Agus Maman Abadi

Neuro fuzzy model with singular value decomposition for forecasting the number of train passengers in Yogyakarta

The neuro fuzzy model is a model that combines fuzzy and neural network, which has been applied to time series forecasting. A singular value decomposition method can be utilized for optimization of the neuro-fuzzy model based on the singular values of the matrix. This research aims to forecast the number of train passengers of PT Kereta Api Indonesia (Persero) Operating Region VI Yogyakarta by applying the neuro-fuzzy model with singular value decomposition. The forecasting accuracy of the proposed model is compared with those of the one order Takagi Sugeno Kang fuzzy model and the neuro-fuzzy whose optimization is done by the least square method. The results demonstrate that neuro-fuzzy models with singular value decomposition are more accurate than the other two models on testing data but not better on training data.

OPTIMIZATION OF NEURO FUZZY MODEL FOR DATA TIME SERIES WITH SINGULAR VALUE DECOMPOSITION METHOD

This study aims to develop new procedures in optimal neuro fuzzy modeling for time series data. Specifically in this research, the development of new procedure in modeling fuzzy Takagi-Sugeno-Kang order one for time series data which parameter-parametemic determination is done by singular value and neural network decomposition method, in order to obtain method of forming neuro fuzzy model for time series data optimal. In this research, we have developed a procedure to get the optimal Takagi-Sugeno-Kang fuzzy model for time series data by optimizing the parameter value search in consequence of fuzzy rule using singular value decomposition method. A new model of neuro fuzzy modeling is optimized, the fuzzy model whose parametem optimization is based on the neural network by the singular value decomposition method. Parameters in consequent part of the rule of fuzzy are optimized by the singular value decomposition method and the parameters in the antecedent part of the fuzzy rule are optimized based on neural network backpropagation with gradient descent method.

Constructing Fuzzy Time Series Model Using Combination of Table Lookup and Singular Value Decomposition Methods and Its Application to Forecasting Inflation Rate

Fuzzy time series is a dynamic process with linguistic values as its observations. Modelling fuzzy time series data developed by some researchers used discrete membership functions and table lookup method from training data. This paper presents a new method to modelling fuzzy time series data combining table lookup and singular value decomposition methods using continuous membership functions. Table lookup method is used to construct fuzzy relations from training data. Singular value decomposition of firing strength matrix and QR factorization are used to reduce fuzzy relations. Furthermore, this method is applied to forecast inflation rate in Indonesia based on six-factors one-order fuzzy time series. This result is compared with neural network method and the proposed method gets a higher forecasting accuracy rate than the neural network method.