Network Traffic Prediction based on Particle Swarm BP Neural Network
Yan Zhu; Guanghua Zhang; Jing Qiu
2013-01-01
The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and part...
Network Traffic Prediction based on Particle Swarm BP Neural Network
Directory of Open Access Journals (Sweden)
Yan Zhu
2013-11-01
Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.
Sensor Temperature Compensation Technique Simulation Based on BP Neural Network
Xiangwu Wei
2013-01-01
Innovatively, neural network function programming in the BPNN (BP neural network) tool boxes from MATLAB are applied, and data processing is done about CYJ-101 pressure sensor, and the problem of the sensor temperature compensation is solved. The paper has made the pressure sensors major sensors and temperature sensor assistant sensors, input the voltage signal from the two sensors into the established BP neural network model, and done the simulation under the NN Toolbox environment of MATLAB...
Uncertain Relation Suited to Overfitting of BP Neural Network
Institute of Scientific and Technical Information of China (English)
REN Jiping; LI Zuoyong; JIANG Chunhua; YANG Haomiao
2004-01-01
A general uncertainty relation between the change of weighted value which represents learning ability and the discrimination error of unlearning sample sets which represents generalization ability is revealed in the modeling of back propagation (BP) neural network.Tests of numerical simulation for multitype of complicated functions are carried out to determine the value distribution (l×10-5～5×10-4) of overfitting parameter in the uncertainty relation.Based on the uncertainty relation,the overfitting in the training process of given sample sets using BP neural network can be judged.
Sub-pixel mapping method based on BP neural network
Institute of Scientific and Technical Information of China (English)
LI Jiao; WANG Li-guo; ZHANG Ye; GU Yan-feng
2009-01-01
A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
Risk assessment of logistics outsourcing based on BP neural network
Liu, Xiaofeng; Tian, Zi-you
The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.
The Application of BP Neural Network In Oil-Field
Directory of Open Access Journals (Sweden)
Pei-Ying ZHANG
2013-09-01
Full Text Available Aiming at the situation that many techniques of production performance analysis acquire lots of data and are expensive considering the computational and human resources, and their applications are limited, this paper puts forward a new method to analyze the production performance of oil-field based on the BP neural network. It builds a dataset with some available measured data such as well logs and production history, then, builds a field-wide production model by neural network technique, a model will be used to predict. The technique is verified, which shows that the predicted results are consistent with the maximum error of rate of oil production lower than 7% and maximum error of rate of water production lower than 5%, having certain application and research value.
Seabed Classification Using BP Neural Network Based on GA
Institute of Scientific and Technical Information of China (English)
Yang Fanlin; Liu Jingnan
2003-01-01
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.
Spiking DNA Computing with applications to BP Neural Networks Classification
Directory of Open Access Journals (Sweden)
Wenke Zang
2012-08-01
Full Text Available The study uses the idea of the extreme parallel to solve the BP neural network classification. Modification of the weights is not the traditional method which is to modify the connection weights between neurons repeatedly, but to find a group of weights in all possible weights combinations. The groups of weights are suitable for the relationship of the ideal input and the ideal output. Therefore, the model has some advantages compared with the traditional serial model in time miscellaneous. In the actual DNA computing, we also associate the coding problem with the model design. The coding problem is an important issue worthy to study in the DNA computing. There are many factors affecting the coding. The coding in this study is made when certain factors are overlooked.
Optimization Design based on BP Neural Network and GA Method
Directory of Open Access Journals (Sweden)
Bing Wang
2013-12-01
Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
A Worsted Yarn Virtual Production System Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
董奎勇; 于伟东
2004-01-01
Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Zhang Cuiping; Yang Qingfo
2003-01-01
According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.
Research on fault location technology based on BP neural network in DWDM optical network
Institute of Scientific and Technical Information of China (English)
LIAO Xiao-min; ZHANG Yin-fa; YANG Shi-ping; LIN Chu-shan
2008-01-01
BP neural network is introduced to the fault location field of DWDM optical network in this paper. The alarm characteris-tics of the optical network equipments are discussed, and alarm vector and fault vector diagrams are generated by analyzingsome typical instances. A 17×14×18 BP neural network structure is constructed and trained by using MATLAB. Bycomparing the training performances, the best training algorithm of fault location among the three training algorithms ischosen. Numerical simulation results indicate that the sum squared error (SSE) of fault location is less than 0.01, and theprocessing time is less than 100 ms. This method not only well deals with the missing alarms or false alarms, but alsoimproves the fault location accuracy and real-time ability.
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Wei He
2013-01-01
Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, th...
A BP neural network model for sea state recognition using laser altimeter
Shi, Chun-bo; Jia, Xiao-dong; Li, Sheng; Wang, Zhen
2009-07-01
A BP neural network method for the recognition of sea state in laser altimeter is presented in this paper. Sea wave is the typical stochastic disturbance factor of laser altimeter effecting on low-altitude defense penetration of the intelligent antiship missiles, the recognition of sea state is studied in order to satisfy the practical needs of flying over the ocean. The BP neural network fed with the feature vector of laser range-measurement presents the analysis of features and outputs the estimation result of sea state. The two most distinguishing features are the mean and the variance of the sea echo, which are extracted from the distance characteristics of sea echo using general theory of statistics. The use of a feedforward network trained with the back-propagation algorithm is also investigated. The BP neural network is trained using sample data set to the neural network, and then the BP neural network trained is tested to recognize the sea state waiting for the classification. The network output shows the recognition accuracy of the model can up to 88%, and the results of tests show that the BP neural network model for the recognition of sea state is feasible and effective.
An improved BP artificial neural network algorithm for urban traffic flow intelligent prediction
Institute of Scientific and Technical Information of China (English)
XIONG Shi-yong; ZHANG Yi
2009-01-01
The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Directory of Open Access Journals (Sweden)
Wei He
2013-01-01
Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.
Pulse frequency classification based on BP neural network
Institute of Scientific and Technical Information of China (English)
WANG Rui; WANG Xu; YANG Dan; FU Rong
2006-01-01
In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.
Application of BP neural networks in non-linearity correction of optical tweezers
Institute of Scientific and Technical Information of China (English)
Ziqiang WANG; Yinmei LI; Liren LOU; Henghua WEI; Zhong WANG
2008-01-01
The back-propagation (BP) neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem. Considering the low convergence rate of the BP algo-rithm, the Levenberg-Marquardt (LM) algorithm is used to improve the BP network. The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics. The result shows that with the nonlinear correction using BP net-works, the range of force measurement of an optical tweezer system is enlarged by 30% and the precision is also improved compared with the polynomial fitting method. It is demonstrated that nonlinear correction by the neural network method effectively improves the per-formance of optical tweezers without adding or changing the measuring system.
Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
ZHANG Jun-hong; XIE An-guo; SHEN Feng-man
2007-01-01
A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.
Mountain ground movement prediction caused by mining based on BP-neural network
Institute of Scientific and Technical Information of China (English)
ZHANG He-sheng; LIU Li-juan; LIU Hong-fu
2011-01-01
Six main influencing factors: slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness,were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the BP neural network to predict mountain mining subsidence.
Research on safety assessment of gas explosion hazard in heading face based on BP neural network
Institute of Scientific and Technical Information of China (English)
TIAN Shui-cheng; ZHU Li-jun; CHEN Yong-gang; WANG Li
2005-01-01
According to hazard theory and the principle of selecting assessment index,combining the causes and mechanism of gas explosion, established assessment index system of gas explosion in heading face. Based on the method of gray clustering, principle of BP neural network and characters of gas explosion in heading face, safety assessment procedural diagram of BP neural network on gas explosion hazard in heading face is designed. Meanwhile, concrete heading face of the gas explosion hazard is assessed by safety assessment method of BP neural network and grades of comprehensive safety assessment are got. The static and dynamic safety assessment can be achieved by this method. It is practical to improve safety management and to develop safety assessment technology in coalmine.
Application of New Type BP Neural Networks for Magnetic Measurement
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods to solve this kind of inverse problem has all kinds of shortcomings, BPNN (Back Propagation Neural Networks) method can be used to solve this typical inverse problem fast enough for real time measurement. In the traditional BPNN method, gradient descent search method is performed for error propagation. In this paper the authors propose a new algorithm that Newton method is performed for error propagation. For the cost function is highly nonconvex in the magnetic measurement problem, the new kind of BPNN can get convergent results quickly and precisely. A simulation result for this method is also presented.
