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A method for predicting random vibration response of train-track-bridge system based on GA-BP neural network 认领 引用
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作者 Jianfeng Mao Yun Zhang +2 位作者 Li Zheng Mansoor Khan Zhiwu Yu 《High-Speed Railway》 CSCD 2025年第4期305-317,共13页
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw... To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems. 展开更多
关键词 Train-track-bridge system Genetic algorithm BP neural network Random response prediction Random parameters
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Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network 认领 引用 被引量：23
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作者 LONG Jiangqi LAN Fengchong +1 位作者 CHEN Jiqing YU Ping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2009年第1期36-41,共6页
For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet... For optimal design of mechanical clinching steel-aluminum joints,the back propagation(BP)neural network is used to research the mapping relationship between joining technique parameters including sheet thickness,sheet hardness,joint bottom diameter etc.,and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA)is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters.The training samples'parameters and the corresponding joints'mechanical properties are supplied to the artificial neural network(ANN)for training.The validating samples'experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints. 展开更多
关键词 genetic algorithm BP neural network mechanical clinching joint properties prediction
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BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker 认领 引用 被引量：9
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作者 Zhanghua Xu Xuying Huang +4 位作者 Lu Lin Qianfeng Wang Jian Liu Kunyong Yu Chongcheng Chen 《Journal of Forestry Research》 SCIE CAS CSCD 2020年第1期107-121,共15页
The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four exper... The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four experimental areas in Sanming City,Jiangle County,Sha County and Yanping District in Fujian Province,sample data on pest damage in 182 sets of Dendrolimus punctatus were collected.The data were randomly divided into a training set and testing set,and five duplicate tests and one eliminating-indicator test were done.Based on the characterization analysis of the host for D.punctatus damage,seven characteristic indicators of ground and remote sensing including leaf area index,standard error of leaf area index(SEL)of pine forest,normalized difference vegetation index(NDVI),wetness from tasseled cap transformation(WET),green band(B2),red band(B3),near-infrared band(B4)of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels.The detection results of these two algorithms were comprehensively compared from the aspects of detection precision,kappa coefficient,receiver operating characteristic curve,and a paired t test.The results showed that the seven indicators all were responsive to pest damage,and NDVI was relatively weak;the average pest damage detection precision of six tests by BP neural networks was 77.29%,the kappa coefficient was 0.6869 and after the RF algorithm,the respective values were 79.30%and 0.7151,showing that the latter is more optimized,but there was no significant difference(p>0.05);the detection precision,kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels(no damage,moderate damage and severe damage).The detection precision and AUC of BP neural networks were a little higher for mild damage,but the difference was not significant(p>0.05)except for the kappa coefficient for the no damage level(p<0.05).An"over-fitting"phenomenon tends to occur in BP neural networks,while RF method is more robust,providing a detection effect that is better than the BP neural networks.Thus,the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data. 展开更多
关键词 BP neural networks Detection precision Kappa coefficient Pine moth Random forest ROC curve
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Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm 认领 引用 被引量：12
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作者 Maohua Xiao Wei Zhang +2 位作者 Kai Wen Yue Zhu Yilidaer Yiliyasi 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第6期252-261,共10页
In the process of Wavelet Analysis,only the low-frequency signals are re-decomposed,and the high-frequency signals are no longer decomposed,resulting in a decrease in frequency resolution with increasing frequency.The... In the process of Wavelet Analysis,only the low-frequency signals are re-decomposed,and the high-frequency signals are no longer decomposed,resulting in a decrease in frequency resolution with increasing frequency.Therefore,in this paper,firstly,Wavelet Packet Decomposition is used for feature extraction of vibration signals,which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals,and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition.The features are visualized by the K-Means clustering method,and the results show that the extracted energy features can accurately distinguish the different states of the bearing.Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algo-rithm is proposed to identify the bearing faults.Compared with the Particle Swarm Algorithm,Beetle Algorithm can quickly find the error extreme value,which greatly reduces the training time of the model.At last,two experiments are conducted,which show that the accuracy of the model can reach more than 95%,and the model has a certain anti-interference ability. 展开更多
