Skip to main content

Optimal Type-3 Fuzzy Systems and Ensembles of Neural Networks Using the Firefly Algorithm

  • Chapter
  • First Online:
Type-3 Fuzzy Logic in Time Series Prediction

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

  • 16 Accesses

Abstract

In this chapter, the COVID-19 information is employed to perform times series prediction. We put forward the optimal design of ensemble neural networks (ENNs).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Z. Jin, J.-Y. Liu, R. Feng, L. Ji, Z.-L. Jin, H.-B. Li, Drug treatment of coronavirus disease 2019 (COVID-19) in China. Eur. J. Pharmacol.Pharmacol. 883, 1–7 (2020)

    Google Scholar 

  2. Q. Zhang, Y. Wei, M. Chen, Q. Wan, X. Chen, Clinical analysis of risk factors for severe COVID-19 patients with type 2 diabetes. J. Diabetes Complicat. 34(10), 1–5 (2020)

    Article  Google Scholar 

  3. P. Melin, J. Monica, D. Sánchez, O. Castillo, Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps. Chaos Solitons Fractals 138, 1–7 (2020)

    Article  Google Scholar 

  4. D. Reddy, V. Atam, P. Rai, F. Khan, S. Pandey, H. Malhotra, K. Gupta, S. Sonkar, R. Verma, COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: a lesson from Northern India. Clin. Epidemiol. Health 15, 1–6 (2022)

    Google Scholar 

  5. L. Zha, T. Sobue, A. Hirayama, T. Takeuchi, K. Tanaka, Y. Katayama, S. Komukai, T. Shimazu, T. Kitamura, COVID-19 epidemiology research group, “Characteristics and outcomes of COVID-19 in reproductive-aged pregnant and nonpregnant women in Osaka, Japan.” Int. J. Infect. Dis. 117, 195–200 (2022)

    Article  Google Scholar 

  6. L. Reyes, A. Rodriguez, A. Bastidas, D. Parra-Tanoux, Y.V. Fuentes, E. García-Gallo, G. Moreno, G. Ospina-Tascon, G. Hernandez, E. Silva, A.M. Díaz, M. Jibaja, M. Vera, E. Díaz, M. Bodí, J. Solé-Violán, R. Ferrer, A. Albaya-Moreno, L. Socias, Á. Estella, A. Loza-Vazquez, R. Jorge-García, I. Sancho, I. Martin-Loeches, Dexamethasone as risk-factor for ICU-acquired respiratory tract infections in severe COVID-19. J. Crit. Care 69, 1–8 (2022)

    Google Scholar 

  7. D. Liu, W. Ding, Z. Dong, W. Pedrycz, Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread. Comput. Ind. Eng.. Ind. Eng. 166, 1–17 (2022)

    Google Scholar 

  8. Y. Kuvvetli, M. Deveci, T. Paksoy, H. Garg, A predictive analytics model for COVID-19 pandemic using artificial neural networks. Decis. Anal. J. 1, 1–13 (2021)

    Google Scholar 

  9. H. Verma, S. Mandal, A. Gupta, Temporal deep learning architecture for prediction of COVID-19 cases in India. Expert Syst. Appl. 195, 1–11 (2022)

    Article  Google Scholar 

  10. S. Khalilpourazari, H. Doulabi, A. Çiftçioglu, G. Weber, Gradient-based grey wolf optimizer with Gaussian walk: application in modelling and prediction of the COVID-19 pandemic. Expert Syst. Appl. 177, 1–23 (2021)

    Article  Google Scholar 

  11. M. Pulido, P. Melin, O. Castillo, Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange. Inf. Sci. 280, 188–204 (2014)

    Article  MathSciNet  Google Scholar 

  12. P. Melin, J. Monica, D. Sánchez, O. Castillo, Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: the case of Mexico. Healthcare 8(2), 1–13 (2020)

    Article  Google Scholar 

  13. D. Jia, Z. Wu, Seismic fragility analysis of RC frame-shear wall structure under multidimensional performance limit state based on ensemble neural network. Eng. Struct.Struct. 246, 1–15 (2021)

    Google Scholar 

  14. I. Wilkinson, R. Bhattacharjee, J. Shafer, A. Osborne, Confidence estimation in the prediction of epithermal neutron resonance self-shielding factors in irradiation samples using an ensemble neural network. Energy AI 7, 1–19 (2022)

    Article  Google Scholar 

  15. P. Melin, D. Sánchez, J. Monica, O. Castillo, Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction. Soft. Comput.Comput. 1, 1–38 (2021)

