Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. First...Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.展开更多
Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial pro...Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.展开更多
This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknow...This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.展开更多
Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand ...Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.展开更多
Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region...Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and lowdensity forests were degraded during 1990–2014,respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.展开更多
A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as...A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.展开更多
Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, g...Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.展开更多
The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy sys...The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
Prediction of disruptions caused by locked modes using the Back-Propagation （BP） neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode...Prediction of disruptions caused by locked modes using the Back-Propagation （BP） neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation （RMPs） locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time.展开更多
Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall co...Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.展开更多
文摘Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.
文摘Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.
文摘This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.
文摘Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.
文摘Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and lowdensity forests were degraded during 1990–2014,respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.
基金supported by National Natural Science Foundation of China(Nos.51107115,11347125,51407156)China Postdoctoral Science Foundation(Nos.20110491766,2014M551735)
文摘A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.
基金Project supported by National Basic Research Program of China (Grant Nos 2009CB320505 and 2009CB320504)National High Technology Research and Development Program of China (Grant Nos 2006AA01Z235, 2007AA01Z206 and 2009AA01Z210)
文摘Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.
基金Supported by the National Natural Science Foundation of China(60074014)
文摘The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
基金supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2010GB107004,2011GB109001,2008CB717805)National Natural Science Foundation of China(Nos.11275080,10935004)
文摘Prediction of disruptions caused by locked modes using the Back-Propagation （BP） neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation （RMPs） locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time.
文摘Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.