关键词 Rolling bearing BP neural network Beetle algorithm Wavelet packet transform
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Development of viscosity model for aluminum alloys using BP neural network 认领 引用 被引量：14
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作者 Heng-cheng LIAO Yuan GAO +1 位作者 Qi-gui WANG Dan WILSON 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2021年第10期2978-2985,共8页
Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of ... Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy. 展开更多
关键词 BP neural network aluminum alloy viscosity genetic algorithm prediction model
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Coal mine safety production forewarning based on improved BP neural network 认领 引用 被引量：40
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作者 Wang Ying Lu Cuijie Zuo Cuiping 《International Journal of Mining Science and Technology》 EI CAS CSCD 2015年第2期319-324,共6页
Firstly,the early warning index system of coal mine safety production was given from four aspects as per-sonnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adapt... Firstly,the early warning index system of coal mine safety production was given from four aspects as per-sonnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adaptive learning rate,particle swarm optimization algorithm,variable weight method and asynchronous learning factor,are used to optimize BP neural network models.Further,the models are applied to a comparative study on coal mine safety warning instance.Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model,and MPSO-BP model can not only effectively reduce the possibility of the network falling into a local minimum point,but also has fast convergence and high precision,which will provide the scientific basis for the forewarnin~management of coal mine safetv production. 展开更多
关键词 Improved PSO algorithm BP neural network Coal mine safety production Early warning
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BP Neural Network of Continuous Casting Technological Parameters and Secondary Dendrite Arm Spacing of Spring Steel 认领 引用 被引量：12
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作者 HANG Li-hong WANG Ai-guo +2 位作者 TIAN Nai-yuan ZHANG Wei-cun FAN Qiao-li 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2011年第8期25-29,共5页
The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of techno... The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of technological parameters of continuous casting process directly impacts the property of slab. The relationships between continuous casting technological parameters and cooling rate of slab for spring steel were built using BP neural network model, based on which, the relevant secondary dendrite arm spacing was calculated. The simulation calculation was also carried out using the industrial data. The simulation results show that compared with that of the traditional method, the absolute error of calculation result obtained with BP neural network model reduced from 0. 015 to 0. 0005, and the relative error reduced from 6, 76 % to 0.22 %. BP neural network model had a more precise accuracy in the optimization of continuous casting technological parameters. 展开更多
关键词 continuous casting technological parameter secondary dendrite arm spacing BP neural network
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A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks 认领 引用 被引量：11
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作者 Fengye Hu Lu Wang +2 位作者 Shanshan Wang Xiaolan Liu Gengxin He 《China Communications》 SCIE CSCD 2016年第8期198-208,共11页
Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been propos... Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications. 展开更多
关键词 wireless body area networks BP neural network signal vector magnitude posture recognition rate
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Mechanical Property Prediction of Strip Model Based on PSO-BP Neural Network 认领 引用 被引量：23
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作者 WANG Ping HUANG Zhen-yi +1 位作者 ZHANG Ming-ya ZHAO Xue-wu 《Journal of Iron and Steel Research International》 SCIE CAS 2008年第3期87-91,共5页
Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimiza... Mechanical property prediction of hot rolled strip is one of the hotspots in material processing research. To avoid the local infinitesimal defect and slow constringency in pure BP algorithm, a kind of global optimization algorithm-particle swarm optimization （PSO） is adopted. The algorithm is combined with the BP rapid training algorithm, and then, a kind of new neural network （NN） called PSO-BP NN is established. With the advantages of global optimization ability and the rapid constringency of the BP rapid training algorithm, the new algorithm fully shows the ability of nonlinear approach of multilayer feedforward network, improves the performance of NN, and provides a favorable basis for further online application of a comprehensive model. 展开更多
