Title :
Neuro fuzzy model with singular value decomposition for forecasting the number of train passengers in Yogyakarta
Author :
Abadi, A.M. ; Wutsqa, D.U.
Author_Institution :
Math. Educ. Dept., Yogyakarta State Univ., Yogyakarta, Indonesia
Abstract :
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.
Keywords :
forecasting theory; fuzzy neural nets; least squares approximations; matrix algebra; optimisation; railways; singular value decomposition; time series; PT Kereta Api Indonesia; Persero; Yogyakarta; forecasting accuracy; least square method; matrix; neural network; neuro fuzzy model; one order Takagi Sugeno Kang fuzzy model; operating region VI; optimization; singular value decomposition method; time series forecasting; train passengers forecasting; Data models; Forecasting; Noise measurement; Predictive models; Singular value decomposition; Time series analysis; Training data; least square method; neuro fuzzy model; one order Takagi Sugeno Kang fuzzy model; singular value decomposition; train passenger;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5147-5
DOI :
10.1109/FSKD.2014.6980828