    Google Scholar 

  16. Z. Liu, A. Mohammadzadeh, H. Turabieh, M. Mafarja, S. Band, A. Mosavi, A new online learned interval type-3 fuzzy control system for solar energy management systems. IEEE Access 9, 10498–10508 (2021)

    Article  Google Scholar 

  17. S. Qasem, A. Ahmadian, A. Mohammadzadeh, S. Rathinasamy, B. Pahlevanzadeh, A type-3 logic fuzzy system: optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size. Inf. Sci. 572, 424–443 (2021)

    Article  MathSciNet  Google Scholar 

  18. Y. Cao, A. Raise, A. Mohammadzadeh, S. Rathinasamy, S. Band, A. Mosavi, Deep learned recurrent type-3 fuzzy system: application for renewable energy modeling/prediction. Energy Rep. 7, 8115–8127 (2021)

    Article  Google Scholar 

  19. S. Hanandeh, Introducing mathematical modeling to estimate pavement quality index of flexible pavements based on genetic algorithm and artificial neural networks. Case Stud. Constr. Mater. 16, 1–13 (2022)

    Google Scholar 

  20. V. Tam, A. Butera, K. Le, L. Da Silva, A. Evangelista, A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks. Constr. Build. Mater. 324, 1–13 (2022)

    Article  Google Scholar 

  21. C. Aggarwal, Neural Networks and Deep Learning: A Textbook, 1st edn. (Springer, 2018)

    Google Scholar 

  22. B. Peng, L. Tong, D. Yan, W. Huo, Experimental research and artificial neural network prediction of free piston expander-linear generator. Energy Rep. 8, 1966–1978 (2022)

    Article  Google Scholar 

  23. K. Prakarsha, G. Sharma, Time series signal forecasting using artificial neural networks: An application on ECG signal. Biomed. Signal Process. Control 76, 1–10 (2022)

    Google Scholar 

  24. K. Gurney, An Introduction to Neural Networks, 1st edn. (CRC Press, 1997)

    Google Scholar 

  25. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edn. (Prentice Hall, 1998).

    Google Scholar 

  26. L. Zadeh, Fuzzy sets. Inf. Control. 8(3), 338–353 (1965)

    Article  Google Scholar 

  27. L. Zadeh, The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  Google Scholar 

  28. L. Zadeh, Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft. Comput.Comput. 2, 23–25 (1998)

    Article  Google Scholar 

  29. P. Melin, O. Castillo, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn. (Springer, 2005)

    Google Scholar 

  30. H. Al-Jamimi, T. Saleh, Transparent predictive modelling of catalytic hydrodesulfurization using an interval type-2 fuzzy logic. J. Clean. Prod. 231, 1079–1088 (2019)

    Article  Google Scholar 

  31. P. Melin, O. Castillo, A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl. Soft Comput.Comput. 21, 568–577 (2014)

    Article  Google Scholar 

  32. J. Rickard, J. Aisbett, G. Gibbon, Fuzzy subsethood for fuzzy sets of type-2 and generalized type-n. IEEE Trans. Fuzzy Syst. 17(1), 50–60 (2009)

    Article  Google Scholar 

  33. A. Mohammadzadeh, M. Sabzalian, W. Zhang, An interval type-3 fuzzy system and a new online fractional-order learning algorithm: theory and practice. IEEE Trans. Fuzzy Syst. 28(9), 1940–1950 (2020)

    Article  Google Scholar 

  34. O. Castillo, J. Castro, P. Melin, Interval Type-3 Fuzzy Systems: Theory and Design (Springer, 2022)

    Google Scholar 

  35. X. Yang, Firefly algorithms for multimodal optimization, in Proceeding 5th Symposium on Stochastic Algorithms, Foundations and Applications, vol. 5792, (2009), pp. 169–178

    Google Scholar 

  36. X. Yang, X. He, Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)

    Google Scholar 

  37. Z. Chen, A. Ashkezari, I. Tlili, Applying artificial neural network and curve fitting method to predict the viscosity of SAE50/MWCNTs-TiO2 hybrid nanolubricant. Phys. A Stat. Mech. Appl. 549, 1–11 (2020)

    Article  Google Scholar 

  38. Z.-G. Che, T.-A. Chiang, Z.-H. Che, Feed-forward neural networks training: a comparison between genetic algorithm and back-propagation learning algorithm. Int. J. Innov. Comput. Inf. Control 7(10), 5839–5850 (2011)

