Prediction Using Cuckoo Search Optimized Echo State Network

  • Abubakar BalaEmail author
  • Idris Ismail
  • Rosdiazli Ibrahim
  • Sadiq M. Sait
  • Hamza Onoruoiza Salami
Research Article - Computer Engineering and Computer Science


The advent of internet of things has brought a revolution in the amount of data generated in industry. Researchers now have to develop ways to harness such huge amount of data. Thus, a new method called “predictive maintenance” was developed. In this technique, sensor data is used to predict failures so that appropriate actions are taken to save accidents and costs. Artificial neural networks have proven to be excellent tools for prediction. In this work, the echo state network (ESN), which is a new concept of recurrent neural network (RNN), is used to predict failures in turbofan engines. The ESN was developed to solve the complexities of earlier RNNs. However, choosing the right topology and parameters for the ESN is often a difficult problem. Hence, we develop a cuckoo search optimization-based algorithm to optimize the ESN. The approach is compared with three particle swarm optimization methods and two other methods, and it performed better.


Algorithms Artificial neural networks Artificial intelligence Cuckoo search Echo state network Lévy flight Prediction Turbofan engine 



The authors recognize the support of Universiti Teknologi PETRONAS (UTP), Malaysia. Appreciation also goes to King Fahd University of Petroleum & Minerals for their support.


