ABSTRACT
In this paper, we propose the modeling of chaotic time series measurements, via a sophisticated soft-computing method. Hybrid Fuzzy Inference Systems are thought to encapsulate the advantages of both Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS), by combining the learning capabilities of ANN, with the feature of rules extraction by linguistic interpretation of the variables (FIS). The nonlinear mapping ability of ANFIS is accessed using a well known chaotic time series. Then its effectiveness in short prediction of a time series of streamwise velocities of a turbulent flow, is illustrated utilizing one component Laser Doppler Velocimetry (LDV) data, obtained experimentally.