Adaptive tracking controller using BP neural networks for a class of nonlinear systems
Institute of Scientific and Technical Information of China (English)
刘子龙; 刘国忠; 刘洁
2004-01-01
An BP neural-network-based adaptive control (NNAC) design method is described whose aim is to control a class of partially unknown nonlinear systems. Making use of the online identification of BP neural networks, the results of the identification could be used into the parameters of the controller. Not only the strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero by Lyapunov theory in the process of this design method.And a simulation example is also presented to evaluate the effectiveness of the design.
Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network
International Nuclear Information System (INIS)
Coal ash fusion temperature is important to boiler designers and operators of power plants. Fusion temperature is determined by the chemical composition of coal ash, however, their relationships are not precisely known. A novel neural network, ACO-BP neural network, is used to model coal ash fusion temperature based on its chemical composition. Ant colony optimization (ACO) is an ecological system algorithm, which draws its inspiration from the foraging behavior of real ants. A three-layer network is designed with 10 hidden nodes. The oxide contents consist of the inputs of the network and the fusion temperature is the output. Data on 80 typical Chinese coal ash samples were used for training and testing. Results show that ACO-BP neural network can obtain better performance compared with empirical formulas and BP neural network. The well-trained neural network can be used as a useful tool to predict coal ash fusion temperature according to the oxide contents of the coal ash
Remote sensing monitoring of a bamboo forest based on BP neural network
Institute of Scientific and Technical Information of China (English)
Yongjun SHI; Xiaojun XU; Huaqiang DU; Guomo ZHOU; Wei JIN; Yufeng ZHOU
2009-01-01
The collection of information on bamboo forests plays a crucial role in the calculation of carbon content reserves, and the acquisition of high-precision information will be good for reducing estimation errors. High precision is obtained with the adoption of a back propagation (BP) neural network to extract information on bamboo forests from Enhanced Thematic Mapper + (ETM +) remote sensing images with the assistance of neural network modules provided by Matlab. We obtained a production precision of 84.04% and a user precision of 98.75%. We also conducted a comparison of classification differences of three training functions, i.e., the, LevenbergMarquardt BP algorithm function (Trainlm), a gradient decreasing function of adaptive learning rate BP (Traingda), and a gradient lowering momentum BP algorithm function (Traingdm). Our analysis suggests that Traingda had the highest precision while Trainlm function required the shortest training time.
Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
ZHOU Mei-hua; WANG Fu-dong; ZHANG Hong-hong
2006-01-01
For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning results of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.
Safety Prediction Analysis of the Agricultural Products Processing Based on the BP Neural Network
Directory of Open Access Journals (Sweden)
Jing Li
2015-09-01
Full Text Available By using BP neural network algorithm, this study aims at prompting the accuracy of safety prediction of the agriculture products processing. The science prediction of the deep-frozen dumplings' shelf-life has an important guiding significance for human health and the safety of quick-frozen food. Artificial Neural Network (ANN is a kind of information processing system which is established by simulating the human nervous system. Based on these, by using the effective theory of integrated temperature combined with BP neural network method to predict the shelf-life of the frozen dumplings in this study, we aim at providing a theory basis for monitoring and controlling the quality change in the storage process of deep-frozen dumplings’ temperature fluctuations. Finally, an example is given to show that it is very effective by using the method adopted in this study.
Application of BP neural network to semi-solid apparent viscosity simulation
Institute of Scientific and Technical Information of China (English)
罗中华; 张质良
2003-01-01
Two-layer BP neural network was designed for the semi-solid apparent viscosity simulation. The apparent viscosity simulations of Sn-15%Pb alloy and Al-4.5%Cu-1.5%Mg alloy stirred slurries were carried out. The trained BP neural network forecast the curve of the apparent viscosity versus solid volume fraction of Sn-15%Pb alloy, under the condition of shear rate, =150 s-1, and cooling rate of G=0.33 ℃/min. The simulation results are well agreement with the experimental values given in references The fitted mathematical formula of Sn-15%Pb alloy apparent viscosity, under the condition of the cooling rate of G=0.33 ℃/min, was obtained by optimization method. The results show that the precision of apparent viscosity simulation value by neural network is much better than that of its calculation value by fitted mathematical formula.
A Prediction Model of Peasants’ Income in China Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
2011-01-01
According to the related data affecting the peasants’ income in China in the years 1978-2008,a total of 13 indices are selected,such as agricultural population,output value of primary industry,and rural employees.Based on the standardized method and BP neural network method,the peasants’ income and the artificial neural network model are established and analyzed.Results show that the simulation value agrees well with the real value;the neural network model with improved BP algorithm has high prediction accuracy,rapid convergence rate and good generalization ability.Finally,suggestions are put forward to increase the peasants’ income,such as promoting the process of urbanization,developing small and medium-sized enterprises in rural areas,encouraging intensive operation,and strengthening the rural infrastructure and agricultural science and technology input.
Circuit Design of On-Chip BP Learning Neural Network with Programmable Neuron Characteristics
Institute of Scientific and Technical Information of China (English)
卢纯; 石秉学; 陈卢
2000-01-01
A circuit system of on chip BP(Back-Propagation) learning neural network with pro grammable neurons has been designed,which comprises a feedforward network,an error backpropagation network and a weight updating circuit. It has the merits of simplicity,programmability, speedness,low power-consumption and high density. A novel neuron circuit with pro grammable parameters has been proposed. It generates not only the sigmoidal function but also its derivative. HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1.2tμm CMOS process. The results show that both functions are matched with their respec ive ideal functions very well. The non-linear partition problem is used to verify the operation of the network. The simulation result shows the superior performance of this BP neural network with on-chip learning.
Coal mine safety production forewarning based on improved BP neural network
Institute of Scientific and Technical Information of China (English)
Wang Ying; Lu Cuijie; Zuo Cuiping
2015-01-01
Firstly, the early warning index system of coal mine safety production was given from four aspects as per-sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO-BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarning management of coal mine safety production.
Institute of Scientific and Technical Information of China (English)
LONG Jiangqi; LAN Fengchong; CHEN Jiqing; YU Ping
2009-01-01
For optimal design of mechanical clinching steel-aluminum joints, the back propagation (BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm (GA) is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM(R) Tog-L-Loc system with different technologic parameters. The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network (ANN) for training. The validating samples' experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.
Institute of Scientific and Technical Information of China (English)
YANG Yang; LI Kai-yang
2006-01-01
The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication-the highest prediction rate 75.65%, the average prediction rate 65.04%.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)
2006-10-15
The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
A new grey forecasting model based on BP neural network and Markov chain
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system's known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model (GNNM(1,1)) was built. In order to reduce stochastic phenomenon in GNNM(1,1), the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model(MNNGM(1,1)) was proposed by combining Markov chain with GNNM(1,1). Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM(1,1) and GNNM(1,1) with the proposed model, the results indicate that the absolute mean error of MNNGM(1,1) is about 0.4 times of GNNM(1,1) and 0.2 times of GM(1,1), and the mean square error of MNNGM(1,1) is about 0.25 times of GNNM(1,1) and 0.1 times of GM(1,1).
The risk evaluation of mine coal-dust explosion based on BP neural network
Institute of Scientific and Technical Information of China (English)
CHEN Lian-jun; CHENG Wei-min
2007-01-01
Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and factor of men, which affect the safety and reliability of coal-dust explosion risk system and then builds up the risk factor indices of coal-dust explosion according to analysis of conditions inducing the coal-dust explosion. It fixes the risk degree of coal-dust explosion risk system by analyzing loss probability and loss scope of risk system and by means of the probabilistic hazard evaluation method and risk matrix method, etc.. According to the feature of strong capability of nonlinear approximation of BP neural network, the paper designed the structure of BP neural network for the risk evaluation of the mine coal-dust explosion with BP neural network. And the weight of the network was finally determined by training the given samples so that the risk degree of samples to be measured could be exactly evaluated and the risk of mine coal-dust explosion could be alarmed in good time.
