Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neura...Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.展开更多
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF ...A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.展开更多
A hybrid approach coupled with a surface panel method for the propeller and a Reynolds averaged Navier-Stokes（RANS） model for the hull with the propeller body forces are presented for predicting the self-propulsion ...A hybrid approach coupled with a surface panel method for the propeller and a Reynolds averaged Navier-Stokes（RANS） model for the hull with the propeller body forces are presented for predicting the self-propulsion performance and the effective wake field of underwater vehicles. To achieve a high accuracy and simplicity, a radial basis function（RBF） based approach is proposed for mapping the force field from the blade surface panels to the RANS model. The effective wake field is evaluated in two ways, i.e., by extrapolation from the flat planes upstream of the propeller disk, and by direct computation in a curved surface upstream of and parallel to the blade leading edges. The hull-propeller system of a real propeller geometry is further simulated with the sliding mesh model to numerically verify the hybrid approach. Numerical simulations are conducted for the fully appended SUBOFF submarine model. The high accuracy of the RBF-based interpolation scheme is confirmed, and the effective wake fraction predicted by the hybrid approach is found consistent with that obtained by the sliding mesh model. The effective wake fractions predicted by the two methods are, respectively, 4.6% and 3% larger than the nominal one.展开更多
Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reco...Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly.展开更多
As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot ...As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence.In order to further improve the timeliness and accuracy of the flame detection network,a radial basis function(RBF)flame detection network based on particle swarm optimization(PSO)algorithm is proposed.First,the proposed algorithm initializes the speed and position parameters of the particles.Then,the parameters of the particles are mapped to the RBF flame detection network.Finally,the algorithm is iteratively updated to obtain the global optimal solution.The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation(BP)flame detection algorithm,and further improves the collection efficiency.The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples.In the flame data samples,the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%.展开更多
In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in...In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in UHF RFID systems,and will have an influence on the recovery on the physical layer.To address the problem of recovery with the frequency drift,this paper adopts a radial basis function(RBF)network to separate the collision signals,and decode the signals via FM0 to recovery collided RFID tags.Numerical results show that the method in this paper has better performance of symbol error rate(SER)and separation efficiency compared to conventional methods when frequency drift occurs.展开更多
The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are us...The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.展开更多
The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholan...The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position（P）, intensity（/） and full width at half-maximum（FWHM）, were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function（RBF） net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.展开更多
Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which in...Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability.展开更多
Based upon motion capture,a semi-automatic technique for fast facial animation was implemented. While capturing the facial expressions from a performer,a camera was used to record her /his front face as a texture map....Based upon motion capture,a semi-automatic technique for fast facial animation was implemented. While capturing the facial expressions from a performer,a camera was used to record her /his front face as a texture map. The radial basis function( RBF) technique was utilized to deform a generic facial model and the texture was remapped to generate a personalized face.Partitioning the personalized face into three regions and using the captured facial expression data,the RBF and Laplacian operator,and mean-value coordinates were implemented to deform each region respectively. With shape blending,the three regions were combined together to construct the final face model. Our results show that the technique is efficient in generating realistic facial animation.展开更多
Radial Basis Function(RBF)kernels are key functional forms for advanced solutions of higher-order partial differential equations(PDEs).In the present study,a hybrid kernel was developed for meshless solutions of PDEs ...Radial Basis Function(RBF)kernels are key functional forms for advanced solutions of higher-order partial differential equations(PDEs).In the present study,a hybrid kernel was developed for meshless solutions of PDEs widely seen in several engineering problems.This kernel,Power-Generalized Multiquadric-Power-GMQ,was built up by vanishing the dependence of e,which is significant since its selection induces severe problems regarding numerical instabilities and convergence issues.Another drawback of e-dependency is that the optimal e value does not exist in perpetuity.We present the Power-GMQ kernel which combines the advantages of Radial Power and Generalized Multiquadric RBFs in a generic formulation.Power-GMQ RBF was tested in higher-order PDEs with particular boundary conditions and different domains.RBF-Finite Difference(RBF-FD)discretization was also implemented to investigate the characteristics of the proposed RBF.Numerical results revealed that our proposed kernel makes similar or better estimations as against to the Gaussian and Multiquadric kernels with a mild increase in computational cost.Gauss-QR method may achieve better accuracy in some cases with considerably higher computational cost.By using Power-GMQ RBF,the dependency of solution on e was also substantially relaxed and consistent error behavior were obtained regardless of the selected e accompanied.展开更多
In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with tradition...In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.展开更多
To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solve...To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.展开更多
基金supported by the National Natural Science Foundation of China(61573078,61573147)the International S&T Cooperation Program of China(2014DFB70120)the State Key Laboratory of Robotics and System(SKLRS2015ZD06)
文摘Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.
基金supported in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
基金Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China
文摘A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.