  1. 1.
    Vichare, N.M.; Pecht, M.G.: Prognostics and health management of electronics. IEEE Trans. Compon. Packag. Technol. 29(1), 222–229 (2006)CrossRefGoogle Scholar
  2. 2.
    Saxena, A.; Goebel, K.; Simon, D.; Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: International Conference on Prognostics and Health Management, 2008. PHM 2008, pp. 1–9. IEEE (2008)Google Scholar
  3. 3.
    Verstraeten, D.; Schrauwen, B.; d’Haene, M.; Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ferreira, A.A.; Ludermir, T.B.; De Aquino, R.R.B.: An approach to reservoir computing design and training. Expert Syst. Appl. 40(10), 4172–4182 (2013)CrossRefGoogle Scholar
  5. 5.
    Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks—with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148(34), 13 (2001)Google Scholar
  6. 6.
    Maass, W.; Natschläger, T.; Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Steil, J.J: Backpropagation–decorrelation: online recurrent learning with O(N) complexity. In: 2004 IEEE International Joint Conference on Neural Networks, 2004. Proceedings, vol. 2, pp. 843–848. IEEE (2004)Google Scholar
  8. 8.
    Sait, S.M.; Youssef, S.M.: Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization problems. Wiley, Hoboken (1999)zbMATHGoogle Scholar
  9. 9.
    Bala, A.; Ismail, I.; Ibrahim, R.; Sait, S.M.: Applications of metaheuristics in reservoir computing techniques: a review. IEEE Access 6, 58012–58029 (2018)CrossRefGoogle Scholar
  10. 10.
    Ferreira, A.A; Ludermir, T.B.: Genetic algorithm for reservoir computing optimization. In: International Joint Conference on Neural Networks, 2009. IJCNN 2009, pp. 811–815. IEEE (2009)Google Scholar
  11. 11.
    Ma, Q.; Shen, L.; Cottrell, G.W: Deep-esn: a multiple projection-encoding hierarchical reservoir computing framework (2017). arXiv preprint arXiv:1711.05255
  12. 12.
    Zhong, S.; Xie, X.; Lin, L.; Wang, F.: Genetic algorithm optimized double-reservoir echo state network for multi-regime time series prediction. Neurocomputing 238, 191–204 (2017)CrossRefGoogle Scholar
  13. 13.
    Wang, H.; Yan, X.: Optimizing the echo state network with a binary particle swarm optimization algorithm. Knowl Based Syst 86, 182–193 (2015)CrossRefGoogle Scholar
  14. 14.
    Basterrech, S.; Alba, E.; Snášel, V.: An experimental analysis of the echo state network initialization using the particle swarm optimization. In: 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 214–219. IEEE (2014)Google Scholar
  15. 15.
    Chouikhi, N.; Ammar, B.; Rokbani, N.; Alimi, A.M.: PSO-based analysis of echo state network parameters for time series forecasting. Appl. Soft Comput. 55, 211–225 (2017)CrossRefGoogle Scholar
  16. 16.
    Amaya, E.J.; Alvares, A.J.: Prognostic of RUL based on echo state network optimized by artificial bee colony. Int. J. Progn. Health Manag. 7, 12 (2016)Google Scholar
  17. 17.
    Yang, X.-S.; Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, 2009. NaBIC 2009, pp. 210–214. IEEE (2009)Google Scholar
  18. 18.
    Bala, A.; Ismail, I.; Ibrahim, R.: Cuckoo search based optimization of echo state network for time series prediction. In: 2018 International Conference on Intelligent and Advanced System (ICIAS), pp. 1–6. IEEE (2018)Google Scholar
  19. 19.
    Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, vol. 5. GMD-Forschungszentrum Informationstechnik, Bonn (2002)Google Scholar
  20. 20.
    Jaeger, H.; Lukoševičius, M.; Popovici, D.; Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335–352 (2007)CrossRefzbMATHGoogle Scholar
  21. 21.
    Dybiec, B.; Gudowska-Nowak, E.; Barkai, E.; Dubkov, A.A.: Lévy flights versus Lévy walks in bounded domains. Phys. Rev. E 95(5), 052102 (2017)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Rhee, I.; Shin, M.; Hong, S.; Lee, K.; Kim, S.J.; Chong, S.: On the Lévy-walk nature of human mobility. IEEE/ACM Trans. Netw. (TON) 19(3), 630–643 (2011)CrossRefGoogle Scholar
  23. 23.
    Chiroma, H.; Herawan, T.; Fister Jr., I.; Fister, I.; Abdulkareem, S.; Shuib, L.; Hamza, M.F.; Saadi, Y.; Abubakar, A.: Bio-inspired computation: recent development on the modifications of the cuckoo search algorithm. Appl. Soft Comput. 61, 149–173 (2017)CrossRefGoogle Scholar
  24. 24.
    Walton, S.; Hassan, O.; Morgan, K.; Brown, M.R.: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9), 710–718 (2011)CrossRefGoogle Scholar
  25. 25.
    Sait, S.M.; Bala, A.; El-Maleh, A.H.: Cuckoo search based resource optimization of datacenters. Appl. Intell. 44(3), 489–506 (2016)CrossRefGoogle Scholar
  26. 26.
    El-Maleh, A.H.; Sait, S.M.; Bala, A.: State assignment for area minimization of sequential circuits based on cuckoo search optimization. Comput. Electr. Eng. 44, 13–23 (2015)CrossRefGoogle Scholar
  27. 27.
    Mathew, V.; Toby, T.; Singh, V.; Rao, B.M.; Kumar, M.G.: Prediction of remaining useful lifetime (RUL) of turbofan engine using machine learning. In: 2017 IEEE International Conference on Circuits and Systems (ICCS), pp. 306–311. IEEE (2017)Google Scholar
  28. 28.
    Ramasso, E.; Saxena, A.: Performance benchmarking and analysis of prognostic methods for C-MAPSS datasets. Int. J. Progn. Health Manag. 5(2), 1–15 (2014)Google Scholar
  29. 29.
    Li, X.; Ding, Q.; Sun, J.-Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf. 172, 1–11 (2018)CrossRefGoogle Scholar
  30. 30.
    Heimes, F.O.: Recurrent neural networks for remaining useful life estimation. In: International Conference on Prognostics and Health Management, 2008. PHM 2008, pp. 1–6. IEEE (2008)Google Scholar
  31. 31.
    Zhang, C.; Lim, P.; Qin, A.K.; Tan, K.C.: Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2306–2318 (2017)CrossRefGoogle Scholar
  32. 32.
    Ramasso, E.: Investigating computational geometry for failure prognostics. Int. J. Progn. Health Manag. 5(1), 005 (2014)Google Scholar
  33. 33.
    Zhao, Z.; Liang, B.; Wang, X.; Weining, L.: Remaining useful life prediction of aircraft engine based on degradation pattern learning. Reliab. Eng. Syst. Saf. 164, 74–83 (2017)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringUniversiti Teknologi PETRONAS, MalaysiaBandar Seri IskandarMalaysia
  2. 2.Electrical Engineering DepartmentBayero University KanoKanoNigeria
  3. 3.Computer Engineering Department and Center for Communications and IT Research, Research InstituteKing Fahd University of Petroleum & MineralsDhahranSaudi Arabia
  4. 4.College of Computer Science and EngineeringUniversity of Hafr AlbatinHafar Al BatinSaudi Arabia

Personalised recommendations