D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process
Directory of Open Access Journals (Sweden)
Shu-zhi Gao
2014-01-01
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Study of predicting breakdown voltage of stator insulation in generator based on BP neural network
Institute of Scientific and Technical Information of China (English)
Jiang Yuao; Zhang Aide; Liu Libing; Du Yu; Gao Naikui; Peng Zongren
2007-01-01
The breakdown voltage plays an important role in evaluating residual life of stator insulation in generator. In this paper, we discussed BP neural network that was used to predict the breakdown voltage of stator insulation in generator of 300 MW/18 kV. At first the neural network has been trained by the samples that include the varieties of dielectric loss factor tanδ, the partial discharge parameters and breakdown voltage. Then we tried to predict the breakdown voltage of samples and stator insulations subjected to multi-stress aging by the trained neural network. We found that it's feasible and accurate to predict the voltage. This method can be applied to predict breakdown voltage of other generators which have the same insulation structure and material.
Particle Swarm Optimization-based BP Neural Network for UHV DC Insulator Pollution Forecasting
Directory of Open Access Journals (Sweden)
Fangcheng Lü
2014-02-01
Full Text Available In order to realize the forecasting of the UHV DC insulator's pollution conditions, we introduced a PSOBP algorithm. A BP neural network (BPNN with leakage current, temperature, relative humidity and dew point as input neurons, and ESDD as output neuron was built to forecast the ESDD. The PSO was used to optimize the the BPNN, which had great improved the convergence rate of the BP neural network. The dew point as a brand new input unit has improved the iteration speed of the PSOBP algorithm in this study. It was the first time that the PSOBP algorithm was applied to the UHV DC insulator pollution forecasting. The experiment results showed that the method had great advantages in accuracy and speed of convergence. The research showed that this algorithm was suitable for the UHV DC insulator pollution forecasting.
Effective Multifocus Image Fusion Based on HVS and BP Neural Network
Directory of Open Access Journals (Sweden)
Yong Yang
2014-01-01
Full Text Available The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS and back propagation (BP neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations.
Water quality forecast through application of BP neural network at Yuqiao reservoir
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.
Institute of Scientific and Technical Information of China (English)
季学武; 王健; 赵又群; 刘亚辉; 臧利国; 李波
2015-01-01
In order to diminish the impacts of external disturbance such as parking speed fluctuation and model un-certainty existing in steering kinematics, this paper presents a parallel path tracking method for vehicle based on pre-view back propagation (BP) neural network PID controller. The forward BP neural network can adjust the parameters of PID controller in real time. The preview time is optimized by considering path curvature, change in curvature and road boundaries. A fuzzy controller considering barriers and different road conditions is built to select the starting po-sition. In addition, a kind of path planning technology satisfying the requirement of obstacle avoidance is introduced. In order to solve the problem of discontinuous curvature, cubic B spline curve is used for curve fitting. The simulation results and real vehicle tests validate the effectiveness of the proposed path planning and tracking methods.
Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum
Mu, Tao-tao; Chen, Si-ying; Zhang, Yin-chao; Guo, Pan; Chen, He; Zhang, Hong-yan; Liu, Xiao-hua; Wang, Yuan; Bu, Zhi-chao
2013-09-01
In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.
Learning algorithm and application of quantum BP neural networks based on universal quantum gates
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A quantum BP neural networks model with learning algorithm is proposed.First,based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate,a quantum neuron model is constructed,which is composed of input,phase rotation,aggregation,reversal rotation and output.In this model,the input is described by qubits,and the output is given by the probability of the state in which |1＞ is observed.The phase rotation and the reversal rotation are performed by the universal quantum gates.Secondly,the quantum BP neural networks model is constructed,in which the output layer and the hide layer are quantum neurons.With the application of the gradient descent algorithm,a learning algorithm of the model is proposed,and the continuity of the model is proved.It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed,convergence rate and robustness,by two application examples of pattern recognition and function approximation.
Research on the Prediction Model of CPU Utilization Based on ARIMA-BP Neural Network
Directory of Open Access Journals (Sweden)
Wang Jina
2016-01-01
Full Text Available The dynamic deployment technology of the virtual machine is one of the current cloud computing research focuses. The traditional methods mainly work after the degradation of the service performance that usually lag. To solve the problem a new prediction model based on the CPU utilization is constructed in this paper. A reference offered by the new prediction model of the CPU utilization is provided to the VM dynamic deployment process which will speed to finish the deployment process before the degradation of the service performance. By this method it not only ensure the quality of services but also improve the server performance and resource utilization. The new prediction method of the CPU utilization based on the ARIMA-BP neural network mainly include four parts: preprocess the collected data, build the predictive model of ARIMA-BP neural network, modify the nonlinear residuals of the time series by the BP prediction algorithm and obtain the prediction results by analyzing the above data comprehensively.
BP-Neural-Network-Based Tool Wear Monitoring by Using Wavelet Decomposition of the Power Spectrum
Institute of Scientific and Technical Information of China (English)
ZHENG Jian-ming; XI Chang-qing; LI Yan; XIAO Ji-ming
2004-01-01
In a drilling process, the power spectrum of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the power spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet transform and extraction of the low frequency decomposition coefficient us the envelope information of the power spectrum. Intelligent identification of the tool wear status is achieved in the drilling process through fusing the wavelet decomposition coefficient of the power spectrum by using a BP ( Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural networks show high identification precision and the ability of extension.
Institute of Scientific and Technical Information of China (English)
LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi
2005-01-01
An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.
An Evaluating Model for Enterprise's Innovation Capability Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
HU Wei-qiang; WANG Li-xin
2007-01-01
To meet the challenge of knowledge-based economy in the 21st century, scientifically evaluating the innovation capability is important to strengthen the international competence and acquire long-term competitive advantage for Chinese enterprises. In the article, based on the description of concept and structure of enterprise's innovation capability, the evaluation index system of innovation capability is established according to Analytic Hierarchy Process (AHP). In succession, evaluation model based on Back Propagation (BP) neural network is put forward, which provides some theoretic guidance to scientifically evaluating the innovation capability of Chinese enterprises.
NEURAL NETWORK BP MODEL APPROXIMATION AND PREDICTION OF COMPLICATED WEATHER SYSTEMS
Institute of Scientific and Technical Information of China (English)
张韧; 余志豪; 蒋全荣
2001-01-01
An artificial neural network BP model and its revised algorithm are used to approximate quite successfully a Lorenz chaotic dynamic system and the mapping relation is established between the indices of Southern Oscillation and equatorial zonal wind and lagged equatorial eastern Pacific sea surface temperature (SST) in the context of NCEP/NCAR data, and thereby a model is prepared.The constructed net model shows fairly high fit precision and feasible prediction accuracy, thus making itself of some usefulness to the prognosis of intricate weather systems.
Prediction of 2A70 aluminum alloy flow stress based on BP artificial neural network
Institute of Scientific and Technical Information of China (English)
刘芳; 单德彬; 吕炎; 杨玉英
2004-01-01
The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble - 1500 thermal simulator over 360 ～480°C with strain rates in the range of 0.01 ～ 1 s- 1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network (ANN) model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ± 3% for the sampled data while it was less than ± 6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.
DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL
Institute of Scientific and Technical Information of China (English)
HE Hui; JIN Long; QIN Zhi-nian; YUAN Li-jun
2007-01-01
Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.
Directory of Open Access Journals (Sweden)
Reza Azad
2014-06-01
Full Text Available In recent years, face detection has been thoroughly studied due to its wide potential applications, including face recognition, human-computer interaction, video surveillance, etc.In this paper, a new and illumination invariant face detection method, based on features inspired by the human's visual cortexand applying BP neural network on the extracted featureset is proposed.A feature set is extracted from face and non-face images, by means of a feed-forward model, which contains a view and illumination invariant C2 features from all images in the dataset. Then, these C2 feature vector which derived from a cortex-like mechanism passed to a BP neural network. In the result part, the proposed approach is applied on FEI and Wild face detection databases and high accuracy rate is achieved. In addition, experimental results have demonstrated our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the first best result on all tested challenging face detection databases.
BP neural network based online prediction of steam turbine exhaust dryness
Institute of Scientific and Technical Information of China (English)
XIE Danmei; CHEN Chang; XIONG Yangheng; GAO Shang; WANG Chun
2014-01-01
In large scale condensing turbine unit,the exhaust status always lies in wet steam area.Due to the lack of effective measuring method,the exhaust dryness of the steam turbine is difficult to obtain di-rectly,which has been the difficult problem in online economic analysis for thermal power units.By taking an N1000-25/600/600 ultra-supercritical steam turbine as an example,the nonlinear mapping ability of BP neural network was used to establish a model which can reflect the relationship between exhaust dryness and unit load and exhaust pressure.After learning and training under some typical conditions,this model was used for exhaust dryness online calculation under full condition.The results show the final error of the training samples and verifying samples were controlled within -0.006 1 and -0.001 0,which satisfies the accuracy requirement for engineering calculation,indicating the established BP neural network can be used in exhaust dryness prediction.
