2013, pp. 2207-2214.
  3. Akhmedova, Sh., Semenkin, E., 2014. Co-Operation of Biology Related Algorithms Meta-Heuristic in ANNBased Classifiers Design. In: Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2014. -accepted to publication.
  4. Bukhtoyarov, V., Semenkin, E., Shabalov, A., 2012. Neural Networks Ensembles Approach for Simulation of Solar Arrays Degradation Process. In Proc. of Hybrid artificial intelligent systems 7th International Conference, HAIS 2012, Salamanca, Spain, 2012, pp. 186-195.
  5. Eiben, A. E., Smith, J. E., 2003. Introduction to Evolutionary Computing. Springer Verlag, 2003, 299p.
  6. Finck, S. et al., 2009. Real-parameter black-box optimization benchmarking 2009. In: Presentation of the noiseless functions. Technical Report Researh Center PPE.
  7. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. In Proc. of IEEE International Conference on Neural networks, IV, 1995, pp. 1942- 1948.
  8. Kennedy, J., Eberhart, R.,1997. A discrete binary version of the particle swarm algorithm. In Proc. of the World Multiconference on Systemics, Cybernetics and Informatics, Piscataway, NJ, 1997, pp. 4104-4109.
  9. Liang, J. J., Qu, B. Y., Suganthan, P. N., Hernandez-Diaz, A. G, 2013. Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on RealParameter Optimization. In Technical Report 2012, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China, and Technical Report, Nanyang Technological University, Singapore.
  10. O'Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W., 2010. Open issues in genetic programming. In: Genetic Programming and Evolvable Machines 11, 2010, pp. 339-363.
  11. Panfilov, I. A., Semenkin, E. S., Semenkina, M. E., 2012. Neural Network Ensembles Design with SelfConfiguring Genetic Programming Algorithm for Solving Computer Security Problems. In: Computational Intelligence in Security for Information Systems, Advances in Intelligent Systems and Computing 189, Springer-Verlag, Berlin Heidelberg, 2012, pp. 25-32.
  12. Poli, R., Langdon, W. B., McPhee, N. F., 2008. A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gpfield-guide.org.uk, 2008. (With contributions by J. R. Koza).
  13. Semenkin, E., Semenkina, M., 2012. Self-Configuring Genetic Programming Algorithm with Modified Uniform Crossover Operator. In: Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2012, pp. 1918-1923.
  14. Semenkin, E. S., Semenkina, M. E., 2012. Selfconfiguring Genetic Algorithm with Modified Uniform Crossover Operator. Advances in Swarm Intelligence, Lecture Notes in Computer Science 7331, Springer-Verlag, Berlin Heidelberg, 2012, pp. 414- 421.
  15. Shabalov, A., Semenkin, E., Galushin, P., 2012. Integration of Intelligent Information Technologies Ensembles for Modeling and Classification. In Proc. of Hybrid artificial intelligent systems 7th International Conference, HAIS 2012, Salamanca, Spain, 2012, pp. 365-374.
  16. Yang, C., Tu, X., Chen, J., 2007. Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search. In Proc. of the International Conference on Intelligent Pervasive Computing, 2007, pp. 462-467.
  17. Yang, X. S., 2009. Firefly algorithms for multimodal optimization. In Proc. of the 5th Symposium on Stochastic Algorithms, Foundations and Applications, 2009, pp. 169-178.
  18. Yang, X. S., Deb, S., 2009. Cuckoo Search via Levy flights. In Proc. of the World Congress on Nature & Biologically Inspired Computing, IEEE Publications, 2009, pp. 210-214.
  19. Yang, X. S., 2010. A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, Vol. 284, 2010, pp. 65-74.
  20. Zhang G., Patuwo B. E., Hu M. Y., 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14, 1998, pp. 35- 62.
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Paper Citation


in Harvard Style

Semenkina M., Akhmedova S., Semenkin E. and Ryzhikov I. (2014). Spacecraft Solar Arrays Degradation Forecasting with Evolutionary Designed ANN-based Predictors . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 421-428. DOI: 10.5220/0005122004210428


in Bibtex Style

@conference{icinco14,
author={Maria Semenkina and Shakhnaz Akhmedova and Eugene Semenkin and Ivan Ryzhikov},
title={Spacecraft Solar Arrays Degradation Forecasting with Evolutionary Designed ANN-based Predictors},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={421-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005122004210428},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Spacecraft Solar Arrays Degradation Forecasting with Evolutionary Designed ANN-based Predictors
SN - 978-989-758-039-0
AU - Semenkina M.
AU - Akhmedova S.
AU - Semenkin E.
AU - Ryzhikov I.
PY - 2014
SP - 421
EP - 428
DO - 10.5220/0005122004210428