Chaotic Time Series Prediction using Correlation Dimension and Adaptive Neuro-Fuzzy Inference System

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated. . Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.