Application of genetic BP network to discriminating earthquakes and explosions
Institute of Scientific and Technical Information of China (English)
边银菊
2002-01-01
In this paper, we develop GA-BP algorithm by combining genetic algorithm (GA) with back propagation (BP) algorithm and establish genetic BP neural network. We also applied BP neural network based on BP algorithm and genetic BP neural network based on GA-BP algorithm to discriminate earthquakes and explosions. The obtained result shows that the discriminating performance of genetic BP network is slightly better than that of BP network.
Institute of Scientific and Technical Information of China (English)
ZHANG Wei; LIANG Cheng-hao
2005-01-01
Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.
Prediction Method of Vessel Maintenance Outlay Based on the BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭冰冰; 黎放; 王威
2002-01-01
With the development of technology, the performance of vessel equipment is improved, the structure is more complicated, the automation level is enhanced, the source needed by maintenance is increased and the outlay is rising day by day. For these questions, this paper analyzes the factors that affect the outlay of equipment maintenance, and describes the computational principle of the BP (back propagation) artificial neural network and its applications in the maintenance of naval ship and craft. Finally, a dynamic investment prediction model of outlay for the military equipment maintenance is designed. It is important for decreasing the entire ilfe period outlay and drawing up the maintenance plan and programming to analyze the position and action of maintenance outlay in entire life period outlay.
The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
LI Shao-kun; SUO Xing-mei; BAI Zhong-ying; QI Zhi-li; Liu Xiao-hong; GAO Shi-ju; ZHAO Shuang-ning
2002-01-01
Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixei labeling was used to segment image and extract feature. (3)A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.
The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network
Directory of Open Access Journals (Sweden)
Zhang Yanhui
2015-01-01
Full Text Available With the rapid development of science and technology, data analysis has become an indispensable part of people’s work and life. Horizontal bar training has multiple categories. It is an emphasis for the re-search of related workers that categories of the training and match should be reduced. The application of data mining methods is discussed based on the problem of reducing categories of horizontal bar training. The BP neural network is applied to the cluster analysis and the principal component analysis, which are used to evaluate horizontal bar training. Two kinds of data mining methods are analyzed from two aspects, namely the operational convenience of data mining and the rationality of results. It turns out that the principal component analysis is more suitable for data processing of horizontal bar training.
Load reduction test method of similarity theory and BP neural networks of large cranes
Yang, Ruigang; Duan, Zhibin; Lu, Yi; Wang, Lei; Xu, Gening
2016-01-01
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.
Wang, Jun; Sheng, Zheng; Zhou, Bihua; Zhou, Shudao
2014-02-01
The method of using the back propagation neural network improved by cuckoo search algorithm (hereafter CS-BP neural network) to forecast lightning occurrence from sounding-derived indices over Nanjing is presented. The general distribution features of lightning activities over Nanjing area are summarized and analyzed first. The sounding data of 156 thunderstorm days and 164 fair-weather days during the years 2007-2012 are used to calculate the values of sounding-derived indices. The indices are pre-filtered using singular spectrum analysis (hereafter SSA) as preprocessing technique and 4 most pertinent indices (namely CAPE, K, JI and SWEAT) are determined as inputs of CS-BP network by a linear bivariate analysis and selection algorithm. The cases of 2007-2010 are used to train CS-BP network and the cases of 2011-2012 are used as an independent sample to test the forecast performance. Some statistical skill score parameters (namely POD, SAR, CSI, et.al.) indicate that the CS-BP model excels in lightning forecasting and has a better performance compared with the traditional BP neural network and linear multiregression method.
Antenna Recognition Based on BP Neural Network%基于BP神经网络的天线识别
Institute of Scientific and Technical Information of China (English)
赵春燕; 石丹; 高攸纲; 陈亚洲
2014-01-01
本文比较研究了BP神经网络中的几种常用算法，针对这些不同算法下的BP神经网络进行训练，并得出了各自网络的性能。在此基础上，针对经典BP算法和LM算法进行对比研究，找到LM算法的改进之处。此外，在实际的应用中表明，不仅不同的BP算法对网络的运算速度、泛化能力等有较大的影响，而且BP神经网络对隐含层神经元数目也很敏感。我们希望在BP神经网络的基础上，构建一种合适的天线模型，来应用于天线的分类识别，这将具有很大的现实意义。%This paper studies several commonly used algorithms in the BP neural network. The BP neural network under these different algorithms is trained to can see the various performance of their networks. The study of both classical BP algorithm and LM algorithm will find the improvements of LM algorithm. In addition, practical applications show the following things. For one hand, different BP algorithm influences the speed of the network. For the other hand, the number of hidden layer neurons is also a sensitive factor to the performance of BP neural network. On the basis of the BP neural network, we want to build a suitable antenna model and use it in the identification of the antenna, so it has great practical significance.
Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu
2013-01-01
As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…
Energy Technology Data Exchange (ETDEWEB)
Zhu, H.; Chang, W.; Zhang, B. [China University of Mining and Technology, Beijing (China)
2007-05-15
Back-propagation (BP) neural network analysis based on the difference- source gas emission quantity prediction theory was applied to predict the quantity of gas emitted from the coal seam being mined, the neighbouring coal seam and the goaf of the working face. Three separate gas emission prediction neural network models were established for these. The prediction model of the coal seam being mined was made up of three layers and nine parameters; that of the neighbouring coal seam was made up of three layers and eight parameters; and that of the goaf of three layers and four parameters. The difference-source gas emission prediction model can greatly improve prediction accuracy. BP neural network analysis using Matlab software was applied in a coal mine. 10 refs., 2 figs., 3 tabs.
Directory of Open Access Journals (Sweden)
Zeng Jie
2016-01-01
Full Text Available Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.
OPTIMIZATION OF INJECTION MOLDING PROCESS BASED ON NUMERICAL SIMULATIONAND BP NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
王玉; 邢渊; 阮雪榆
2001-01-01
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.
Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network
Institute of Scientific and Technical Information of China (English)
彭源; 莫玉龙
2003-01-01
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery.Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between theimpedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.
The Application of LM-BP Neural Network in the Prediction of Total Output Value of Agriculture
Institute of Scientific and Technical Information of China (English)
Zimin; ZHANG; Yanying; FAN; Guanping; CHEN
2015-01-01
Gross agricultural product is an important indication to measure the agricultural development level of a region. It would be affected by many factors,having the characteristics of non- linearity. For this reason,LM- BP neural network was put forward as the model and method for predicting gross agricultural product. Taking the indications of the sown area of crop,the output of grain,sugarcane,cassava,tea,meat,aquatic products,turpentine and camellia seed,etc. as inputs,during 2000 to 2012 in Guangxi,the gross agricultural product data from the analysis of simulation experiment show that the prediction of LM- BP neural network fits well with actual results.
International Nuclear Information System (INIS)
Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms
Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment
Institute of Scientific and Technical Information of China (English)
杨卫平; 廖宁放; 柴冰华; 胡中平; 白力; 栗兆剑
2003-01-01
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.
基于 BP 神经网络的结构损伤识别研究%Study on structural damage identification based on BP neural network
Institute of Scientific and Technical Information of China (English)
常虹; 尹春超
2014-01-01
This paper studied the structural damage , chose the change rate of squared natural frequency of the structure as characteristic parameter established 12 ×25 ×1 BP neural net-work using the mean square error function as target error function , selected gradient descent momentum learning function and L -M optimization algorithm and set up a three layers BP neural network to test four layers steel frame structure ’ s damage .%针对结构的损伤识别进行了研究，选取结构固有频率平方变化比作为特征参数，建立12×25×1 BP网络结构，采用均方误差函数目标误差函数，学习函数选取梯度下降动量学习函数和L-M优化算法，对四层钢框架结构的损伤进行了检测。
The Signal Extraction of Fetal Heart Rate Based on Wavelet Transform and BP Neural Network
Institute of Scientific and Technical Information of China (English)
YANG Xiao-hong; ZHANG Bang-cheng; FU Hu-dai
2005-01-01
This paper briefly introduces the collection and recognition of biomedical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI, the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women' s signals of FM were collected successfully by using the sensor. The correct of BP network recognition is about 83.3% by using the above measure.
