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bp neural networks and random forest models to detect damage by Dendrolimus punctatus Walker

Journal of Forestry Research 2020年第1期31卷 107-121页

作者： Zhanghua Xu Xuying Huang Lu Lin Qianfeng Wang Jian Liu Kunyong Yu Chongcheng Chen College of Environment and Resources Fuzhou UniversityFuzhou 350116People’s Republic of China Fujian Provincial Key Laboratory of Resources and Environment Monitoring and Sustainable Management and Utilization Sanming 365004People’s Republic of China Key Lab of Spatial Data Mining and Information Sharing Ministry of EducationFuzhou 350116People’s Republic of China

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(pbp neural networks,while RF method is more robust,provi

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

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Journal of Environmental Sciences 2005年第4期17卷 699-704页

作者： CHIYong WENJun-ming ZHANGDong-ping YANJian-hua NIMing-jiang CENKe-fa CleanEnergyandEnvironmentEngineeringKeyLaboratoryofMOE InstituteforThermalPowerEngineeringofZhejiangUniversityHangzhou310027China CleanEnergyandEnvironmentEngineeringKeyLaboratoryofMOE InstituteforThermalPowerEngineeringofZhejiangUniversityHangzhou310027／／SuyuanEnvironmentProtectionEngineeringLtd.Co.Nanjing210024China

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.

关键词： municipal solid waste(MSW) HCl emission fluidized bed bp neural networks prediction model

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A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and bp neural networks

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Information Processing in Agriculture 2021年第2期8卷 299-309页

作者： Weihong Ma Qifeng Li Jiawei Li Luyu Ding Qinyang Yu Beijing Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Key Laboratory of Agri-informatics Ministry of AgricultureBeijing 100097China Beijing Engineering Research Center of Agriculture Internet of Things Beijing 100097China

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.

关键词： Weighing of broiler chickens Improved amplitude-limiting filtering algorithm bp neural networks Dynamic weighing

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The Application of bp neural networks to Analysis the National Vulnerability

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Computers, Materials & Continua 2019年第2期58卷 421-436页

作者： Guodong Zhao Yuewei Zhang Yiqi Shi Haiyan Lan Qing Yang Harbin Engineering University No.145Nantong AvenueNangang DistrictHarbin150001China Harbin University of Commerce No.138Tongda StreetDaoli DistrictHarbin150028China University of North Texas 1155 Union CirDenton76207USA.

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.

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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Journal of Beijing Institute of Technology 2003年第4期12卷 376-380页

作者：杨卫平廖宁放柴冰华胡中平白力栗兆剑 National Laboratory of Color Science and Engineering

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.

关键词： CRT characterization cross-media color reproduction vision matching bp neural networks

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The Prediction of Stock Prices Based on PCA and bp neural networks

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Chinese Business Review 2005年第5期4卷 64-68页

作者： Xiaoping Yang Jiangxi University of Finance and Economics

There are many factors to influence stock prices indeed. The research method combining models and examples is applied to study how the factors affect stock prices here. Firstly, the principal component analysis is used to deal with a set of variables as the input of a bp neural Network. Therefore, not only is the number of variables less, but also most of the information of original variables is kept. Then, the bp neural Network is established to analyze and predict stock prices. Finally, the analysis of Chinese stock market illustrates that the method predicting stock prices is satisfying and feasible.

关键词： bp neural networks Prediction PCA Stock prices

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OPTIMIZATION OF INJECTION MOLDING PROCESS BASED ON NUMERICAL SIMULATION AND bp neural networks

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Journal of Shanghai Jiaotong university(Science) 2001年第2期6卷 212-215页

作者：王玉邢渊阮雪榆 National Die Mold CAD Eng. Research Center Shanghai Jiaotong Univ.

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.

关键词： injection molding process optimization bp neural networks numerical simulation

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Linearization　Learning　Method　of　bp　neural　networks

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Wuhan University Journal of Natural Sciences 1997年第1期2卷 37-41页

作者： Zhou Shaoqian Ding Lixin Zhang Jian Tang Xinhua

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.

关键词： bp neural networks activation function linearization method

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基于VMD-CIGWO-bp-DTA算法的空调负荷预测

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建模与仿真 2024年第2期13卷 1651-1661页

作者：白雪松王志毅鲁浩翔谭永辉魏同正浙江理工大学建筑工程学院，浙江杭州

提出一种Circle混沌化灰狼算法(CIGWO)优化bp神经网络与变分模态分解(VMD)结合的预测模型(VMD-CIGWO-bp-DTA)，对蓄能空调负荷进行预测分析。采用CIGWO算法对bp神经网络模型寻优得到最优神经元阈值和权值，将其与多种单一模型进行实验... 详细信息

提出一种Circle混沌化灰狼算法(CIGWO)优化bp神经网络与变分模态分解(VMD)结合的预测模型(VMD-CIGWO-bp-DTA)，对蓄能空调负荷进行预测分析。采用CIGWO算法对bp神经网络模型寻优得到最优神经元阈值和权值，将其与多种单一模型进行实验比较，CIGWO-bp模型预测精度最高。采用变分模态分解(VMD)对单一模型的预测残差进行分解，利用决策树(DTA)模型对分解量预测，将其与原模型预测值合并为最终预测结果，预测精度均有较大提升，其中VMD-CIGWO-bp-DTA模型的MAE、MAPE和RMSE相较于CIGWO-bp模型分别降低了20.79%、45.58%、55.12%。

关键词：计量学实验动物房空调负荷混沌映射序列灰狼算法 bp神经网络变分模态分解 Metrology Laboratory Animal Rooms Air-Conditioned Loads Chaotic Mapping Grey Wolf Algorithm bp neural networks Variational Modal Decomposition

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A Method for Determining Scale Factor of CFAR Detector Based on bp neural networks

A Method for Determining Scale Factor of CFAR Detector Based...

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2012年计算机应用与系统建模国际会议

作者： Dang Ling Wang Pingjun Wang Zhikai Dalian Naval Academy

As applying constant false alarm rate (CFAR) detection algorithms, an important task is to determine its scale factor according to given false alarm probability. When analytic expression of scale factor vs false alarm probability is difficult or impossible to be obtained, simulation is adopted traditionally. But the computation of simulation is very large. A method for determining scale factor of CFAR detector based on bp neural networks is proposed in the paper using powerful ability to approximate any non-linear expression. Studies of examples indicate training times of bp neural networks approximating relation between false alarm probability and scale factor can be largely shorten, after nonlinear transformation of natural logarithm is applied to input of bp neural networks. Studies also indicate method for determining scale factor based on bp neural networks can provide high accuracy.

关键词： CFAR Scale factor bp neural networks

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