Clarification of the molecular mechanism underlying the interaction of coal with CH4, CO2, and H2 O molecules is the basis for an in-depth understanding of the states of fluid in coal and fluid-induced coal swelling/c...Clarification of the molecular mechanism underlying the interaction of coal with CH4, CO2, and H2 O molecules is the basis for an in-depth understanding of the states of fluid in coal and fluid-induced coal swelling/contraction. In terms of instrumental analysis, molecular simulation technology based on molecular mechanics/dynamics and quantum chemistry is a powerful tool for revealing the relationship between the structure and properties of a substance and understanding the interaction mechanisms of physical-chemical systems. In this study, the giant canonical ensemble Monte Carlo（GCMC） and molecular dynamics（MD） methods were applied to investigate the adsorption behavior of a Yanzhou coal model（C222H185N3O17S5）. We explored the adsorption amounts of CH4, CO2, and H2 O onto Yanzhou coal, the adsorption conformation, and the impact of oxygen-containing functional groups. Furthermore, we revealed the different adsorption mechanisms of the three substances using isosteric heat of adsorption and energy change data.（1） The adsorption isotherms of the mono-component CH4, CO2, and H2 O were consistent with the Langmuir model, and their adsorption amounts showed an order of CH4CO2〉CH4. In addition, at higher temperatures, the isosteric heat of adsorption decreased; pressure had no significant effect on the heat of adsorption.（3） CH4 molecules displayed an aggregated distribution in the pores, whereas CO2 molecules were cross arranged in pairs. Regarding H2 O molecules, under the influence of hydrogen bonds, the O atom pointed to surrounding H2 O molecules or the H atoms of coal molecules in a regular pattern. The intermolecular distances of the three substances were 0.421, 0.553, and 0.290 nm, respectively. The radial distribution function（RDF） analysis showed that H2 O molecules were arranged in the most compact fashion, forming a tight molecular layer.（4） H2 O molecules showed a significantly stratified distribution around oxygen-containing functional groups on the coal surface, and the b展开更多
Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
To dynamically update the shape of orebody according to the knowledge of a structural geologist’s insight,an approach of orebody implicit modeling from raw drillhole data using the generalized radial basis function i...To dynamically update the shape of orebody according to the knowledge of a structural geologist’s insight,an approach of orebody implicit modeling from raw drillhole data using the generalized radial basis function interpolant was presented.A variety of constraint rules,including geology trend line,geology constraint line,geology trend surface,geology constraint surface and anisotropy,which can be converted into interpolation constraints,were developed to dynamically control the geology trends.Combined with the interactive tools of constraint rules,this method can avoid the shortcomings of the explicit modeling method based on the contour stitching,such as poor model quality,and is difficult to update dynamically,and simplify the modeling process of orebody.The results of numerical experiments show that the 3D ore body model can be reconstructed quickly,accurately and dynamically by the implicit modeling method.展开更多
Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions.An accurate prediction of wind speed plays a major role in environmental planning,en...Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions.An accurate prediction of wind speed plays a major role in environmental planning,energy system balancing,wind farm operation and control,power system planning,scheduling,storage capacity optimization,and enhancing system reliability.This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network(RRBFNN)possessing the three inputs of wind direction,temperature and wind speed to improve modern power system protection,control and management.Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.展开更多
The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve ...The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve the problem of control in conversion mode of TQR, this paper carries out the design of the controller based on improved Active Disturbance Rejection Control(ADRC). According to the characteristics of flight in conversion mode, Tracking Differentiator(TD) with explicit model is used to solve the problem of multiple integrals when the system is high-order system. Extended State Observer(ESO) with Radial Basis Function(RBF) neural network is used to estimate and compensate for internal and external uncertainties, and the adaptive sliding mode control in Nonlinear State Error Feedback(NLSEF) is used to improve the response speed of the controller and reduce the parameters which should be tuned. Through the flight control simulation of the TQR, the validity and rationality of the control system are verified.展开更多