Flux-measuring approach of high temperature metal liquid based on BP neural networks
Institute of Scientific and Technical Information of China (English)
胡燕瑜; 桂卫华; 李勇刚
2003-01-01
A soft-measuring approach is presented to measure the flux of liquid zinc with high temperature andcausticity. By constructing mathematical model based on neural networks, weighing the mass of liquid zinc, the fluxof liquid zinc is acquired indirectly, the measuring on line and flux control are realized. Simulation results and indus-trial practice demonstrate that the relative error between the estimated flux value and practical measured flux value islower than 1.5%, meeting the need of industrial process.
Application of Optimized BP Neural Network in Addressing for Garbage Power Plant
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects.One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.
Directory of Open Access Journals (Sweden)
Fuqiang Zhou
2014-10-01
Full Text Available This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.
Zhou, Fuqiang; Su, Zhen; Chai, Xinghua; Chen, Lipeng
2014-01-01
This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory. PMID:25347581
Institute of Scientific and Technical Information of China (English)
LIU Man-lan; ZHU Chun-bo; WANG Tie-cheng
2005-01-01
In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.
HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds
Institute of Scientific and Technical Information of China (English)
CHI Yong; WEN Jun-ming; ZHANG Dong-ping; YAN Jian-hua; NI Ming-jiang; CEN Ke-fa
2005-01-01
The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a ф150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The Hclemission 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 x 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 cofired fluidized bed incinerator.
Wang, Shu-tao; Chen, Dong-ying; Wang, Xing-long; Wei, Meng; Wang, Zhi-fang
2015-12-01
In this paper, fluorescence spectra properties of potassium sorbate in aqueous solution and orange juice are studied, and the result.shows that in two solution there are many difference in fluorescence spectra of potassium sorbate, but the fluorescence characteristic peak exists in λ(ex)/λ(em) = 375/490 nm. It can be seen from the two dimensional fluorescence spectra that the relationship between the fluorescence intensity and the concentration of potassium sorbate is very complex, so there is no linear relationship between them. To determine the concentration of potassium sorbate in orange juice, a new method combining Particle Swarm Optimization (PSO) algorithm with Back Propagation (BP) neural network is proposed. The relative error of two predicted concentrations is 1.83% and 1.53% respectively, which indicate that the method is feasible. The PSO-BP neural network can accurately measure the concentration of potassium sorbate in orange juice in the range of 0.1-2.0 g · L⁻¹. PMID:26964248
Directory of Open Access Journals (Sweden)
Jie-sheng Wang
2014-01-01
Full Text Available For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210
Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks
Institute of Scientific and Technical Information of China (English)
HU Mei; WANG Hong; HU Geng; YANG Shiyuan
2007-01-01
Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.
Tracking Control of Mobile Robot Based on BP Neural Network%基于 BP 神经网络的移动机器人循迹控制
Institute of Scientific and Technical Information of China (English)
雷双江; 肖世德; 熊鹰; 查峰
2013-01-01
研制自动控制移动机器人循迹控制系统，通过感测外界黑色指导线的变化来控制电机的实时变化。考虑了运动过程中会遇到的各种情况，通过训练BP神经网络使微控制器能够根据不同的环境做出快速、正确的反映。采用微控制技术对电机进行控制，使自动和无线遥控兼容。实验结果表明：移动机器人能根据室内黑色指导线的变化情况快速做出反映，有效抑制了移动机器人在运动过程中的出轨和静止现象，证明了提出的基于B P神经网络的循迹控制系统可靠性较高。%An intelligent tracking control system based on micro-control unit (MCU)was developed to real-time control the mo-tors by sensing the change of the black guide lines. After training the BP neural network,the MCU was able to make quick and accu-rate decisions for various situations encountered during the robot moving. Using MCU technology to control the motors,the system was compatible for both manual and automatic control. The experiment results show that the mobile robot can follow the change of black guide lines accurately and quickly,and stillness and out-of-orbit phenomena are effectively inhibited during moving. The proposed tracking control system based on BP neural network has been verified to be high reliability.
Chen, Ying; Liu, Teng; Wang, Wenyue; Zhu, Qiguang; Bi, Weihong
2015-04-01
According to the band gap and photon localization characteristics, the single-arm notching and the double-arm notching Mach-Zehnder interferometer (MZI) structures based on 2D triangular lattice air hole-typed photonic crystal waveguide are proposed. The back-propagation (BP) neural network is introduced to optimize the structural parameters of the photonic crystal MZI structure, which results in the normalized transmission peak increasing from 85.3% to 97.1%. The sensitivity performances of the two structures are compared and analyzed using the Salmonella solution samples with different concentrations in the numerical simulation. The results show that the sensitivity of the double-arm notching structure is 4583 nm/RIU, which is about 6.4 times of the single-arm notching structure, which can provide some references for the optimization of the photonic devices and the design of high-sensitivity biosensors.
Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
龚声蓉; 王朝晖
2004-01-01
In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.
Application of BP neural network in DNBR prediction%BP神经网络在DNBR计算中的应用
Institute of Scientific and Technical Information of China (English)
黄禹; 刘俊强; 刘乐
2015-01-01
在压水堆事故分析中，通常采用系统分析程序、热流密度计算程序和子通道分析程序分步计算堆芯偏离泡核沸腾比(Departure from Nucleate Boiling Ratio, DNBR)。利用该方法计算的堆芯DNBR结果准确性较好，但是计算过程繁琐、费时。对于系统分析程序自带的堆芯DNBR简化计算模型，由于其根据堆芯限制线偏微分近似得到，适用范围较窄，准确性也难以保证。利用神经网络中的误差反向传播(Back Propagation, BP)算法，基于堆芯核功率、入口温度、流量和压力4个变量对应的一系列DNBR值，选取部分数据进行训练并建立模型，以达到快速和准确地预测堆芯DNBR的目的。根据误差分析，建立的计算模型具有较好的准确性，而且在部分失流事故和汽机停机事故下可较好地预测堆芯DNBR。%Background: In safety analysis of pressurized water reactor (PWR), departure from nucleate boiling ratio (DNBR) is usually calculated by three codes: a system transient analysis code, a heat flux calculation code and a subchannel analysis code, or by simplified model through a partial derivative approximation of the core DNB limit lines, but either procedure has problems of cumbersome or low accuracy.Purpose: The aim of this study is to gain a simple DNBR calculation method with high accuracy.Methods: A 3-layers back propagation (BP) neural network was proposed with a training data set to quickly predict DNBR using four variables of reactor coolant system (nuclear power, core inlet temperature, mass flow rate and pressure).Results: The error of the developed BP network is very small, and has similar results compared with the subchannel code calculations in two typical events.Conclusion: The trained BP network is accurate enough to be used in predicting DNBR, even in transient conditions.
Evaluation of Regional Pedotransfer Functions Based on the BP Neural Networks
Qu, Zhongyi; Guanhua, Guanhua; Yang, Jingyu
The unsaturated soil hydraulic properties, including soil water retention curve and hydraulic conductivity, are the crucial input parameters for simulating soil water and solute transport through the unsaturated zone at regional scales, and are expensive to measure. These properties are frequently predicted with pedotransfer functions (PTFs) using the routinely measured soil properties. 110 soil samples at 22 soil profiles from Jiefangzha Irrigation Scheme in the Hetao Irrigation District of Inner Mongolia, China were collected for the analysis of soil properties i.e. soil bulk density, soil texture, particle size distribution, organic content, and soil water retention curve (SWRC). The Brooks-Corey (BC) model and van Genuchten (VG) model were used to fit the measured SWRC data for each soil sample by using the RETC software. Pedo-transfer functions (PTFS), which describes relationship between the basic soil properties and the parameters of the BC and VG models, were then established with the artificial neural networks (ANN) model. It is found that the ANN model has better effect on the clay loam, loamy clay, loam soil and silty clay to simulate BC model. However, it has better effect on the loam soil, loamy clay and sandy clay to simulate VG model. So, we can draw the conclusion that the ANN model can conveniently establish PTFS between soil basic feature parameters and SWRC model and has reasonable precision. This will be a good method to estimate soil water characteristic curve model and soil hydraulic parameter in the regional soil water and salt movement simulation and water resources evaluation.
