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Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability provides petroleum engineers with a tool for efficiently managing the production process of a field...
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Permeability is one of the most important characteristics of hydrocarbon bearing formations. An accurate knowledge of permeability provides petroleum engineers with a tool for efficiently managing the production process of a field. Furthermore, it is one of the most important pieces of information in the design and management of enhanced recovery operations. Formation permeability is often measured in the laboratory from cores or evaluated from well test data Core analysis and well test data, however, are only available from a few wells in a field, while the majority of wells are logged. Therefore, applying an efficient method, which can model this important parameter, is necessary. One of these methods, which recently have been used frequently, is artificial neural networks (ANNs), which have a significant ability to find the complex spatial relationship in the existence parameters of reservoir. Despite all of the applications of ANNs, most of them need a time-consuming procedure of architecture design and the problem of local minima and slow convergence. In this paper, an alternative method of permeability prediction, which is based on integration between wavelet theory and Artificial Neural Network (ANN) or wavelet network (wavenet), is presented. In this study, different wavelets are applied as activation functions to predict the permeability for well logging. Wavenet parameters such as dilation and translation are fixed and only the weights of the network are optimized during its learning process. The efficacy of this type of network in function learning and estimation is compared with ANNs. The results showed that the wavelet network (WNN, Morlet) with 92% correlation coefficient for permeability would be an appropriate substitute for artificial neural network with 89% correlation coefficient. (C) 2015 Elsevier B.V. All rights reserved.
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Porosity is one of the most important parameters of the hydrocarbon reservoirs, the accurate knowledge of which allows petroleum engineers to have adequate tools to evaluate and minimize the risk and uncertainty in the exploration...
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Porosity is one of the most important parameters of the hydrocarbon reservoirs, the accurate knowledge of which allows petroleum engineers to have adequate tools to evaluate and minimize the risk and uncertainty in the exploration and production of oil and gas reservoirs. Different direct and indirect methods are used to measure this parameter, most of which (e.g. core analysis) are very time-consuming as well as cost-consuming. Hence, applying an efficient method that can model this important parameter is of the highest importance. Most of the researches show that the capability (i,e. classification, pattern matching, optimization and data mining) of an ANN is suitable for inherenting uncertainties and imperfections found in petroleum engineering problems considering its successful application. In this paper, an alternative method of porosity prediction, which is based on integration between wavelet theory and Artificial Neural Network (ANN) or wavelet network (wavenet), is presented. In this study, different wavelets are applied as activation functions to predict the porosity from well log data. The efficacy of this type of network in function learning and estimation is compared with ANNs. The simulation results indicate decrease in estimation error values that depicts its ability to enhance the function approximation capability and consequently exhibits excellent learning ability compared to the conventional neural network with sigmoid or other activation functions.
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This paper presents a new method to design power system stabilizer (PSS) using fuzzy wavelet neural network (FWNN) for stability enhancement of a multi-machine power system. In the proposed approach, Wavelet Neural Network (WNN) i...
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This paper presents a new method to design power system stabilizer (PSS) using fuzzy wavelet neural network (FWNN) for stability enhancement of a multi-machine power system. In the proposed approach, Wavelet Neural Network (WNN) is used to construct a well localized in both time and frequency domains consequent part for each fuzzy rule of a Takagi-Sugeno-Kang (TSK) fuzzy model. In designing the FWNN stabilizer the activation function of hidden layer neurons is substituted with dilated and translated Mexican Hat wavelet function. In the proposed method, an efficient genetic algorithm (GA) approach is used to obtain the optimal values of such parameters as translation, dilation, weights, and membership functions. These parameters are tuned through simulation of non-linear model of power system under chosen disturbance by minimizing a non-explicit based objective function. Results are promising and demonstrate the capabilities of the proposed FWNN stabilizer in damping of overall power oscillations in the system. It is worth noting that the proposed FWNN stabilizer, moreover, significantly improves the dynamic response characteristics, reducing the number of fuzzy rules as well as a fast convergence of network.
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Convolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and...
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Convolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.
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In this paper a new approach for predicting oil temperature for substation distribution transformer is presented. It takes advantage of a wavelet neural network (WNN), which is used to predict the oil temperature in the following ...
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In this paper a new approach for predicting oil temperature for substation distribution transformer is presented. It takes advantage of a wavelet neural network (WNN), which is used to predict the oil temperature in the following 32 minutes. The in this article we choose optimal parameters for WNN and demonstrate that linear WNN performs better in terms of mean square error (MSE) of predicted values than linear and nonlinear neural networks.
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A variety of researches Dealt with the iris identification in different ways and Showed different results. A new system for personal identification based on iris patterns is presented in this paper We propose to use two activation...
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A variety of researches Dealt with the iris identification in different ways and Showed different results. A new system for personal identification based on iris patterns is presented in this paper We propose to use two activation function wavelet neural network for feature extraction and identification process after segments the image into 32 blocks with (128~*128) dimension. The proposed method in this paper involves three steps. First reduced image size using wavelet packet 1-level decomposition , second feature extraction using two activation function wavelet neural network and finally identification using trained data and correlation.
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This paper presents fuzzy wavelet neural network (FWNN) models for prediction and identification of nonlinear dynamical systems. The proposed FWNN models are obtained from the traditional Takagi-Sugeno–Kang fuzzy system by replac...
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This paper presents fuzzy wavelet neural network (FWNN) models for prediction and identification of nonlinear dynamical systems. The proposed FWNN models are obtained from the traditional Takagi-Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains. The first and last model use summation and multiplication of dilated and translated versions of single-dimensional wavelet basis functions, respectively, and in the second model, THEN parts of the rules consist of radial function of wavelets. Gaussian type of activation functions are used in IF part of the fuzzy rules. A fast gradient-based training algorithm, i.e., the Broyden-Fletcher–Goldfarb-Shanno method, is used to find the optimal values for unknown parameters of the FWNN models. Simulation examples are also given to compare the effectiveness of the models with the other known methods in the literature. According to simulation results, we see that the proposed FWNN models have impressive generalization ability.
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In this paper, a comparison study among three neural-network algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The archi...
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In this paper, a comparison study among three neural-network algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The architecture of the neural networks is discussed and simulation results are presented. Two new neural networks, based on radial basis functions (RBFs) and wavelet neural networks (WNNs), are introduced, The proposed networks offer a more efficient synthesis procedure, as compared to other available techniques.
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Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin,The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigate...
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Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin,The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0,3648 respectively,! whereas for1 the WANN model the respective values were 0.1946 and 0,1695. Efficiencies during calibration and validationfor ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0 9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hencehybrid , ANN model is the promising tool to predict water table fluctuation as compared to ANN model.
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Wavelet network (WN) based on wavelet decomposition principle is applied to channel equalization for both linear and non-linear channels. The WN is trained by extended Kalman filter (EKF) based recursive algorithm and is compared ...
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Wavelet network (WN) based on wavelet decomposition principle is applied to channel equalization for both linear and non-linear channels. The WN is trained by extended Kalman filter (EKF) based recursive algorithm and is compared with EKF based multi-layered perceptron (MLP) and radial basis function neural network (RBFNN). Exhaustive simulation study reveals the superiority of the WN based equalizer in terms of bit error rate performance, compared to the above equalizer scheme. (C) 2005 Elsevier Inc. All rights reserved.
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