Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

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Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.1116 ◽

2012 ◽

Vol 452-453 ◽

pp. 1116-1120

Author(s):

Hong Ping Li ◽

Hong Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Capillary Electrophoresis ◽

Artificial Neural Networks ◽

Quantitative Analysis ◽

Network Model ◽

Neural Network Model ◽

Network Training ◽

Dissociation Degree ◽

Artificial Neural

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

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Evaluation Model of Decoy Effectiveness Based on Improved GA-BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.1205 ◽

2010 ◽

Vol 108-111 ◽

pp. 1205-1210

Author(s):

Chao He ◽

Ling Li ◽

Peng Liu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Evaluation Model ◽

Extremum Problem ◽

Local Extremum ◽

Neural Network Training ◽

Network Training

When evaluating decoy effectiveness by means of BP neural network, training sometimes failed because of local extremum problem. The genetic algorithm neural network model for evaluating camouflage effectiveness of decoy is created for this purpose. Two steps of evaluating by this method is necessary and a series of index is put forward. After initializing weights and executing genetic operation, we finally retrain the network to get the results which show that the method has fast convergence and the model reliable, effective and objective. This paper is meaningful to camouflage theory and application.

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The Evaluation of Fabric Prickle Based on BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.441.645 ◽

2012 ◽

Vol 441 ◽

pp. 645-650

Author(s):

Tian Tian Li ◽

Jian Zhong Shao ◽

Jin Li Zhou ◽

Tian Zuo Zhang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Subjective Evaluation ◽

Network Training ◽

Bp Neural Network Model ◽

Elastic Gradient

A three-layer BP neural network model was established by relating subjective evaluation of fabric prickle level and 16 objective parameters from KES-FB system. The elastic gradient decrease method was adopted for network training to achieve the preset precision of the model which was later applied to fabric prickle level evaluation. Results from this method gave a considerably accuracy compared with actual subjective results which implied a compatibility between BP neural network and traditional subjective evaluation.

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Optimization the Layout of Airport Noise Monitoring Points Based on Gray Dynamic Neural Network Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.459.615 ◽

2012 ◽

Vol 459 ◽

pp. 615-619 ◽

Cited By ~ 1

Author(s):

Jian Li Ding ◽

Zhao Hui Yang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Optimization Algorithm ◽

Noise Prediction ◽

Contour Map ◽

Dynamic Neural Network ◽

Network Training ◽

Hub Airport

The key of airport noise monitoring is the appropriate layout of airport noise monitoring points. In this paper, we bring out an optimization algorithm based on the advantages of gray dynamic neural network model in the network training and fitting operations. We use it with the airport noise prediction contour map from INM software to optimize the present layout of airport noise monitoring points in a large domestic hub airport. Experiment results show that the experimental layout of monitoring points program can reflect the distribution of airport noise.

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