Institute of Scientific and Technical Information of China (English)
杨尔辅; 张振鹏; 刘国球; 崔定军
1999-01-01
A system's architecture for condition monitoring of propulsion system using BP-ART hybrid neural networks was presented.The topology of this hybrid architecture was:the first processing unit consisted of BP(Back-Propagation)neural networks,one BP network per subassembly,the second unit was made up of ART(Adaptive Resonance Theory)neural network which had an autoassociative architecture,only one network for the whole propulsion system.Each output of ART network represented a"health state"of propulsion system.The hybrid architecture was made full uses of advantages of every neural network.An application example of this system was illustrated,which demonstrated that this hybrid neural networks' system could be implemented effectively in developing an advanced real-time system for condition monitoring and fault diagnosis of propulsion system.%应用BP-ART混合神经网络提出了一种供推进系统状态监控实时使用的系统,其拓扑结构为: 第一层处理单元由BP神经网络组成,每个BP网络代表一个相应的推进系统组件;第二层处理单元为一个ART神经网络,网络的每一个输出代表推进系统的一种"健康状态",据此可对其故障进行"诊断".该混合结构充分发挥了两类网络的优点,给出的具体应用实例也显示出在推进系统实时状态监控与故障诊断应用中的有效性.
Institute of Scientific and Technical Information of China (English)
张本国; 李强; 王葛; 张水仙
2012-01-01
LM algorithm was introduced to the training process of a BP neural network and a LM--BP neural network model was established aiming at the defects of slow convergence in the train- ing process of the traditional BP neural network. The LM--BP neural network model was applied to the breakout prediction in the continuous casting processes, and it was tested with the historical data collected from a steel mill. The feasibility and the validity of the model are verified by the results with the accuracy rate of 96.15% and the prediction rate of 100%%针对传统BP神经网络在训练过程中存在收敛速度慢的缺陷，将LM（levenberg marquardt）算法引入到BP神经网络的训练过程，建立了LM—BP神经网络模型，并将其应用于连铸过程中的漏钢预报系统。结合某钢厂连铸现场历史数据对系统进行了测试，测试结果以96．15％的预报率及100％的报出率，验证了基于LM算法的BP神经网络连铸漏钢预报方案的可行性和有效性。
基于BP神经网络的深基坑变形预测%Deep Foundation Pit Considering Excavation Effect Based on BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
李水兵; 李培现
2011-01-01
We set up BP neural network model of deep foundation pit considering the excavation effect.Sigmoid is applied in transmitting between input and output layer.Batching descent iselected gradient to train reverse direction network,and BP network is optimized by additional momentum and adaptive tuning for the learning step sizes of the BP learning algorithm.We set up BP neural network model based on Matlab.The prediction results prove that the optimized BP network has a better precision,and improve the learning rate and increases the reliability of prediction.%建立深基坑变形监测数据处理的BP神经网络模型,采用双曲正切S形函数进行输入和输出层传递,选择批处理梯度下降法训练前向网络,并采用附加动量法和学习速率自适应调整进行改进,运用Matlab建立BP神经网络模型。预测结果表明,改进的BP神经网络模型预测精度更高,提高了学习速度并增加了算法的可靠性。
Airport noise prediction model based on BP neural network%一种 BP 神经网络机场噪声预测模型
Institute of Scientific and Technical Information of China (English)
杜继涛; 张育平; 徐涛
2013-01-01
机场噪声预测对机场噪声控制、航班计划制定和机场规划设计具有十分重要的作用.现有的机场噪声预测模型都是以飞机的噪声距离曲线(NPD 曲线)为核心，用相应的数学模型将其修正至与具体机场的特定环境条件相关的噪声传播模型，存在预测成本高和误差大的缺点.针对这种情况，提出一种使用 BP 神经网络利用机场噪声历史监测数据进行NPD 曲线修正计算方法，从而建立适用于特定机场环境条件的机场噪声预测模型.实验表明，在特定机场的特定环境条件下，允许误差为0.5 dB 时，该模型预测准确率高达91.5%以上，具有预测成本小、准确度高的特点.%Airport noise prediction plays an important role in airport noise controlling, flight planning and airport designing. The airport noise prediction models are usually built based on aircraft noise distance curve(NPD), and the NPD curves are little by little revised to the noise propagation model under the specific airport environmental conditions by using a variety of mathematical models. In this way, there are shortcomings of the high cost and great prediction error. This paper presents an airport noise pre-diction model for particular airport environmental conditions. The proposed model applies BP neural network and history data of the airport noise monitoring to modifying the NPD curves. Experiment results show that in particular specific airport environ-mental conditions, the accuracy rate of noise prediction is more than 91.5% in the case of ±0.5 dB error. The proposed model has the features of lower cost and high accuracy.
Institute of Scientific and Technical Information of China (English)
孙晨; 李阳; 李晓戈; 于娇艳
2016-01-01
针对当前智能算法对股票市场预测精度不高的问题，提出使用布谷鸟算法优化神经网络（CS-BP）的方法，对股票市场进行预测。并与粒子群算法优化神经网络模型（PSO-BP）和遗传算法优化神经网络模型（GA-BP）的测试结果进行比较。通过对SZ300091（金通灵）日线的收盘价数据回测分析看出，布谷鸟算法优化神经网络模型明显优于这两种算法，能有效对股票市场进行预测，对于30天的预测精度约为98．633％。%This paper puts forward the method of predicting the stock market by using the cuckoo search algorithm to optimise BP-neural network(CS-BP)aimed at the problem of current intelligent algorithms in poor prediction accuracy on the market.Besides,it compares its test result with the results of PSO-BP model (optimising BP-neural network with particle swarm optimisation)and GA-BP model (optimising BP-neural network with genetic algorithm).After analysing the data backtesting result of the closing price of daily candlesticks of SZ300091 (JTL),we can conclude that the CS-BP model is obviously superior to these two algorithms,it can effectively predict the stock market with about 98.633% of accuracy for thirty days prediction.
Prediction of improved BP neural network by Adaboost algorithm%Adaboost算法改进BP神经网络预测研究
Institute of Scientific and Technical Information of China (English)
李翔; 朱全银
2013-01-01
The traditional BP (Back Propagation) neural network is easy to fall into local minimum and has lower accuracy.According to this problem,a method that combines the Adaboost algorithm and BP neural network is proposed to improve the prediction accuracy and generalization ability of the neural network.Firstly,the method preprocesses the historical data and initializes the distribution weights of test data.Secondly,it selects different hidden layer nodes,node transfer functions,training functions,and network learning functions to construct weak predictors of BP neural network and trains the sample data repeatedly.Finally,it made more weak predictors of BP neural network to form a new strong predictor by Adaboost algorithm.The database of UCI (University of California Irvine) is used in experiments.The results show that this method can reduce nearly 50％ for the mean error absolute value compared to the traditional BP network,and improve the prediction accuracy of network.So this method provides references for the neural network prediction.%针对传统BP神经网络容易陷入局部极小、预测精度低的问题,提出使用Adaboost算法和BP神经网络相结合的方法,提高网络预测精度和泛化能力.该方法首先对样本数据进行预处理并初始化测试数据分布权值；然后通过选取不同的隐含层节点数、节点传递函数、训练函数、网络学习函数构造出不同类型的BP弱预测器并对样本数据进行反复训练；最后使用Adaboost算法将得到的多个BP神经网络弱预测器组成新的强预测器.对UCI数据库中数据集进行仿真实验,结果表明本方法比传统BP网络预测平均误差绝对值减少近50％,提高了网络预测精度,为神经网络预测提供借鉴.
