A Novel Fuzzy Based Method for Heart Rate Variability Prediction

In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. The learning process in this method is similar to the conventional gradient descent learning process, except that the input patterns and parameters are stored in memory as a look-up table after upgrade. In the testing phase according to input patterns, the nearest neighbors and the weights corresponding to the test pattern, similar patterns are extracted from memory. Eventually, by extracted weights and input pattern, prediction is performed. In order to validate the proposed method for predicting, the Mackey-Glass, Lorenz and biological Heart Rate Variability (HRV) time series is used. Finally, the results of proposed method with the conventional methods of time-series prediction are also compared. The results demonstrate the capability of proposed method in chaotic time series prediction.