关键词 particle swarm optimization algorithm BP neural network hot continuous rolling strip mechanical property prediction
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Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake 认领 引用 被引量：6
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作者 XU Min ZENG Guang-ming +3 位作者 XU Xin-yi HUANG Guo-he SUN Wei JIANG Xiao-yun 《Journal of Environmental Sciences》 SCIE EI CAS 2005年第6期946-952,共7页
Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of t... Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake. 展开更多
关键词 Dongting Lake chlorophyll-a Bayesian regularized BP neural network model sum of square weights
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Path Planning and Tracking for Vehicle Parallel Parking Based on Preview BP Neural Network PID Controller 认领 引用 被引量：10
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作者 季学武 王健 +3 位作者 赵又群 刘亚辉 臧利国 李波 《Transactions of Tianjin University》 EI CAS 2015年第3期199-208,共10页
In order to diminish the impacts of extemal disturbance such as parking speed fluctuation and model un- certainty existing in steering kinematics, this paper presents a parallel path tracking method for vehicle based ... In order to diminish the impacts of extemal disturbance such as parking speed fluctuation and model un- certainty existing in steering kinematics, this paper presents a parallel path tracking method for vehicle based on pre- view back propagation （BP） neural network PID controller. The forward BP neural network can adjust the parameters of PID controller in real time. The preview time is optimized by considering path curvature, change in curvature and road boundaries. A fuzzy controller considering barriers and different road conditions is built to select the starting po- sition. In addition, a kind of path planning technology satisfying the requirement of obstacle avoidance is introduced. In order to solve the problem of discontinuous curvature, cubic B spline curve is used for curve fitting. The simulation results and real vehicle tests validate the effectiveness of the proposed path planning and tracking methods. 展开更多
关键词 parallel parking path tracking path planning BP neural network curve fitting
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Study of a New Improved PSO-BP Neural Network Algorithm 认领 引用 被引量：11
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作者 Li Zhang Jia-Qiang Zhao +1 位作者 Xu-Nan Zhang Sen-Lin Zhang 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第5期106-112,共7页
In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based ... In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability. 展开更多
关键词 improved particle swarm optimization inertia weight learning factor BP neural network rolling bearings
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The Rapidly Solidified Aging Copper Alloy by BP Neural Network 认领 引用 被引量：3
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作者 苏娟华 DONGQi-ming +2 位作者 LIUPing LIHe-jun KANGBu-xi 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2003年第4期50-53,共4页
Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu-Cr-Zr copper alloy.The use of BP neural network(NN) is presented to model the non-linear relationship between p... Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu-Cr-Zr copper alloy.The use of BP neural network(NN) is presented to model the non-linear relationship between parameters of age hardening processes and the mechanical and electrical properties of rapdily solidified Cu-Cr-Zr alloy.The improved model is developed by the Levenberg-Marquardt training algorithm and the good generalization performance is demonstrated.So,an important foundation has been laid for optimisticaly controlling the rapidly solidified aging processes of Cu-Cr-Zr alloy. 展开更多
关键词 Cu-Cr-Zr alloy rapid solidification aging BP neural network Levenberg-Marquard algorithm
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A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation 认领 引用 被引量：5
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作者 Qifeng Jiang Mianshui Rong +1 位作者 Wei Wei Tingting Chen 《Journal of Earth Science》 SCIE CAS CSCD 2024年第4期1355-1366,共12页
Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method bas... Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training. 展开更多
关键词 seismic topographic effect finite element method BP neural network algorithm earthquake disaster prevention
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The Application of BP Neural Networks to Analysis the National Vulnerability 认领 引用 被引量：4
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作者 Guodong Zhao Yuewei Zhang +2 位作者 Yiqi Shi Haiyan Lan Qing Yang 《Computers, Materials & Continua》 SCIE EI 2019年第2期421-436,共16页