Trajectory tracking control of space robots in task space is of great importance to space missions, which require on-orbit manipulations. This paper focuses on position and attitude tracking control of a tree-floating...Trajectory tracking control of space robots in task space is of great importance to space missions, which require on-orbit manipulations. This paper focuses on position and attitude tracking control of a tree-floating space robot in task space. Since nei- ther the nonlinear terms and parametric uncertainties of the dynamic model, nor the external disturbances are known, an adap- tive radial basis function network based nonsingular terminal sliding mode （RBF-NTSM） control method is presented. The proposed algorithm combines the nonlinear sliding manifold with the radial basis function to improve control performance. Moreover, in order to account for actuator physical constraints, a constrained adaptive RBF-NTSM, which employs a RBF network to compensate for the limited input is developed. The adaptive updating laws acquired by Lyapunov approach guar- antee the global stability of the control system and suppress chattering problems. Two examples are provided using a six-link free-floating space robot. Simulation results clearly demonstrate that the proposed constrained adaptive RBF-NTSM control method performs high precision task based on incomplete dynamic model of the space robots. In addition, the control errors converge faster and the chattering is eliminated comparing to traditional sliding mode control.展开更多
Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource exper...Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.展开更多
The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and ra...The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.展开更多
Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed ...Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.展开更多
Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power syst...Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.展开更多
Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performa...Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.41072116,41102092,41302127,41372165)Special Research Foundation for the Doctoral Program of Higher Education of China(Grant No.20091402110002)+1 种基金Science Project of Taiyuan city(Grant No.120247-27)outstanding funding innovative projects for the graduate students by Shanxi Province in 2010
文摘Clarification of the molecular mechanism underlying the interaction of coal with CH4, CO2, and H2 O molecules is the basis for an in-depth understanding of the states of fluid in coal and fluid-induced coal swelling/contraction. In terms of instrumental analysis, molecular simulation technology based on molecular mechanics/dynamics and quantum chemistry is a powerful tool for revealing the relationship between the structure and properties of a substance and understanding the interaction mechanisms of physical-chemical systems. In this study, the giant canonical ensemble Monte Carlo（GCMC） and molecular dynamics（MD） methods were applied to investigate the adsorption behavior of a Yanzhou coal model（C222H185N3O17S5）. We explored the adsorption amounts of CH4, CO2, and H2 O onto Yanzhou coal, the adsorption conformation, and the impact of oxygen-containing functional groups. Furthermore, we revealed the different adsorption mechanisms of the three substances using isosteric heat of adsorption and energy change data.（1） The adsorption isotherms of the mono-component CH4, CO2, and H2 O were consistent with the Langmuir model, and their adsorption amounts showed an order of CH4CO2〉CH4. In addition, at higher temperatures, the isosteric heat of adsorption decreased; pressure had no significant effect on the heat of adsorption.（3） CH4 molecules displayed an aggregated distribution in the pores, whereas CO2 molecules were cross arranged in pairs. Regarding H2 O molecules, under the influence of hydrogen bonds, the O atom pointed to surrounding H2 O molecules or the H atoms of coal molecules in a regular pattern. The intermolecular distances of the three substances were 0.421, 0.553, and 0.290 nm, respectively. The radial distribution function（RDF） analysis showed that H2 O molecules were arranged in the most compact fashion, forming a tight molecular layer.（4） H2 O molecules showed a significantly stratified distribution around oxygen-containing functional groups on the coal surface, and the b
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
文摘To dynamically update the shape of orebody according to the knowledge of a structural geologist’s insight,an approach of orebody implicit modeling from raw drillhole data using the generalized radial basis function interpolant was presented.A variety of constraint rules,including geology trend line,geology constraint line,geology trend surface,geology constraint surface and anisotropy,which can be converted into interpolation constraints,were developed to dynamically control the geology trends.Combined with the interactive tools of constraint rules,this method can avoid the shortcomings of the explicit modeling method based on the contour stitching,such as poor model quality,and is difficult to update dynamically,and simplify the modeling process of orebody.The results of numerical experiments show that the 3D ore body model can be reconstructed quickly,accurately and dynamically by the implicit modeling method.
