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2011 International Conference on Power Electronics and Engineering Application

作者： Tuo zhong Wang li-yuan Glorious Sun School of Business and Management Donghua University

Franchise value affects bank stability, so this article analyzes of the various factors franchise value, constructs bank stability evaluation system from market-related factors and bank-related factors, based on improved bp neural network constructs early warning model of bank stability, and takes America banks’ data as training samples, takes Chinese Banks’ data as test samples, finds that Chinese bank stability is worth than America bank stability, so it is necessary to take measures to enhance Chinese bank stability.

关键词： improved bp neural network Franchise value bank stability Warning

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On Post-Evaluation of Power Plant Construction Project Based on improved Back Propagation neural network

On Post-Evaluation of Power Plant Construction Project Based...

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第二十九届中国控制会议

作者： NIU Dongxiao1,LI Xin1,Zhang Kun2,LIU Yimin1 1.School of Economics and Management,North China Electric Power University,Beijing 102206,P.R.China2.School of Electrical&Electronic Engineering,North China Electric Power University,Beijing 102206,P.R.China

Construction project post-evaluation is the feedback link of the project decision management,and it can provide a scientific basis for construction projects in the *** paper established the post-evaluation indicator system of power plant construction project according to its *** on the improved back propagation neural network by using adaptive learning rate and momentum algorithm,combined with Delphi method and success degree method,the post-evaluation model was *** the empirical study on 300MW power plant construction projects,compared with the traditional back propagation neural network model,the result of improved bp neural network model is more accurate and effective.

关键词： Power Plant Construction Project Post-evaluation improved bp neural network

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Prediction of the Slope Solute Loss Based on bp neural network

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Computers, Materials & Continua 2021年第12期69卷 3871-3888页

作者： Xiaona Zhang Jie Feng Zhiguo Yu Zhen Hong Xinge Yun School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Water Resources Research Institute China Institute of Water Resources and Hydropower ResearchBeijing100044China Department of Geography and Environmental Sustainability University of OklahomaNorman73071USA

The existence of soil macropores is a common *** to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic *** this study,a new improved bp(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil *** rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input *** network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer *** cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer *** rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this *** error analysis and the performance comparison between the proposed method and traditional gradient descent method are *** results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent *** addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.

关键词： Solute loss soil macropores improved bp neural network slope

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An improved Velocity Forecasts Method Considering the Speed and Following Distance of Two Vehicles

An Improved Velocity Forecasts Method Considering the Speed ...

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第三十九届中国控制会议

作者： Hao Nie Naxin Cui Yi Du Ming Wang School of Control Science and Engineering Shandong University

Accurate vehicle speed prediction is essential to the optimal control of electric vehicles. Compared with the traditional vehicle speed prediction, with the rapid development of the Internet of Vehicles technology, it’s more practical to make predictions using the speed and traffic information of the vehicles ahead. In this paper, we propose an improved velocity prediction algorithm for intelligent connected vehicles based on the historical speed of the target vehicle, the historical speed of the preceding vehicle, and the following distance. First, build a single-lane traffic model in VISSIM and simulate it to get the speed and distance of the two vehicles. Then, an improved bp neural network prediction algorithm is proposed to train the neural network based on the obtained multivariate traffic information. Finally, the proposed velocity prediction algorithm is used to predict the speed of the target vehicle. In order to verify the method proposed in this paper, the prediction effect of the improved bp neural network prediction algorithm is compared with that of gray prediction and traditional bp neural network. The results show that the accuracy of velocity prediction is significantly improved in a simple traffic environment.

关键词： Internet of vehicles information Traffic flow model Velocity prediction improved bp neural network

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A NOVEL METHOD ON TOOL WEAR MONITORING

A NOVEL METHOD ON TOOL WEAR MONITORING

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2005年中国机械工程学会年会

作者： WANG ShiLong, YANG Dan, CHEN Wen (School of Software Engineering, Chongqing University, Chongqing, 400044, China)

＜正＞Tool wear monitoring can greatly enhance the ability of machine tool fault diagnosis and improving accuracy of manufacturing. Though many associated researches have been done by some specific methods, none satisfied results have been obtained. Artificial neural network is a rapidly developing area in the recent years, and has demonstrated a great application in manufacturing process monitoring. However, standard bp neural network has disadvantages of slow convergence rate, long period of learning time and low accuracy. Thus in this paper, the mathematical model of network is established, and an improved algorithm for bp neural network is presented, meanwhile how to decide the number of units in hidden layer is also discussed. The advantages of improved bp algorithm are confirmed with the help of simulation. Simulation results show that tool wear monitoring method based on improved bp neural network is applicable and effective.

关键词： improved bp neural network tool wear monitoring simulation

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Health Evaluation of Larix Kaempferi Plantation Ecosystem based on bp neural network Model

Health Evaluation of Larix Kaempferi Plantation Ecosystem ba...

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2012 International Conference of Health,Structure,Material and Environment(HSME 2012)

作者： HUI Shu-rong Dai Xiao-xiao LIU Hui LIU Qiang College of Science Shenyang Agricultural University College of Forestry Shenyang Agricultural University Peking University Shenzhen Graduate School College of Information and Electrical Engineering Shenyang Agricultural University

Based on the basic theory of the forest ecosystem,we build the index system to evaluate forest ecosystem health from the stand-scale and take advantage of improved bp neural network to evaluate the ecosystem health of Larix Kaempferi plantation in the Liaodong area *** then we analyze the stand-scale health grade status according to different slope aspect,forest age,average tree height and *** results indicate that we make the satisfactory process to research the complex forest ecosystem using the improved bp neural *** improved bp neural network which uses the momentum-adaptive learning rate adjustment algorithm and L-M learning rules decreases iteration times,makes the convergence speed very fast and improve the precision.

关键词： Larix Kaempferi Forest ecosystem The stand-scale improved bp neural network Index system

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Thickness Measuring of Multilayer Conductor Based on Eddy Current Testing

Thickness Measuring of Multilayer Conductor Based on Eddy Cu...

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2009中国控制与决策会议

作者： Li Guohou~1,Kang Xusheng~(1,2),Huang Pingjie~(1),Chen Peihua~1,Hou Dibo~1,Zhang Guangxin~1,Zhou Zekui~1 1.National Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China 2.Zhejiang University City College,Hangzhou 310015,China

＜正＞Eddy current testing（ECT） is becoming a widely used inspection technique,particularly in the aircraft,power and nuclear *** factors may affect the eddy current *** problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain *** objectives of this study are to introduce a method based on improved Back Propagation neural network（bpNN） to identify the multilayer thickness from their ECT *** simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.

关键词： Eddy-current Testing Inverse Problems Multilayer Conductor improved bp neural network Thickness Measuring

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