基金Project supported by the National Basic Research Development Program of China(973 Program,Grant No.613134)
文摘A hybrid approach coupled with a surface panel method for the propeller and a Reynolds averaged Navier-Stokes（RANS） model for the hull with the propeller body forces are presented for predicting the self-propulsion performance and the effective wake field of underwater vehicles. To achieve a high accuracy and simplicity, a radial basis function（RBF） based approach is proposed for mapping the force field from the blade surface panels to the RANS model. The effective wake field is evaluated in two ways, i.e., by extrapolation from the flat planes upstream of the propeller disk, and by direct computation in a curved surface upstream of and parallel to the blade leading edges. The hull-propeller system of a real propeller geometry is further simulated with the sliding mesh model to numerically verify the hybrid approach. Numerical simulations are conducted for the fully appended SUBOFF submarine model. The high accuracy of the RBF-based interpolation scheme is confirmed, and the effective wake fraction predicted by the hybrid approach is found consistent with that obtained by the sliding mesh model. The effective wake fractions predicted by the two methods are, respectively, 4.6% and 3% larger than the nominal one.
基金Supported by the Key Program of National Natural Science Foundation of China(Nos.61077071,51075349)Program of National Natural Science Foundation of Hebei Province(Nos.F2011203207,F2010001312)
文摘Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly.
基金Supported by Shaanxi Province Key Research and Development Project(No.2021GY-280,2021GY-029)Shaanxi Province Natural Science Basic Research Program Project(No.2021JM-459)National Natural Science Foundation of China(No.61834005,61772417,61802304).
文摘As the main production tool in the industrial environment,large boilers play a vital role in the conversion and utilization of energy.Therefore,the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence.In order to further improve the timeliness and accuracy of the flame detection network,a radial basis function(RBF)flame detection network based on particle swarm optimization(PSO)algorithm is proposed.First,the proposed algorithm initializes the speed and position parameters of the particles.Then,the parameters of the particles are mapped to the RBF flame detection network.Finally,the algorithm is iteratively updated to obtain the global optimal solution.The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation(BP)flame detection algorithm,and further improves the collection efficiency.The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples.In the flame data samples,the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%.
基金supported by the National Natural Science Foundation of China(61762093)the 17th Batches of Young and Middle-aged Leaders in Academic and Technical Reserved Talents Project of Yunnan Province(2014HB019)+1 种基金the Key Applied and Basic Research Foundation of Yunnan Province(2018FA036)the Program for Innovative Research Team(in Science and Technology)in University of Yunnan Province。
文摘In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in UHF RFID systems,and will have an influence on the recovery on the physical layer.To address the problem of recovery with the frequency drift,this paper adopts a radial basis function(RBF)network to separate the collision signals,and decode the signals via FM0 to recovery collided RFID tags.Numerical results show that the method in this paper has better performance of symbol error rate(SER)and separation efficiency compared to conventional methods when frequency drift occurs.
文摘The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.
文摘The aim of this study was to explore the possibility of applying Fourier transform infrared（FTIR） spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position（P）, intensity（/） and full width at half-maximum（FWHM）, were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function（RBF） net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.
文摘Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability.
基金Youth Foundation of Higher Education Scientific Research of Hebei Province,China(No.2010228)Foundation for Returned Overseas Scholars of Hebei Province,China(No.C2013003015)
文摘Based upon motion capture,a semi-automatic technique for fast facial animation was implemented. While capturing the facial expressions from a performer,a camera was used to record her /his front face as a texture map. The radial basis function( RBF) technique was utilized to deform a generic facial model and the texture was remapped to generate a personalized face.Partitioning the personalized face into three regions and using the captured facial expression data,the RBF and Laplacian operator,and mean-value coordinates were implemented to deform each region respectively. With shape blending,the three regions were combined together to construct the final face model. Our results show that the technique is efficient in generating realistic facial animation.
文摘Radial Basis Function(RBF)kernels are key functional forms for advanced solutions of higher-order partial differential equations(PDEs).In the present study,a hybrid kernel was developed for meshless solutions of PDEs widely seen in several engineering problems.This kernel,Power-Generalized Multiquadric-Power-GMQ,was built up by vanishing the dependence of e,which is significant since its selection induces severe problems regarding numerical instabilities and convergence issues.Another drawback of e-dependency is that the optimal e value does not exist in perpetuity.We present the Power-GMQ kernel which combines the advantages of Radial Power and Generalized Multiquadric RBFs in a generic formulation.Power-GMQ RBF was tested in higher-order PDEs with particular boundary conditions and different domains.RBF-Finite Difference(RBF-FD)discretization was also implemented to investigate the characteristics of the proposed RBF.Numerical results revealed that our proposed kernel makes similar or better estimations as against to the Gaussian and Multiquadric kernels with a mild increase in computational cost.Gauss-QR method may achieve better accuracy in some cases with considerably higher computational cost.By using Power-GMQ RBF,the dependency of solution on e was also substantially relaxed and consistent error behavior were obtained regardless of the selected e accompanied.
基金National Natural Science Foundation ofChina(No.60374011)
文摘In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.
基金Supported by the National"Thirteenth Five-year Plan"National Key Program(2016YFD0701301)the Heilongjiang Provincial Achievement Transformation Fund Project(NB08B-011)。
文摘To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.
