Evolving Systems

, Volume 7, Issue 2, pp 107–116 | Cite as

Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

  • Yevgeniy V. Bodyanskiy
  • Oleksii K. Tyshchenko
  • Daria S. Kopaliani
Original Paper


This paper proposes an architecture and learning algorithms for a cascade neo-fuzzy system based on pools of extended neo-fuzzy neurons. The proposed system is different from existing cascade systems in its capability to operate in an online mode, which allows it to work with non-stationary and stochastic non-linear chaotic signals that come in the form of data streams. A new pool optimization procedure is introduced. Compared to conventional analogues, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.


Evolving system Learning method Extended neo-fuzzy neuron Online generalization Cascade network  Data stream 


  1. Aggarwal C (2007) Data streams: models and algorithms (advances in database systems). Springer, New YorkCrossRefGoogle Scholar
  2. Angelov P (2013) Autonomous learning systems: from data streams to knowledge in real-time. Wiley, ChichesterGoogle Scholar
  3. Angelov P, Filev D (2004) An approach to online identification of takagi-sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B Cybern 34(1):484–498CrossRefGoogle Scholar
  4. Angelov P, Lughofer E (2008) Data-driven evolving fuzzy systems using ets and flexfis: comparative analysis. Int J Gen Syst 37(1):45–67CrossRefMATHGoogle Scholar
  5. Angelov P, Zhou X (2006) Evolving fuzzy systems from data streams in real-time. In: 2006 international symposium on evolving fuzzy systems, pp 29–35Google Scholar
  6. Angelov P, Filev D, Kasabov N (2010) Evolving intelligent systems: methodology and applications. Wiley, New YorkCrossRefGoogle Scholar
  7. Arrow K, Hurwicz L, Uzawa H (1958) Studies in linear and non-linear programming. Stanford University Press, StanfordMATHGoogle Scholar
  8. Avedjan E, Barkan G, Levin I (1999) Cascade neural networks. Avtom i telemekhanika 3:38–55MathSciNetGoogle Scholar
  9. Bifet A (2010) Adaptive stream mining: pattern learning and mining from evolving data streams. IOS Press, AmsterdamMATHGoogle Scholar
  10. Bodyanskiy Y (2005) Computational intelligence techniques for data analysis. Lect Notes Informat 72:15–36Google Scholar
  11. Bodyanskiy Y, Kolodyazhniy V (2010) Cascaded multi-resolution spline-based fuzzy neural network. In: Int. symp. on evolving intelligent systems, pp 26–29Google Scholar
  12. Bodyanskiy Y, Pliss I (1990) Adaptive generalized forecasting of multivariate stochastic signals. In: Latvian sign. proc. int. conf., vol 2, pp 80–83Google Scholar
  13. Bodyanskiy Y, Viktorov Y (2009a) The cascaded neo-fuzzy architecture and its on-line learning algorithm. Intell Process 9:110–116Google Scholar
  14. Bodyanskiy Y, Viktorov Y (2009b) The cascaded neo-fuzzy architecture using cubic-spline activation functions. Inf Theor Appl 16(3):245–259Google Scholar
  15. Bodyanskiy Y, Vorobyov S (2000) Recurrent neural network detecting changes in the properties of nonlinear stochastic sequences. Autom Remote Control 61(7):1113–1124Google Scholar
  16. Bodyanskiy Y, Madjarov N, Pliss I (1983) Adaptive forecasting of nonstationary processes. Avtom I Izchislitelna Tekh 6:5–12Google Scholar
  17. Bodyanskiy Y, Pliss I, Solovyova T (1986) Multistep optimal predictors of multidimensional non-stationary stochastic processes. Dokl AN USSR A 12:47–49Google Scholar
  18. Bodyanskiy Y, Pliss I, Solovyova T (1989) Adaptive generalized forecasting of multidimensional stochastic sequences. Dokl AN USSR A 9:73–75Google Scholar
  19. Bodyanskiy Y, Stephan A, Vorobyov S (1999) Algorithm for adaptive identification of dynamical parametrically nonstationary objects. J Comput Syst Sci Int 38(1):14–38Google Scholar
  20. Bodyanskiy Y, Cichocki A, Vorobyov S (2001) An adaptive noise cancellation for multisensory signals. Fluct Noise Lett 1(1):13–24Google Scholar
  21. Bodyanskiy Y, Kokshenev I, Kolodyazhniy V (2003) An adaptive learning algorithm for a neo-fuzzy neuron. In: Int. conf. of European union soc. for fuzzy logic and technology, pp 375–379Google Scholar
  22. Bodyanskiy Y, Tyshchenko O, Kopaliani D (2015a) An extended neo-fuzzy neuron and its adaptive learning algorithm. Int J Intell Syst Appl (IJISA) 7(2):21–26Google Scholar