    Google Scholar 

  39. J. Gauthier, P. Micheau, Feedfoward and feedback adaptive controls for continuously variable transmissions. IFAC Proc. Vol. 45(16), 1460–1465 (2012)

    Article  Google Scholar 

  40. Y. An, K. Yoo, M. Na, Y.-S. Kim, Critical flow prediction using simplified cascade fuzzy neural networks. Ann. Nucl. Energy 136, 1–11 (2020)

    Article  Google Scholar 

  41. Ü. Budak, Y. Guo, E. Tanyildizi, A. Şengür, Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. Med. Hypotheses 134, 1–8 (2020)

    Article  Google Scholar 

  42. M. Pulido, P. Melin, Optimization of ensemble neural networks with type-2 fuzzy integration of responses for the Dow Jones time series prediction. Intell. Autom. Soft Comput. 20, 403–418 (2014)

    Article  Google Scholar 

  43. D. Sánchez, P. Melin, O. Castillo, Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. Artif. Intell.Intell. 64, 172–186 (2017)

    Article  Google Scholar 

  44. The Humanitarian Data Exchange (HDX). (2022, April). https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases

  45. F. Valdez, P. Melin, O. Castillo, Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making, in IEEE International Conference on Fuzzy Systems, (2009), pp. 2114–2119

    Google Scholar 

  46. F. Valdez, J.C. Vazquez, P. Melin, O. Castillo, Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl. Soft Comput.Comput. 52, 1070–1083 (2017)

    Article  Google Scholar 

  47. O. Castillo, E. Lizarraga, J. Soria, P. Melin, F. Valdez, New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. Inf. Sci. 294, 203–215 (2015)

    Article  MathSciNet  Google Scholar 

  48. L. Amador-Angulo, O. Mendoza, J.R. Castro, A. Rodriguez-Diaz, P. Melin, O. Castillo, Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors 16(9), 1458 (2016)

    Article  Google Scholar 

  49. F. Valdez, H. Carreon-Ortiz, O. Castillo, CMOA—Continuous Mycorrhiza Optimization Algorithm, in Mycorrhiza Optimization Algorithm. SpringerBriefs in Applied Sciences and Technology. (Springer, Cham, 2023). https://doi.org/10.1007/978-3-031-47369-2_5

  50. F. Valdez, H. Carreon-Ortiz, O. Castillo, DMOA—Discrete Mycorrhiza Optimization Algorithm, in Mycorrhiza Optimization Algorithm. SpringerBriefs in Applied Sciences and Technology. (Springer, Cham, 2023). https://doi.org/10.1007/978-3-031-47369-2_6

  51. E. Ontiveros, P. Melin, O. Castillo, Comparative study of interval type-2 and general type-2 fuzzy systems in medical diagnosis. Inf. Sci. 525, 37–53 (2020)

    Article  MathSciNet  Google Scholar 

  52. J.R. Castro, O. Castillo, P. Melin, A. Rodriguez-Diaz, Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox. Trans. Comput. Sci. I, 104–114 (2008).

    Google Scholar 

  53. D. Sanchez, P. Melin, O. Castillo, A grey wolf optimizer for modular granular neural networks for human recognition. Comput. Intell. Neurosci. 2017, 4180510:1–4180510:26 (2017)

    Google Scholar 

  54. O. Castillo, P. Melin, Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach. Appl. Soft Comput.Comput. 3(4), 363–378 (2003)

    Article  Google Scholar 

  55. M.H.F. Zarandi, A.A.S. Asl, S. Sotudian, O. Castillo, A state of the art review of intelligent scheduling. Artif. Intell. Rev.. Intell. Rev. 53, 501–593 (2020)

    Article  Google Scholar 

  56. H.I. Seker, S. Kacar, O. Castillo, S. Uzun, I. Pehlivan, Z. Tatli, Detection of resistance spot welding faults in copper materials by transfer learning method. Appl. Comput. Math. 22(3), 430–445 (2023). https://doi.org/10.30546/1683-6154.22.3.2023.430

  57. F. Valdez, O. Castillo, P. Cortes-Antonio, P. Melin, Applications of intelligent optimization algorithms and fuzzy logic systems in aerospace: a review. Appl. Comput. Math. 21(3), 233–245 (2022). https://doi.org/10.30546/1683-6154.21.3.2022.233

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oscar Castillo .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Castillo, O., Melin, P. (2024). Optimal Type-3 Fuzzy Systems and Ensembles of Neural Networks Using the Firefly Algorithm. In: Type-3 Fuzzy Logic in Time Series Prediction. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-031-59714-5_7

Download citation

Publish with us

Policies and ethics