Li, Zhongwei; Sun, Beibei; Xin, Yuezhen; Wang, Xun; Zhu, Hu
2016-01-01
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL. PMID:27595102
Institute of Scientific and Technical Information of China (English)
何勇; 李妍琰
2014-01-01
该文提出改进的PSO‐BP算法在洪水预测应用中建立预测模型。以BP神经网络为基础，提取观测站往年平均径流量作为洪水属性。采用改进的PSO‐BP算法对神经网络的各个参数进行优化，最后建立模型应用于流域观测站的洪水预报模型，叙述了PSO粒子群算法和BP神经网络算法，详细阐述粒子群算法优化BP神经网络的权值和阈值，得出最优的BP神经网络预测适应度值。通过实验仿真对比，结果表明此方法预测结果比BP神经网络算法和混沌径向基神经网络模型算法精度更高，提高了预测的效率。%The flood prediction model base on PSO‐BP algorithm has been proposed in this paper .Extrac‐tion of observation station in average runoff as flood has been conducted on the bases of BP neural net‐work .Using the improved PSO‐BP algorithm parameters of the neural network has been optimized ,the flood forecasting model for watershed observing station with the model .This paper introduces the particle swarm optimization algorithm and BP neural network algorithm ,a detailed explanation of PSO algorithm to optimize BP neural network weights and threshold .Through the simulation results ,this method fore‐casting result is higher than the BP neural network algorithm and chaos RBF neural network model accura‐cy ,is an effective method of prediction and reliable flood .
Directory of Open Access Journals (Sweden)
Hankun Ye
2014-05-01
Full Text Available Evaluating supply chain performance of fresh agricultural products is one of the key techniques and a research hotspot in supply chain management and in fields related. The paper designs a new evaluation indicator system and presents a new model for evaluating supply chain performance of fresh agriculture product companies. First, based on analyzing the specific characteristics of the supply chain performance evaluation of fresh agriculture products, the paper designs a new evaluation indicator system including external and internal performance. Second, some improvements, such as adjusting dynamic strategy and the value of momentum factor, are taken to speed up calculation convergence and simplify the structure and to improve evaluating accuracy of the original BP evaluation model. Finally the model is realized with the data from certain supply chains of three fresh agriculture product companies and the experimental results show that the algorithm can improve calculation efficiency and evaluation accuracy when used for supply chain performance evaluation of fresh agriculture product companies practically.
基于 BP 神经网络的调剖效果预测模型分析%Prediction model of conformance control effect based on BP neural network
Institute of Scientific and Technical Information of China (English)
刘宁; 刘士梦; 李明
2014-01-01
产油量预测是调剖方案实施以后效果预测或评价的关键，基于BP神经网络理论，通过分析影响调剖效果的因素，利用Matlab神经网络工具箱函数，建立了调剖神经网络预测模型，经过模型预测效果分析及实际运用，认为利用BP神经网络预测产油量与实际值较为吻合，误差相对较小，可靠性高，可运用此模型预测调剖产油量。%The oil production prediction is a key to prediction or evaluation of conformance control effect after the scheme implementation .Based on the theory of BP neural network ,by analyzing the influencing factors of conformance con-trol effect ,the neural network prediction model for conformance control was established by employing the toolbox functions in the Matlab neural network .Through the analysis of the model forecast effect and practical application ,it was considered that the oil production predicted by the BP neural network was consistent with the actual one ,which had smaller relative error and high reliable .The model can be used to predict the conformance control production .
Evaluation of Value Chain Risks Based on BP Artificial Neural Network%基于BP人工神经网络的价值链风险的评价
Institute of Scientific and Technical Information of China (English)
郭秋霞; 邓样明; 欧阳江
2011-01-01
通过对价值链管理深人研究,利用BP人工神经网络对价值链风险管理进行评价,采用专家评分法,获得实际的数据,对模型进行仿真和测试,证实价值链风险管理的指标体系的实用性和BP人工神经网络模型的价值.%Through in-depth analysis of value chain management, the paper employs BP artificial neural network to evaluate the risk management of value chains. Expert scoring is used on practical data collected and simulation and testing are conducted to verify the practicality of the index system of value chain risk management and the value of BP artificial neural network.
Application of LM-BP neural network in predicting dam deformation.%LM-BP神经网络在大坝变形预测中的应用
Institute of Scientific and Technical Information of China (English)
缪新颖; 褚金奎; 杜小文
2011-01-01
为了对大坝进行切实有效的监控,需要建立一个良好的大坝预测模型.针对传统BP (Back-Propagation)神经网络存在的收敛速度慢和泛化能力弱等缺陷,利用LM-BP(Levenberg Marquardt Back Propagation)算法对大坝变形进行预测,并根据丹江口大坝1996和1997两年的变形观测数据,对大坝挠度预测结果进行分析.结果表明,所建立的LM-BP神经网络的预测精度和收数速度明显提高.%It is significant to establish an effective and practical dam safety monitoring model. The shortcomings of the traditional BP neural network lie in the slowness in the convergence rate and the weakness in the generalization ability. Based on the above, LM-BP neural network is adopted for predicting the dam deformation. With the measured data of Danjiangkou dam deformations in the year of 1996 and 1997 as examples,the deflection of dam is predicted using LM-BP. The results show that the proposed method can obviously enhance the forecasting precision and convergence rate.
Jie-sheng Wang; Shuang Han; Na-na Shen; Shu-xia Li
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracte...
Li, Chunhui; Yu, Chuanhua
2013-08-15
To provide a reference for evaluating public non-profit hospitals in the new environment of medical reform, we established a performance evaluation system for public non-profit hospitals. The new "input-output" performance model for public non-profit hospitals is based on four primary indexes (input, process, output and effect) that include 11 sub-indexes and 41 items. The indicator weights were determined using the analytic hierarchy process (AHP) and entropy weight method. The BP neural network was applied to evaluate the performance of 14 level-3 public non-profit hospitals located in Hubei Province. The most stable BP neural network was produced by comparing different numbers of neurons in the hidden layer and using the "Leave-one-out" Cross Validation method. The performance evaluation system we established for public non-profit hospitals could reflect the basic goal of the new medical health system reform in China. Compared with PLSR, the result indicated that the BP neural network could be used effectively for evaluating the performance public non-profit hospitals.
一种低温锆弱凝胶调剖剂的研制%Modeling of rock drillability with BP neural network optimized by SDCQGA
Institute of Scientific and Technical Information of China (English)
李谦定; 李彦闯; 李彦庆
2013-01-01
In the control process of intelligent drilling,there are some difficulties in the rock drillability modeling,such as poor real-time, low accuracy of rock drillability extraction,etc. For this reason,a modeling method for the rock drillability extraction is put for-ward, which is based on the BP neural network optimized by SDCQGA ( Self-Adaptive Double Chain Quantum Genetic Algorithm). A fast self-adaptive double chain quantum genetic algorithm is established according to the variation rate of objective function at search point, and then the structure of BP neural network is optimized using this algorithm in order to overcome the shortcomings of easily being influenced by initial weights and poor generalization ability of BP neural network. The model for rock driilability extraction was established according to statistical analysis and preprocessing and analysis shortage of over-fitting, random and of back-propagation neural network which can affect the generalization ability with the subtle changes of the parameters of the network, this paper presents rock driilability extraction modeling methods using an optimization the BP neural network structure which is based on the adaptive double chain quantum genetic algorithm. Finally,the rock driilability extraction model is constructed by using a large number of measurement while drilling data in different drilling areas. The model can be effectively solved difficult extraction of rock driilability in the complex formation. The tests of extraction rock driilability of different lithology prove that this modeling approach not only improves the accuracy of parameter extraction and generalization ability,but also has a good real-time and suitability in the actual rock driilability extraction.%针对温度低于50 ℃的高含水油藏,研制出了一种聚合物/有机锆弱凝胶调剖剂.该调剖剂以部分水解聚丙烯酰胺(HPAM)为主剂,以自制有机锆YJ-1为交联剂,在35～50℃温度下能形成稳定的
Method of Deep Web entities identification based on BP neural network%基于BP神经网络的Deep Web实体识别方法
Institute of Scientific and Technical Information of China (English)
徐红艳; 党晓婉; 冯勇; 李军平
2013-01-01
针对现有实体识别方法自动化水平不高、适应性差等不足,提出一种基于反向传播(BP)神经网络的Deep Web实体识别方法.该方法将实体分块后利用反向传播神经网络的自主学习特性,将语义块相似度值作为反向传播神经网络的输入,通过训练得到正确的实体识别模型,从而实现对异构数据源的自动化实体识别.实验结果表明,所提方法的应用不仅能够减少实体识别中的人工干预,而且能够提高实体识别的效率和准确率.%To solve the problems such as low level automation and poor adaptability of current entity recognition methods, a Deep Web entity recognition method based on Back Propagation ( BP) neural network was proposed in this paper. The method divided the entities into blocks first, then used the similarity of semantic blocks as the input of BP neural network, lastly obtained a correct entity recognition model by training which was based on the autonomic learning ability of BP neural network. It can achieve entity recognition automation in heterogeneous data sources. The experimental results show that the application of the method can not only reduce manual interventions, but also improve the efficiency and the accuracy rate of entity recognition.
