Parameter Estimation using Least Square Method for MIMO Takagi-Sugeno Neuro-Fuzzy in Time Series Forecasting

Abstract

This paper describes LSE method for improving Takagi-Sugeno neuro-fuzzy model for a multi-input and multi-output system using a set of data (Mackey-Glass chaotic time series). The performance of the generated model is verified using certain set of validation / test data. The LSE method is used to compute the consequent parameters of Takagi-Sugeno neurofuzzy model while mean and variance of Gaussian Membership Functions are initially set at certain values and will be updated using Back Propagation Algorithm. The simulation using Matlab shows that the developed neuro-fuzzy model is capable of forecasting the future values of the chaotic time series and adaptively reduces the amount of error during its training and validation.

Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

“…In the recent 20 years, fuzzy system is one of the most frequently used methods [22][23][24][25]. Fuzzy system has been used for a variety of optimization algorithms.…”