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

机译：具有奇异价值分解的神经模糊模型预测日惹火车乘客数量

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摘要

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.
机译：神经模糊模型是一种模型，它结合了模糊和神经网络，这已应用于时间序列预测。奇异值分解方法可用于基于基质的奇异值来优化神经模糊模型。本研究旨在通过应用具有奇异值分解的神经模糊模型，预测PT Kereta API印度尼西亚（Pererea API Indonesia（Persero）操作区域VI Yogyakarta的乘客数量。将所提出的模型的预测精度与Takagi Sugeno Kang模糊模型的预测精度和神经模糊的比较进行了比较。结果表明，具有奇异值分解的神经模糊模型比测试数据的其他两种模型更准确，但在训练数据上没有更好。

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