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IEEE PHOTONICS JOURNAL 2020年第2期12卷 1-9页

作者： Bian, Haiyi Yao, Hua Lin, Guohua Yu, Yinshan Chen, Ruiqiang Wang, Xiaoyan Ji, Rendong Yang, Xiao Zhu, Tiezhu Ju, Yongfeng Huaiyin Inst Technol Fac Elect Informat Engn Huaian 223003 Jiangsu Peoples R China Jiangsu Engn Lab Lake Environm Remote Sensing Tec Huaian 223003 Jiangsu Peoples R China

Fluorescence spectroscopy attracted more and more attention in pesticide residue detection field because of its advantages of non-destructive, non-contact, high speed and no requirement of complex pre-process procedure. However, given that the concentration of the pesticide detected via fluorescence spectroscopy is calculated in accordance with the Beer-Lambert law, this method can only be used to detect samples containing a single kind of pesticide or several kinds of pesticides with completely different fluorescence which is not in accordance with practical cases. In this article, to overcome this disadvantage, back-propagation (bp) neural network algorithm was introduced to detect multiple kinds of pesticides via fluorescence spectroscopy. The results from four kinds of pesticides which are usually used for fruits and vegetables indicated the effectiveness of bp neural network algorithm.

关键词： Pesticide residue fluorescence spectroscopy bp neural network algorithm

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Computer Communication network Fault Detection Based on Improved neural network algorithm

ELECTRICA

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ELECTRICA 2022年第3期22卷 351-357页

作者： Sun, Dong Chopra, Pooja Bhola, Jyoti Neware, Rahul Henan Polytech Univ Sch Comp Sci & Technol Xinxiang Henan Peoples R China GNA Univ Phagwara Punjab India Natl Inst Technol Elect & Commun Engn Dept Hamirpur India Hogskulen Vestlandet Dept Comp Math & Phys Bergen Norway Lovely Profess Univ Sch Comp Applicat Jalandhar Punjab India

To detect computer communication network failures, a computer communication network fault detection based on an improved neural network algorithm is proposed. A network fault diagnosis example is used to verify the effectiveness of the method. There are many network failure phenomena. Here, the author selected 13 network fault information parameters for a comprehensive diagnosis of network failures. The author designed a three-layer backpropagation (bp) neural network. There are 13 nodes in the input layer, corresponding to the above 13 network fault parameter information. The output layer has three nodes, which output the fault code sequence. The training of the network uses the trainlm0 function. The performance function uses the mean square error performance function mse0 and set e = 0.001;the network learning rate is set to a = 0.05. The author selects 100 failure data as the training set for network training and selects 10 sets of samples as the test set. The experimental data shows that after the network has been trained 25 times, the output error reaches the set precision e. After training the bp network using this algorithm 140 times, the output error reaches the set precision e. This method effectively improves the effectiveness and accuracy of S network fault diagnosis.

关键词： network failure improved neural network algorithm bp neural network algorithm fault detection

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The application of the intelligent algorithm in the prevention and early warning of mountain mass landslide disaster

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第2期13卷 1-6页

作者： Yan, Yuan Ashraf, Muhammad Aqeel Xi An Jiao Tong Univ Sch Humanities & Social Sci Xian 710049 Shaanxi Peoples R China China Univ Geosci Sch Environm Studies Wuhan 430074 Peoples R China

The objective is to realize the early warning of disasters caused by mountain mass landslide. The probable causes of mountain mass landslide were predicted and analyzed in prior by constructing the back propagation (bp) neural network algorithm, which included earthquake, rainstorm, human activity, landslide displacement, slope gradient, and soil texture. In terms of the early warning of mountain mass landslide caused by earthquakes, rainstorm, or human activities, the accuracy of bp neural network algorithm was relatively high;especially, given the relevant data such as the free slope gradient, slide surface slope gradient, and soil texture of the mountain mass, the accuracy of bp neural network algorithm in the early warning of mountain mass landslide could reach 94.7%;the proposed algorithm could predict not only the occurrence possibility of mountain mass landslide but also the severity and possible range of mountain mass landslide. In addition, in terms of stability, the standard deviation of the proposed algorithm was 0.0062, which indicated the fairly good stability of the algorithm. The possible mountain mass landslide was predicted based on the comprehensive analysis of probable causes of mountain mass landslide and their proportions of weights by using the bp neural network algorithm;the accuracy and stability of the proposed algorithm were excellent, which also showed that the occurrence of mountain mass landslide was a comprehensive consequence caused by various factors and could never be predicted through singular factor analysis. Thus, the understanding of both bp neural network algorithm and mountain mass landslide was greatly improved.

关键词： bp neural network algorithm Model Early warning Rainstorm Mountain mass

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Wind Speed Prediction based on neural network algorithm of Mapreduce

International Journal of Social Science and Education Resear...

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International Journal of Social Science and Education Research 2021年第1期4卷 104-109页

作者： Yue Wang

In order to solve the relatively lack of research on the wind speed prediction of domestic transmission lines. In this paper, the time series analysis method is used to predict the wind speed of the transmission line, and the prediction accuracy is not high. By consulting related references, this paper proposes a combination of bp neural network algorithm and genetic algorithm GA-bp neural network algorithm combined with time series analysis method to obtain high-precision wind speed prediction algorithm. Finally, using the advantages of MapReduce distributed operation mechanism, the GA-bp neural network algorithm is implemented on MapReduce. The simulation results show that the algorithm has a great improvement in prediction accuracy compared with the time series analysis method.

