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Seabed Classification Using bp Neural network Based on GA

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Acta Oceanologica Sinica 2003年第4期22卷 523-531页

作者： Yang Fanlin1, Liu Jingnan2 1. GPS Engineering Research Center, Wuhan University, Wuhan 430079, China. 2. Presidential Secretariat, Wuhan University, Wuhan 430079, China GPS Engineering Research Center Wuhan University Wuhan 430079 China Presidential Secretariat Wuhan University Wuhan 430079 China

Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of different classes of seabed materials. In this paper, seabed image is classified according to bp neural network, and. Genetic Algorithm is adopted in train network in this paper. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It considers not only the spatial correlation between different pixels, but also the terrain coarseness. The texture is denoted by the statistics of the co-occurrence matrix. Double Blanket algorithm is used to calculate dimension. Because a uniform fractal may not be sufficient to describe a seafloor, two dimensions are calculated respectively by the upper blanket and the lower blanket. However, in sonar image, fractal has directivity, i. e. there are different dimensions in different direction. Dimensions are different in acrosstrack and alongtrack, so the average of four directions is used to solve this problem. Finally, the real data verify the algorithm. In this paper, one hidden layer including six nodes is adopted. The bp network is rapidly and accurately convergent through GA. Correct classification rate is 92.5 % in the result.

关键词： bp network co-occurrence matrix fractal classification genetic algorithin

An exploration of the uncertainty relation satisfied by bp network learning ability and generalization ability

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Science in China(Series F) 2004年第2期47卷 137-150页

作者： LIZuoyong PENGLihong ChengduUniversityofInformationTechnology Chengdu610041China EnvironmentalScienceResearchCenter XiamenUniversityXiamen361005China

This paper analyses the intrinsic relationship between the bp network learning ability and generalization ability and other influencing factors when the overfit occurs, and introduces the multiple correlation coefficient to describe the complexity of samples; it follows the calculation uncertainty principle and the minimum principle of neural network structural design, provides an analogy of the general uncertainty relation in the information transfer process, and ascertains the uncertainty relation between the training relative error of the training sample set, which reflects the network learning ability, and the test relative error of the test sample set, which represents the network generalization ability; through the simulation of bp network overfit numerical modeling test with different types of functions, it is ascertained that the overfit parameter q in the relation generally has a span of 7 × 10−3 to 7 × 10−2; the uncertainty relation then helps to obtain the formula for calculating the number of hidden nodes of a network with good generalization ability under the condition that multiple correlation coefficient is used to describe sample complexity and the given approximation error requirement is satisfied; the rationality of this formula is verified; this paper also points out that applying the bp network to the training process of the given sample set is the best method for stopping training that improves the generalization ability.

关键词： bp network learning ability generalization ability overfit relation network structure optimiza-tion.

To define the ratio of the dataset for training and testing based on the optimization of bp network system

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To define the ratio of the dataset for training and testing ...

Research and Implement for Vehicle License Plate Recognition Based on improved bp network

2010 4th International Conference on Intelligent Information Technology Application (IITA2010)

作者： Pu Chuanxin Northwestern Polytechnical University Xi’an China Yang Ting Qin Liping Northwestern Polytechnical University Xi’an China

The neural network has been introduced into the studies of credit risk assessment.However,the ratio of the dataset for training and testing is difficult to determine,so the neural network is not robust enough to give the judgment.Therefore,using the 2000 instances of personal consumer credit data set for approval of credit applications of a provincial-level China Construction Bank,for the bp neural network model,the study focused on the ratio of the dataset for training and testing.The results show that,when the ratio of the dataset for training and testing is 800:1200,the neural network model 2 for credit risk assessment has better performance.And it can achieve the desired accuracy and computational efficiency,so the bp network system for credit risk assessment is optimized.

关键词： credit risk assessment bp network

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Research and Implement for Vehicle License Plate Recognition...

Diagnostic Parameters Selection of Aero-engine Based on bp network

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE 2010)

作者： Zhiwen WANG1 2 1.Department of Computer and Engineering Guangxi University of Technology Liuzhou 545006 China Shaozi LI2 2.Cognitive Science Department of Xiamen University Xiamen 361005 China

In this article,bp network learning algorithm is improved by using momentum and genetic algorithm after analyzing the defects of the bp network learning algorithm.So the convergence rate of bp network is speeded up greatly.The vehicle license plate character image is segmented and feature is extracted by edge detection using sobel operator.And then license plates of cars are automatically recognized by using improved bp neural network.This algorithm can improve the speed and accuracy for the automatic identification of the license plates of vehicle.

关键词： bp network momentum genetic algorithm license plate location license plate character segmentation license plate character recognition

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Diagnostic Parameters Selection of Aero-engine Based on BP N...

Hybrid windowed networks for on-the-fly Doppler broadening in RMC code

第二十九届中国控制会议

作者： ZHANG Chuan-Chao China Aeronautic Poly-technolgy Establishment,Beijing 100028,P.R.China

Selecting diagnostic parameters of aero-engine performance is an essential approach to enhance the accuracy and reliability of aero-engine fault recognition.In view of the strong sensitivity of diagnostic parameters to the system state,an algorithm based on bp network was put forward and used for fault recognition of one aero-engine.The result shows that the diagnostic parameters selected by the algorithm are the key factors of aero-engine failure,whereby the algorithm ensures high precision of fault diagnosis.At the same time,the anti-interference ability of the algorithm enhances the validity of fault recognition.The algorithm can be also used for fault classification,fault diagnosis and performance monitoring aero-engine.

