Real-Time Identification and Forecasting of Chaotic Time Series Using Hybrid Systems of Computational Intelligence

  • Yevgeniy Bodyanskiy
  • Vitaliy Kolodyazhniy
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 187)


In this chapter, the problems of identification, modeling, and forecasting of chaotic signals are discussed. These problems are solved with the use of the conventional techniques of computational intelligence as radial basis neural networks and learning neuro-fuzzy architectures, as well as novel hybrid structures based on the Kolmogorov’s superposition theorem and using the neo-fuzzy neurons as elementary processing units. The need for the solution of the forecasting problem in real time poses higher requirements to the processing speed, so the considered hybrid structures can be trained with the proposed algorithms having high convergence rate and providing a compromise between the smoothing and tracking properties during the processing of nonstationary noisy signals.


Membership Function Fuzzy System Fuzzy Rule Radial Basis Function Network Hurst Exponent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yevgeniy Bodyanskiy
  • Vitaliy Kolodyazhniy

There are no affiliations available

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