关键词： Transmission line bp neural network algorithm Genetic algorithm MapReduce

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Application of bp neural network algorithm in Sustainable Development of Highway Construction Projects

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Physics Procedia 2012年 25卷 1212-1217页

作者： Mingshun Li Wencui Chen Department of Project Management Changsha University of Science & Technology Changsha China

Highway construction project does not exist for its own, but also to meet the needs of the society. Its development strategy should be based on the overall goal of the society, not just for its own. Therefore, the sustainable evaluation of the highway construction should be considered from the two parts of sustainability of social needs and economic development. In this paper, using the bp neural network algorithm, through the analysis of sustainable development of the following four areas in road construction: economics, environmental resources, operations, management systems and policy, the author studies the sustainable development evaluation of highway construction project.

关键词： bp neural network algorithm Highway construction projects Sustainable Development Evaluation

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Visual prediction of gas diffusion concentration based on regression analysis and bp neural network

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JOURNAL OF ENGINEERING-JOE 2019年第13期2019卷 19-23页

作者： Wang, Jing Fang, Jiandong Zhao, Yvdong Inner Mongolia Univ Technol Hohhot Peoples R China Inner Mongolia Autonomous Reg Agr & Anim Husb Inf Hohhot Peoples R China

In order to realise the prediction of the diffusion trend of chemical gases under toxic, flammable, and explosive conditions, this article used a multiple regression model and a back propagation (bp) neural network model, established two kinds of leakage gas diffusion models based on Fluent simulation data. First, the multivariate function relation between multiple influencing factors and diffusion concentration is established by using linear fitting method, i.e. multivariate regression model. Second, according to the large number and non-linearity of Fluent simulation data of leakage gas, a three-layer bp neural network prediction model for leakage gas, wind speed, X-axis diffusion distance, and Y-axis diffusion distance was established by using bp neural network algorithm. Under the same data conditions, the prediction error of bp neural network prediction model is smaller than that of multivariate reversion model, and the fitting degree is high, but the stability of multiple linear regression is better.

关键词： backpropagation neural nets diffusion regression analysis toxicology chemical engineering computing finite element analysis explosive conditions multiple regression model bp neural network model leakage gas diffusion models Fluent simulation data multivariate function relation multiple influencing factors linear fitting method nonlinearity three-layer bp neural network prediction model diffusion distance bp neural network algorithm data conditions prediction error multivariate reversion model multiple linear regression visual prediction gas diffusion concentration regression analysis diffusion trend chemical gases toxic conditions flammable conditions

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Application of bp neural network algorithm in Sustainable Development of Highway Construction Projects

Application of BP Neural Network Algorithm in Sustainable De...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Mingshun Li Wencui Chen Department of Project Management Changsha University of Science & Technology

关键词： bp neural network algorithm Highway construction projects Sustainable Development Evaluation

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Experimental investigation on processing of fused silica microchannels by high repetition rate femtosecond laser

Experimental investigation on processing of fused silica mic...

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Optifab Conference

作者： Liao, Kai Wang, Wenjun Mei, Xuesong Liu, Bin Pan, Aifei Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Intelligent Robots 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

ISBN: (数字)9781510630642

ISBN: (纸本)9781510630642

Femtosecond laser processing has been extensively used in micromachining, especially for the precision processing of hard and brittle materials. However, the precision of the materials ablated by femtosecond laser is not easy to control. This paper reports an experimental and theoretical study on the ablation characteristics of fused silica using high repetition rate femtosecond laser. An experimental study of microchannels milling on the fused silica was carried out. The influence of pulse energy, repetition rate, scanning velocity, scanning times on the size and morphology of the microchannels was obtained. Simultaneously, the experimental data on the depth and width of microchannels under different parameter combinations were acquired through the orthogonal experiment. The prediction model of aspect ratio was obtained by bp neural network algorithm. Finally, the verification test was established and showed that the experimental results were consistent with the theoretical results. It would provide a theoretical basis for further study on the microchannels fabrication of femtosecond laser.

关键词： Microchannels processing Femtosecond laser Aspect ratio bp neural network algorithm

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Comprehensive Prediction of Generation Power in Photovoltaic Microgrid 8

Comprehensive Prediction of Generation Power in Photovoltaic...

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IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Tu, Kunquan Xiong, Hejin Chen, Feihu Wuhan Univ Technol Coll Automat Wuhan Hubei Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538681787

The output power of distributed photovoltaic power supply is mainly affected by environmental and meteorological conditions. The output power has great randomness and fluctuation. If the output power of photovoltaic cells can be accurately predicted, it will be of great significance to the optimal dispatch of distribution network. In this paper, based on the improved bp neural network algorithm, considering the correction of solar radiation intensity of photovoltaic cloud cover and the correction of output power fluctuation of similar day history, a comprehensive forecasting method of output power of photovoltaic power generation is established, and satisfactory forecasting results are obtained through simulation and analysis.

关键词： photovoltaic power generation bp neural network algorithm power prediction grid connection simulation

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Personalized Distance Education System Based on Data Mining

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第7期13卷 4-16页

作者： Qi, Zhimin Jilin Normal Univ Siping Jilin Peoples R China

To improve the poor intelligence and personalized service of the learning system in current distance education and training, a personalized learning system model based on data mining technology was proposed. Then, the method of applying the decision tree and bp neural network algorithm to design the system was described in detail. Finally, the core module of the personalized distance education system combined with Web was designed. The personalized function module introduced how the user operates the intelligent Web reasoning in the module according to the user input learning information and gave the most matching learning materials. Finally, the application results showed that the system greatly improved the personalized service module.

关键词： learning system data mining technology bp neural network algorithm

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