关键词： Aero-engine bp network Feature Selection Fault Recognition

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Nuclear Science and Techniques 2021年第6期32卷 70-82页

作者： Tian-Yi Huang Ze-Guang Li Kan Wang Xiao-Yu Guo Jin-Gang Liang Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Department of Engineering Physics Tsinghua UniversityBeijing 100084China

On-the-fly Doppler broadening of cross sections is important in Monte Carlo simulations,particularly in Monte Carlo neutronics-thermal hydraulics coupling simulations.Methods such as Target Motion Sampling(TMS)and windowed multipole as well as a method based on regression models have been developed to solve this problem.However,these methods have limitations such as the need for a cross section in an ACE format at a given temperature or a limited application energy range.In this study,a new on-the-fly Doppler broadening method based on a Back Propagation(bp)neural network,called hybrid windowed networks(HWN),is proposed to resolve the resonance energy range.In the HWN method,the resolved resonance energy range is divided into windows to guarantee an even distribution of resonance peaks.bp networks with specially designed structures and training parameters are trained to evaluate the cross section at a base temperature and the broadening coefficient.The HWN method is implemented in the Reactor Monte Carlo(RMC)code,and the microscopic cross sections and macroscopic results are compared.The results show that the HWN method can reduce the memory requirement for cross-sectional data by approximately 65%;moreover,it can generate keff,power distribution,and energy spectrum results with acceptable accuracy and a limited increase in the calculation time.The feasibility and effectiveness of the proposed HWN method are thus demonstrated.

关键词： Monte Carlo method Reactor Monte Carlo(RMC) On-the-fly Doppler broadening bp network

Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization

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The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 17-25,42页

作者： Yang Jianjian Zhang Qiang Wang Xiaolin Du Yibo Wang Chao Wu Miao Department of Mechanical Electrical and Information EngineeringChina University of Mining and Technology(Beijing)Beijing 100083China Tian Di Science&Technology Co. LtdBeijing 100013China

The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization(RSAPSO). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization(PSO) algorithms in a back propagation(bp) network are compared experimentally. The results show that the RSAPSO algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with PSO, it has a faster optimization speed and higher accuracy. Finally, the RSAPSO algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97.5%, and the average recognition rate of the two kinds of motor data reaches 100%, which prove the universality of the method.

关键词： fault diagnosis bp network gradient descent PSO algorithm

Obtaining the Rea Height Profile of Ionospheric Electron Density with Back Propagation network

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Wuhan University Journal of Natural Sciences 1999年第2期4卷 64-66页

作者： Wu, Qing-lin Li, Xian-wen Wuhan Univ Wuhan China

A new method to obtain the real height profile of ionospheric electron density is described, which is called 'approximation with network'. Also it has been demonstrated how to solve the group path integral equation with Back Propagation(bp) neural network, and given the computation results with this method for a parabolic model.Simulation results show that the proposed method is effective.

关键词： approximation with network bp network ionogram group path

3D geological modeling for mineral resource assessment of the Tongshan Cu deposit,Heilongjiang Province,China

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Geoscience Frontiers 2012年第4期3卷 483-491页

作者： Gongwen Wang Lei Huang State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences Beijing 100083 China

Three-dimensional geological modeling （3DGM） assists geologists to quantitatively study in three-dimensional （3D） space structures that define temporal and spatial relationships between geological objects. The 3D property model can also be used to infer or deduce causes of geological objects. 3DGM technology provides technical support for extraction of diverse geoscience information, 3D modeling, and quantitative calculation of mineral resources. Based on metallogenic concepts and an ore deposit model, 3DGM technology is applied to analyze geological characteristics of the Tongshan Cu deposit in order to define a metallogenic model and develop a virtual borehole technology; a bp neural network and a 3D interpolation technique were combined to integrate multiple geoscience information in a 3D environment. The results indicate：（1） on basis of the concept of magmatic-hydrothermal Cu polymetallic mineraliza- tion and a porphyry Cu deposit model, a spatial relational database of multiple geoscience information for mineralization in the study area （geology, geophysics, geochemistry, borehole, and cross-section data） was established, and 3D metallogenic geological objects including mineralization stratum, granodiorite, alteration rock, and magnetic anomaly were constructed; （2） on basis of the 3D ore deposit model, 23,800 effective surveys from 94 boreholes and 21 sections were applied to establish 3D orebody models with a kriging interpolation method; （3） combined 23,800 surveys involving 21 sections, using VC＋＋ and OpenGL platform, virtual borehole and virtual section with bp network, and an improved inverse distance interpolation （IDW） method were used to predict and delineate mineralization potential targets （Cu-grade of cell not less than 0.1%）; （4） comparison of 3D ore bodies, metallogenic geological objects of mineralization, and potential targets of mineralization models in the study area, delineated the 3D spatial and temporal relationship and causal processes among the ore bodies, alteration rock, metallo- ge

关键词： Three-dimensional geological modeling （3DGM） Virtual borehole Virtual section bp network Interpolation Tongshan Cu deposit