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BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker 被引量:5
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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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HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds 被引量:3
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作者 CHIYong WENJun-ming +3 位作者 ZHANGDong-ping YANJian-hua NIMing-jiang CENKe-fa 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2005年第4期699-704,共6页
The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed in... The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator. 展开更多
关键词 municipal solid waste(MSW) HCl emission fluidized bed bp neural networks prediction model
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The Application of BP Neural Networks to Analysis the National Vulnerability 被引量:1
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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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Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment
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作者 杨卫平 廖宁放 +3 位作者 柴冰华 胡中平 白力 栗兆剑 《Journal of Beijing Institute of Technology》 EI CAS 2003年第4期376-380,共5页
A CRT characterization method based on color appearance matching is presented. A matching between Munsell color chips and CRT charts was obtained in vision perceiver in typical office environment and viewing condition... A CRT characterization method based on color appearance matching is presented. A matching between Munsell color chips and CRT charts was obtained in vision perceiver in typical office environment and viewing condition by recommending. And neural networks were utilized to accomplish the color space conversion from CIE standard color space to CRT device color space. The neural networks related the color space conversion and color reproduction of soft/hard-copy directly to the influence of the illuminance and viewing condition in vision perceiver. The average color difference of training samples is 3.06 and that of testing samples is 5.17. The experiment results indicated that the neural networks can satisfy the requirements for the color appearance of hard-copy reproduction in CRT. 展开更多
关键词 CRT characterization cross-media color reproduction vision matching bp neural networks
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OPTIMIZATION OF INJECTION MOLDING PROCESS BASED ON NUMERICAL SIMULATION AND BP NEURAL NETWORKS
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作者 王玉 邢渊 阮雪榆 《Journal of Shanghai Jiaotong university(Science)》 EI 2001年第2期212-215,共4页
Plastic injection molding is a very complex process and its process planning has a direct influence on product quality and production efficiency. This paper studied the optimization of injection molding process by com... Plastic injection molding is a very complex process and its process planning has a direct influence on product quality and production efficiency. This paper studied the optimization of injection molding process by combining the numerical simulation with back-propagation(BP) networks. The BP networks are trained by the results of numerical simulation. The trained BP networks may:(1) shorten time for process planning;(2) optimize process parameters;(3) be employed in on-line quality control;(4) be integrated with knowledge-based system(KBS) and case-based reasoning(CBR) to make intelligent process planning of injection molding. 展开更多
关键词 injection molding process optimization bp neural networks numerical simulation
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Load Reduction Test Method of Similarity Theory and BP Neural Networks of Large Cranes 被引量:4
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作者 YANG Ruigang DUAN Zhibin +2 位作者 LU Yi WANG Lei XU Gening 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第1期145-151,共7页
Static load tests are an important means of supervising and detecting a crane's lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solv... Static load tests are an important means of supervising and detecting a crane's lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes. 展开更多
关键词 similarity theory bp neural network large bridge crane load reduction equivalent test method
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Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm
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作者 Xin Hu Hua Xu Fengqin Li 《Journal of Clinical and Nursing Research》 2024年第6期276-283,共8页
Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to i... Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors. 展开更多
关键词 Vasovagal response Related factors Prediction bp neural network
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A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks 被引量:7
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作者 Weihong Ma Qifeng Li +2 位作者 Jiawei Li Luyu Ding Qinyang Yu 《Information Processing in Agriculture》 EI 2021年第2期299-309,共11页
Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighin... Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens.We tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts. 展开更多
关键词 Weighing of broiler chickens Improved amplitude-limiting filtering algorithm bp neural networks Dynamic weighing
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Linearization Learning Method of BP Neural Networks 被引量:4
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作者 Zhou Shaoqian Ding Lixin +1 位作者 Zhang Jian Tang Xinhua 《Wuhan University Journal of Natural Sciences》 CAS 1997年第1期37-41,共5页
Feedforward multi layer neural networks have very strong mapping capability that is based on the non linearity of the activation function, however, the non linearity of the activation function can cause the multiple ... Feedforward multi layer neural networks have very strong mapping capability that is based on the non linearity of the activation function, however, the non linearity of the activation function can cause the multiple local minima on the learning error surfaces, which affect the learning rate and solving optimal weights. This paper proposes a learning method linearizing non linearity of the activation function and discusses its merits and demerits theoretically. 展开更多
关键词 bp neural networks activation function linearization method
