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
neuro-fuzzy 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.
Author: Indar Sugiarto,
Saravanakumar Natarajan
Journal Code: jptlisetrogg070003