文摘Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions.An accurate prediction of wind speed plays a major role in environmental planning,energy system balancing,wind farm operation and control,power system planning,scheduling,storage capacity optimization,and enhancing system reliability.This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network(RRBFNN)possessing the three inputs of wind direction,temperature and wind speed to improve modern power system protection,control and management.Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.
基金sponsored by China Aerodynamics Research and Development Center Rotor Aerodynamics Key Laboratory opening topic fund。
文摘The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve the problem of control in conversion mode of TQR, this paper carries out the design of the controller based on improved Active Disturbance Rejection Control(ADRC). According to the characteristics of flight in conversion mode, Tracking Differentiator(TD) with explicit model is used to solve the problem of multiple integrals when the system is high-order system. Extended State Observer(ESO) with Radial Basis Function(RBF) neural network is used to estimate and compensate for internal and external uncertainties, and the adaptive sliding mode control in Nonlinear State Error Feedback(NLSEF) is used to improve the response speed of the controller and reduce the parameters which should be tuned. Through the flight control simulation of the TQR, the validity and rationality of the control system are verified.
文摘Trajectory tracking control of space robots in task space is of great importance to space missions, which require on-orbit manipulations. This paper focuses on position and attitude tracking control of a tree-floating space robot in task space. Since nei- ther the nonlinear terms and parametric uncertainties of the dynamic model, nor the external disturbances are known, an adap- tive radial basis function network based nonsingular terminal sliding mode （RBF-NTSM） control method is presented. The proposed algorithm combines the nonlinear sliding manifold with the radial basis function to improve control performance. Moreover, in order to account for actuator physical constraints, a constrained adaptive RBF-NTSM, which employs a RBF network to compensate for the limited input is developed. The adaptive updating laws acquired by Lyapunov approach guar- antee the global stability of the control system and suppress chattering problems. Two examples are provided using a six-link free-floating space robot. Simulation results clearly demonstrate that the proposed constrained adaptive RBF-NTSM control method performs high precision task based on incomplete dynamic model of the space robots. In addition, the control errors converge faster and the chattering is eliminated comparing to traditional sliding mode control.
基金the National Natural Science Foundation of China (No.40671145)the Natural Science Foundation of Guangdong Province (Nos.04300504 and 05006623)and the Science and Technology Plan Foundation of Guangdong Province (Nos.2005B20701008,2005B10101028,and 2004B20701006).
文摘Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.
基金Project supported by the National Natural Science Foundation of China (No.40571115)the National High Tech-nology Research and Development Program (863 Program) of China (Nos.2006AA120101 and 2007AA10Z205)
文摘The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
文摘Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.
基金supported by the Electric Power Research Institute and also makes use of Engineering Research Center Shared Facilities supported by the DOE under U.S.NSF Award Number EEC1041877support is provided by the U.S.CURENT Industry Partnership Program and China National Government Building Highlevel University Graduate Programs([2012]3013).
文摘Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications.Wide area measurement systems(WAMS)based on synchrophasors make power system dynamics visible to system operators,delivering an accurate picture of overall operating conditions.However,in actual field implementations,some measurements can be inaccessible for various reasons,e.g.,most notably communication failure.To reconstruct these inaccessible measurements,in this paper,the radial basis function artificial neural network(RBF-ANN)is used to estimate the system dynamics.In order to find the best input features of the RBF-ANN model,geometric template matching(GeTeM)and quality-threshold(QT)clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system.The proposed method is tested and verified on the Eastern Interconnection(EI)transmission system in the United States.The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.
基金co-National Science and Technology Major Project(No.2017-II-0009-0023)Innovation Guidance Support Project for Taicang Top Research Institutes(No.TC2019DYDS09)。
文摘Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.
