A neural network model was used to predict the groundwater rebound process after cessation of dewatering at a restored open cut coal site in the East Midlands area of the UK. Time (days after dewatering), water table ...
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A neural network model was used to predict the groundwater rebound process after cessation of dewatering at a restored open cut coal site in the East Midlands area of the UK. Time (days after dewatering), water table levels in the aquifer and the backfilled site, hydraulic conductivity of the aquifer and backfilled site, and precipitation were used as input. The output of the network was the water table height, until the water table reached its equilibrium position. A feed-forward artificial neural network that uses batch gradient descent with a momentum-learningalgorithm and 6-1-6-1 arrangement was found capable of predicting the groundwater rebound process. Predicted values were very close to the monitored results. The correlation coefficient values were 0.98221 for the training set, and 0.99329, 0.99499, 0.98667, 0.98289, and 0.97141 during the testing stage for the five monitoring points, showing that the model prediction was satisfactory.
In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the m...
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In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.
In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured...
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In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output;the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.
In this paper, we propose a Wavelet Neural Network with Hybrid learning Approach (WNN-HLA). A novel hybrid learning approach, which combines the on-line partition method (OLPM) and the gradient descent method, is prop...
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In this paper, we propose a Wavelet Neural Network with Hybrid learning Approach (WNN-HLA). A novel hybrid learning approach, which combines the on-line partition method (OLPM) and the gradient descent method, is proposed to identify a parsimonious internal structure and adjust the parameters of WNN-HLA model. First, the proposed OLPM is an online method and is a distance-based connectionist clustering method. Unlike the traditional cluster techniques that only consider the total variation to update the one mean and deviation. Second, a back-propagationlearning method is used to adjust the parameters for the desired outputs. Several simulation examples have been given to illustrate the performance and effectiveness of the proposed model. The computer simulations demonstrate that the proposed WNN-HLA model performs better than some existing models.
In [6], we distributed training instances over a single-channel broadcast communication model to speed up execution of the back-propagation learning algorithm for classification problems. In this paper, we extend this...
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In [6], we distributed training instances over a single-channel broadcast communication model to speed up execution of the back-propagation learning algorithm for classification problems. In this paper, we extend this concept to control problems, where the output is not necessarily 0 or 1, but ranges over an interval. We first propose a modified back-propagation learning algorithm that incrementally decreases the error threshold by half in order to process training instances with large weight changes as quickly as possible. This modified back-propagation learning algorithm is then parallelized using the single-channel broadcast communication model to n processors, where n is the number of training instances. Finally, the parallel back-propagation learning algorithm is modified for execution on a bounded number of processors to cope with real-world conditions.
The nonlinearity included in the P-CO2 control system in humans is evaluated using the degree of nonlinearity based on a difference of residuals, An autoregressive moving average (ARMA) model and neural networks (line...
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The nonlinearity included in the P-CO2 control system in humans is evaluated using the degree of nonlinearity based on a difference of residuals, An autoregressive moving average (ARMA) model and neural networks (linear and nonlinear) are employed to model the system, and three types of network (Jordan, Elman and fully interconnected) are compared. As the Jordan-type linear network cannot approximate respiratory data accurately, the other two types and the ARMA model are used for the evaluation of the nonlinearity. The results of the evaluation indicate that the linear assumption for the P-CO2 control system is invalid for three subjects out of seven, In particular, strong nonlinearity was observed for two subjects.
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