A neutron spectrum unfolding code based on generalized regression artificial neural networks

Precise and correct estimation of streamflow is important for the operative progression in water resources systems. The artificial intelligence approaches; such as artificial neural networks (ANN) have been applied for efficiently tackling the hydrological matters like streamflow forecasting in this study at upper Yangtze River. The objective is to investigate the certainty of monthly streamflow by applying artificial neural networks including Generalized Regression Network (GRNN). To overcome the non-linearity problem of streamflow, artificial neural networks integrated with discrete wavelet transform (DWT). Data has been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors (RMSE). It is found that the decomposition technique DWT has ability to improve the forecasting results as compare to single applied artificial neural networks. Moreover, all applied models are separately applies on the peak values as well which also have showed that intergrated model has more ability to catch the peak values

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Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks

Applied Radiation and Isotopes ◽

10.1016/j.apradiso.2009.05.020 ◽

2009 ◽

Vol 67 (10) ◽

pp. 1912-1918 ◽

Cited By ~ 22

Author(s):

A. Sharghi Ido ◽

M.R. Bonyadi ◽

G.R. Etaati ◽

M. Shahriari

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Neutron Spectrum ◽

Artificial Neural

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Detection of Organic Pollutions Using Artificial Neural Networks and Domain Transform Techniques

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.279 ◽

2014 ◽

Vol 610 ◽

pp. 279-282

Author(s):

Ling Gao ◽

Shou Xin Ren

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Packet ◽

Back Propagation ◽

Noise Removal ◽

Generalized Regression Neural Network ◽

Mathematical Methods ◽

Artificial Neural ◽

Generalized Regression

This paper presented a novel method for detection of organic pollutions based on artificial neural networks combining domain transform techniques. Domain transform techniques are mathematical methods that allow the direct mapping of information from one domain to another. The most effectively used domain transform technique is wavelet packet transform (WPT). Wavelet packet representations of signals provided a local timefrequency description and separation ability between information and noise. The quality of the noise removal can be further improved by using best-basis algorithm and thresholding operation. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system. Generalized regression neural network (GRNN) is a kind of ANN and is applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation. In the case a method named WPT-based generalized regression neural network (WPTGRNN) was used for analyzing overlapping spectra.

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