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In this paper, a novel committee of wavelet and recurrent neural networks to predict the next hour, 24 hour and one-week-ahead load is addressed. Using wavelet multiresolution analysis, the load series are decomposed to different sub-series, which show the different frequency characteristics of the load. Different recurrent neural networks are optimally designed and developed to predict each sub-series...
The article develops a wavelet neural network for trip chaining pattern recognition. Based on the data obtained from Beijing Resident Trip Survey, a set of socioeconomic and demographic factors related to the of traveller situation which potentially influence trip-chaining patterns are selected as input variables of neural network, and a categorical trip chaining pattern (simple and complex trip chaining)...
Based on the nonlinear ensemble and level dependent denoising framework, a novel wavelet denoising support vector regression (SVR) ensemble forecasting model is proposed. The proposed model attempts to incorporate the level dependent denoising technique that utilizes the multi scale heterogeneous characteristics of data and noises into the modeling process. Forecasting results based on different wavelet...
This paper proposes a novel multi scale nonlinear ensemble methodology for analyzing and modeling the complex exchange rate behaviors. Using several techniques integrated under the proposed unified framework, it deals with data characteristics such as autocorrelation, multi scale heterogeneity and parameter instability during the modeling process. The multi scale heterogeneity property is modeled...
Wavelet transformation is performed on NIR transmittance and Raman spectroscopic data followed by prediction of active substance content of pharmaceutical tablet from the spectral data. Partial least squares regression (PLSR) is used to build the prediction models. Comparison is made between prediction models with and without wavelet compression. Results show that wavelet-transformed NIR spectral...
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