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There is a complicated non-linear relationship between the factors and water demand. General regression neural network (GRNN) was adopted to model the non-linear relationship in the study. The prediction performance of GRNN can vary considerably depending on smoothing parameter. The optimal smoothing parameter is usually determined empirically based on trial-and-error. Particle swarm optimization...
This paper uses wavelet neural networks for flood prediction. It presents an flood prediction model and give an rapid algorithm. The water flux and level are used as input and output variables in the prediction model. The analysis of time-frequency characteristic of wavelet transformation is given. The prediction precision is improved by combining low frequency feature vector with high frequency ones...
Predicting oil price movements is very important for investors. Fuzzy time series which combine people's subjective attitude and objective history values can help people to solve forecasting problems. It has been applied to many areas such as stock index, university enrollments, exchange rates and tourism forecasting. This paper brings fuzzy time series into short term crude oil price forecasting...
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