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Adaptive fuze-warhead coordination method based on BP artificial neural network 被引量:1
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作者 Peng Hou Yang Pei Yu-xue Ge 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第11期117-133,共17页
The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the... The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point. 展开更多
关键词 Aircraft vulnerability Fuze-warhead coordination bp artificial neural network Damage probability Initiation delay
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Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism 被引量:1
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作者 Shuiping Zhang Xi Liang +2 位作者 Lin Shi Lei Yan Jun Tang 《Sound & Vibration》 EI 2023年第1期29-44,共16页
Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to ... Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum. 展开更多
关键词 FxLMS NNR-bpFxLMS line spectrum noise bp neural network feedback convergence speed
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Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network 被引量:1
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作者 Wenhua Xu Yuming Zhu +4 位作者 YingrongWei Ya Su YanXu Hui Ji Dehua Liu 《Energy Engineering》 EI 2023年第7期1701-1715,共15页
Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In o... Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells. 展开更多
关键词 Ultra-deep well drilling costs cost estimation bp neural network genetic algorithm
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A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks 被引量:9
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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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An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver
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作者 Yiming Liang Guangning Li +3 位作者 Min Xu Junmin Zhao Feng Hao Hongbo Shi 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期663-674,共12页
In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a... In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application. 展开更多
关键词 Numerical virtual flight Intelligent control bp neural network PID Moving chimera grid
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Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel
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作者 Qi Zhuang Dong Liu Zhuo Chen 《Energy Engineering》 EI 2024年第3期821-834,共14页
Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety man... Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance. 展开更多
关键词 Oil and gas pipeline corrosion defect failure pressure prediction sparrow search algorithm bp neural network logistic chaotic map
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Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer
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作者 Wenxue Chen Yudong Hu +1 位作者 Changsheng Gao Ruoming An 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期412-429,共18页
This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance system... This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots. 展开更多
关键词 bp network neural Integral sliding mode control(ISMC) Missile defense Prescribed performance function(PPF) State observer Tracking guidance system
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Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model
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作者 Chaoming Shu Bin Qin Xin Wang 《Journal of Power and Energy Engineering》 2024年第1期29-43,共15页
Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind s... Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms. 展开更多
关键词 Wind Speed Forecast Long Short-Term Memory Network bp neural Network Variational Mode Decomposition Data Fusion
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A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks
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作者 Quanqing Yu Yukun Liu +3 位作者 Shengwen Long Xin Jin Junfu Li Weixiang Shen 《Green Energy and Intelligent Transportation》 2022年第2期112-123,共12页
In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approxima... In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel. 展开更多
关键词 bp neural Network Branch current estimation and correction Electric vehicles Lithium-ion battery pack
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The application of neural networks to comprehensive prediction by seismology prediction method 被引量:1
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作者 王炜 吴耿锋 宋先月 《Acta Seismologica Sinica(English Edition)》 CSCD 2000年第2期210-215,共6页
BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is ca... BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction. 展开更多
关键词 bp neural networks nonlinear relationship seismological method of earthquake prediction comprehensive earthquake prediction
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STUDY ON TURBOMACHINERY PERFORMANCE PREDICTION WITH NEURAL NETWORKS
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作者 Fan Huiyuan Xi Guang Wang Shangjin (SER Turbomachinery Research Center School of Power and Energy Engineering, Xi’an Jiaotong University) 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2000年第1期52-57,共6页
Traditional methods for performance prediction of a turbomachinery are usually based on certain computations from a set of data obtained in limited experiment measurements of the machine, or the machinemodels. Since ... Traditional methods for performance prediction of a turbomachinery are usually based on certain computations from a set of data obtained in limited experiment measurements of the machine, or the machinemodels. Since the computational (mathematical) models used in such performance prediction are often crude, most of the predicted results are only correct in very small ranges around the known data points. Beyond the limited ranges, the accuracy of the resultant predictions decrease abruptly. Therefore, an alternative approach, neural network technique, is studied for performance prediction of turbomachinery. The new approach has been applied to two typical performance prediction cases to verify its feasibility and reliability. 展开更多
关键词 bp neural networks Turbomachine Performance prediction
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