  23. Bodyanskiy Y, Tyshchenko O, Kopaliani D (2015b) A hybrid cascade neural network with an optimized pool in each cascade. Soft Comput 19(12):3445–3454CrossRefGoogle Scholar
  24. Chan L, Fallside F (1987) An adaptive learning algorithm for backpropagation networks. Comput Speech Lang 2:205–218CrossRefGoogle Scholar
  25. Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Teubner, StuttgartMATHGoogle Scholar
  26. Fahlman S, Lebiere C (1990) The cascade-correlation learning architecture. Adv Neural Inf Process Syst 2:524–532Google Scholar
  27. Fister I, Fister I (2015) Adaptation and hybridization in computational intelligence. Springer, BerlinCrossRefMATHGoogle Scholar
  28. Haykin S (1999) Neural networks. A comprehensive foundation. Prentice Hall, Upper Saddle RiverMATHGoogle Scholar
  29. Holmes G, Veitch A (1991) A modified quickprop algorithm. Neural Comput 3:310–311CrossRefGoogle Scholar
  30. Jang JSR, Sun CT, Muzutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle RiverGoogle Scholar
  31. Juang CF, Lin CT (1998) An online self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6(1):12–32CrossRefGoogle Scholar
  32. Kacprzyk J, Pedrycz W (2015) Springer handbook of computational intelligence. Springer, BerlinCrossRefMATHGoogle Scholar
  33. Kasabov N (2001) Ensembles of efunns: an architecture for a multimodule classifier. In: Proc. int. conf. on fuzzy systems, vol 3, pp 1573–1576Google Scholar
  34. Kasabov N (2003) Evolving connectionist systems. Springer, LondonCrossRefMATHGoogle Scholar
  35. Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach. Springer, LondonMATHGoogle Scholar
  36. Kasabov N, Song Q (2002) Denfis: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154Google Scholar
  37. Kruse R, Borgelt C, Klawonn F, Moewes C, Steinbrecher M, Held P (2013) Computational intelligence. A methodological introduction. Springer, LondonMATHGoogle Scholar
  38. Kuremoto T, Obayashi M, Kobayashi K (2005a) Forecasting time series by sofnn with reinforcement learning. In: Advances in intelligent computing, international conference on intelligent computing (ICIC’05), Hefei. Proceedings, Part I, pp 1085–1094Google Scholar
  39. Kuremoto T, Obayashi M, Kobayashi K (2005b) Nonlinear prediction by reinforcement learning. In: Lecture notes in computer science, pp 1085–1094Google Scholar
  40. Ljung L (1999) System identification: theory for the user. Prentice Hall PTR, Upper Saddle RiverCrossRefMATHGoogle Scholar
  41. Lughofer E (2008) Flexfis: a robust incremental learning approach for evolving TS fuzzy models. IEEE Trans Fuzzy Syst 16(6):1393–1410CrossRefGoogle Scholar
  42. Lughofer E (2011) Evolving fuzzy systems and methodologies, advanced concepts and applications. Springer, BerlinCrossRefMATHGoogle Scholar
  43. Mumford C, Jain L (2009) Computational intelligence. Collaboration, fusion and emergence. Springer, BerlinMATHGoogle Scholar
  44. Pratama M, Anavatti S, Angelov P, Lughofer E (2014) Panfis: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68CrossRefGoogle Scholar
  45. Prechelt L (1997) Investigation of the cascor family of learning algorithms. Neural Netw 10:885–896CrossRefGoogle Scholar
  46. Rong NJ, Huang GB, Saratchandran P (2006) Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260–1275MathSciNetCrossRefMATHGoogle Scholar
  47. Rutkowski L (2008) Computational intelligence. Methods and techniques. Springer, BerlinCrossRefMATHGoogle Scholar
  48. Schalkoff R (1997) Artificial neural networks. The McGraw-Hill Comp., New YorkMATHGoogle Scholar
  49. Shawkat AA, Xiang Y (2009) Dynamic and advanced data mining for progressing technological development: innovations and systemic approaches. Hershey, New YorkGoogle Scholar
  50. Silva FM, Almeida LB (1990) Speeding up backpropagation. In: Eckmiller R (ed) Advanced neural computers. North-Holland, pp 151–158Google Scholar
  51. Wang L (1994) Adaptive fuzzy systems and control. Design and stability analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar
  52. Wang L, Mendel J (1993) Fuzzy basis functions, universal approximation and orthogonal least squares learning. IEEE Trans Neural Netw 3:807–814CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Control Systems Research LaboratoryKharkiv National University of Radio ElectronicsKharkivUkraine

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