Climate change is the main factor affecting the country’s vulnerability,meanwhile,it is also a complicated and nonlinear dynamic system.In order to solve this complex problem,this paper first uses the analytic hierar... Climate change is the main factor affecting the country’s vulnerability,meanwhile,it is also a complicated and nonlinear dynamic system.In order to solve this complex problem,this paper first uses the analytic hierarchy process(AHP)and natural breakpoint method(NBM)to implement an AHP-NBM comprehensive evaluation model to assess the national vulnerability.By using ArcGIS,national vulnerability scores are classified and the country’s vulnerability is divided into three levels:fragile,vulnerable,and stable.Then,a BP neural network prediction model which is based on multivariate linear regression is used to predict the critical point of vulnerability.The function of the critical point of vulnerability and time is established through multiple linear regression analysis to obtain the regression equation.And the proportion of each factor in the equation is established by using the partial least-squares regression to select the main factors affecting the country’s vulnerability,and using the neural network algorithm to perform the fitting.Lastly,the BP neural network prediction model is optimized by genetic algorithm to get the chaotic time series BP neural network prediction model.In order to verify the practicability of the model,Cambodia is selected to be an example to analyze the critical point of the national vulnerability index. 展开更多
关键词 Climate change BP neural networks national vulnerability GA-BP
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Seismic signal recognition using improved BP neural network and combined feature extraction method 认领 引用 被引量：4
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作者 彭朝琴 曹纯 +1 位作者 黄姣英 刘秋生 《Journal of Central South University》 SCIE EI CAS 2014年第5期1898-1906,共9页
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural... Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network. 展开更多
关键词 seismic signal feature extraction BP neural network signal identification
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An Image Encryption Algorithm Based on BP Neural Network and Hyperchaotic System 认领 引用 被引量：13
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作者 Feifei Yang Jun Mou +1 位作者 Yinghong Cao Ran Chu 《China Communications》 SCIE CSCD 2020年第5期21-28,共8页
To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based... To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication. 展开更多
关键词 BP neural network fractional-order hyperchaotic system image encryption algorithm secure communication
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Combined Transmission Interference Spectrum of No Core Fiber and BP Neural Network for Concentration Sensing Research 认领 引用 被引量：2
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作者 Fang Wang Heng Lu +1 位作者 Yunpeng Li Yufang Liu 《Journal of Beijing Institute of Technology》 EI CAS 2017年第2期267-275,共9页
To investigate wavelength response of the no core fiber（NCF）interference spectrum to concentration,a three-layer back propagation（BP）neural network model was established to optimize the concentration sensing data.... To investigate wavelength response of the no core fiber（NCF）interference spectrum to concentration,a three-layer back propagation（BP）neural network model was established to optimize the concentration sensing data.In this method,the measured wavelength and the corresponding concentration were trained by a BP neural network,so that the accuracy of the measurement system was optimized.The wavelength was used as the training set and got into the input layer of the three layer BP network model which is used as the input value of the network,and the corresponding actual concentration value was used as the output value of the network,and the optimal network structure was trained.This paper discovers a preferable correlation between the predicted value and the actual value,where the former is approximately equal to the latter.The correlation coefficients of the measured and predicted values for a sucrose concentration were 1.000 89 and 1.003 94;similarly,correlations of0.999 51 and 1.018 8 for a glucose concentration were observed.The results demonstrate that the BP neural network can improve the prediction accuracy of the nonlinear relationship between the interference spectral data and the concentration in NCF sensing systems. 展开更多
关键词 no core fiber dislocation optical fiber BP neural network concentration detection interference spectrum
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Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks 认领 引用 被引量：3
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作者 刘曼兰 朱春波 王铁成 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第3期266-270,共5页
In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural n... In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper. 展开更多
关键词 DC motor current analysis BP neural networks fault detection fault diagnosis
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A Worsted Yarn Virtual Production System Based on BP Neural Network 认领 引用 被引量：5
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作者 董奎勇 于伟东 《Journal of Donghua University(English Edition)》 EI CAS 2004年第4期34-37,共4页
Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results f... Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system. 展开更多
关键词 BP neural network yarn properties top qualities virtual production prediction deduction.
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