Optimized BP Neural Networks for EMG Finger Movement Recognition%改进BP神经网络的EMG手指运动识别
Institute of Scientific and Technical Information of China (English)
方一新
2014-01-01
In the pattern recognition of Finger movement based on electromyography (EMG), the Stability and Recognition rate are both the problem. The paper proposes a new method of pattern recognition of EMG signal. The method combination of the algorithm using BP neural network AR model and the improvement of modern signal pro-cessing in the theory of the algorithm, can effectively solve the problem of BP network into local extremum recogni-tion. To make the classification of the eigenvalues of the EMG, these eigenvalues have been inputted to the MAT-LAB to build up a improved multilayer BP neural networks. For the recognition of three different kinds of finger mo-tion's EMG signals, the experiments show that the improved BP algorithm, to obtain higher recognition accuracy than the traditional BP algorithm, to around 94%.%在基于肌电信号（EMG）手指运动的模式识别中，稳定性和识别率是两个主要问题，为此提出了一种新的EMG模式识别算法。该算法采用现代信号处理理论中的AR模型和改进的BP神经网络相结合的算法，有效的解决了BP网络识别中落入局部极值问题。进行试验，将提取到的特征值输入MATLAB建立一个改进多层BP神经网络，识别三个不同类型的手指运动。实验表明，改进BP算法较传统BP算法获得了更高的识别精度，达到94%左右。
Handwritten digit recognition based on AP and BP neural network algorithm%基于 AP 和 BP 神经网络算法的手写数字识别
Institute of Scientific and Technical Information of China (English)
朱婷婷; 魏海坤; 张侃健
2014-01-01
Given the problem that current methods of handwritten digit recognition are not ideal for large-scale application,a new method of handwritten digit recognition has been proposed,combining affinity propagation with error back-propagation neural network algorithm.Firstly,pretreatment of samples was carried out.Then the AP algorithm was used to cluster samples to e-liminate redundant and re-construct the sample space.Finally,the BP neural network was utilized to learn and recognize each class from AP clustering.Experiments were conducted with the data from UCI machine learning database,and the correct identi-fication rate of the method reaches 96.10%,which is better than that of BP neural network algorithm (94.88%),and the pro-cessing time of the method is only one over ten of BP neural network algorithm.Thus,the proposed method can be used to effi-ciently and effectively identify handwritten digits with high practical value.%针对现有的手写数字识别技术不适合大规模应用的问题，提出了一种基于 AP 和 BP 神经网络的快速手写数字识别算法。首先对预处理后的样本通过 AP 算法（affinity propagation）聚类消除冗余，重新构造样本空间；然后构造 BP（误差反向传播）神经网络模型，学习测试集合样本。采用 UCI 机器学习数据库中的数据进行实验，结果表明，算法的识别正确率可达96．10％，高于 BP 神经网络算法的识别正确率94．88％，且执行时间约为后者的10％，具有较高的实用价值。
The Text-Learning Algorithm Based on Kohonen and BP Neural Network%基于Kohonen和BP神经网络的文本学习算法
Institute of Scientific and Technical Information of China (English)
傅忠谦; 王新跃; 周佩玲; 彭虎; 陶小丽
2001-01-01
This paper introduces a text-learning algorithm based on Kohonen and BP neural network on Internet.It adopts vector space model to encode the text,and uses the Self-Organizing feature of Kohonen neural network and the nonlinear feature of BP neural network.After trained,the algorithm can present the rating which measure the closeness to the user＇s interests.So it can be used to perform the tasks,such as information filtering and intelligent browsing on Internet,etc.%介绍了基于Kohonen和BP神经网络结合的Internet网上文本学习算法。它采用向量空间模型对文本进行编码,利用Kohonen网络的自组织特性和BP网络的非线性特性进行学习。经过训练,算法能够有效地对输入文本进行判断, 给出一个评价等级, 标识出文本和用户兴趣的相关程度, 从而为基于Internet的信息过滤、智能浏览等处理提供基础。
Institute of Scientific and Technical Information of China (English)
姚仲敏; 潘飞; 沈玉会; 吴金秋; 于晓红
2015-01-01
当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题.基于本地5 kW小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用 BP 以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型.实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度.其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强.%In the current PV output short-term forecast, BP or optimization BP neural network algorithm is used commonly, which has problems of single optimization algorithm, the lack of a variety of optimization algorithms for comparison and selection, and big forecast error. Therefore, based on local 5 kW small-scale distributed PV power station, considering the related factors that influence PV output such as solar radiation intensity, environmental temperature, wind speed and historical generation data of photovoltaic power station, this paper uses BP, GA-BP and POS-BP neural network algorithm respectively to construct short-term prediction model of PV output in sunny, cloudy and rainy weather conditions. Test results show that three kinds of neural network prediction models all reach certain prediction accuracy under three different weather conditions, among which GA-BP and POS-BP prediction models reduce the prediction errors compared to the traditional BP model, and POS algorithm has a better optimization effect on BP neural network prediction model and a stronger applicability compared to GA algorithm, and
基于BP神经网络的CCI预测模型%Prediction Model of CCI Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭庆春; 寇立群; 孔令军; 张小永; 崔文娟; 史永博
2011-01-01
将BP神经网络模型应用到消费者信心指数预测中,并建立了消费者信心指数的神经网络预测模型.将计算结果与真实值进行了验证,结果表明:CCI的计算值与真实值之间的相对误差为2.6%,且具有较好的相关性.%BP neural network model has been applied to forecast the consumer confidence index. a neural network prediction model of consumer confidence index is set up, and the calculating results are verified with real value. The research results show that the relative errors between CCI and the real value is 2.6％, and the correlation between them is very well.
Circuit design of a LSI neural network using BP-GA algorithm%采用BP-GA算法的一种LSI神经网络的电路设计
Institute of Scientific and Technical Information of China (English)
卢纯; 石秉学
2001-01-01
A new algorithm is proposed to combine the Back-Propagation algorithm (BP) and the Genetic Algorithm (GA). The combined algorithm is used to design a Large Scale Integrated circuit (LSI) for a two-layer feedforward Artificial Neural Network (ANN). A novel neuron is proposed as the key element of the neural network. The neuron's activation function fit the sigmoid well and the bias weight and the gain factor of the neuron can be modulated. Further more, the saturation levels of the sigmoid remain constant for different gain values. HSPICE simulations were done using the neural network using transistor models for a standard 1.2μm CMOS process. Results using the exclusive or (XOR) benchmark demonstra te its effectiveness.%将误差反传（BP）算法和遗传算法（GA）有机地结合在一起，提出了一种新的算法BP-GA。采用BP-GA算法，设计了一个两层前向LSI神经网络。作为神经网络的关键部件，提出的新型神经元性能优越。它的激活函数与理想sigmoid函数拟合很好； 可实现对阈值及增益因子的编程并且不同增益因子下饱和输出电压值相同。采用标准1.2μm CMOS工艺的模型参数，对该两层前向神经网络电路进行的HSPICE模拟证明了它有解决异或(XOR)问 题的能力。
The supermarket goods inventory control model based on BP neural network%基于BP神经网络的商品库存控制模型
Institute of Scientific and Technical Information of China (English)
孔繁烨; 耿也
2013-01-01
In order to overcome the drawbacks of the high traditional inventory costs and low consumer satisfaction, this paper analyzed the training process of BP neural network models of inventory control based on the sales record of a supermarket during a period of time as sample data, and verified that the BP neural network adaptive ability, fault-tolerant ability and the ability of dealing with the nonlinear relationship, ensured the accuracy of inventory forecast, finally proposed the inventory control model based on BP neural network algorithm. The results show that the control model can accurately control the supermarket merchandise inventory, provide decision support for reasonable control of inventory, and improve the efficiency of inventory control.% 为克服传统商品库存成本过大和消费者满意度过低的弊端，采用BP神经网络方法，以超市一段时间内的销售记录为样本数据，分析BP神经网络库存控制模型的训练过程，并验证BP神经网络的自适应能力、容错能力以及处理非线性关系的能力，保证库存预测的准确性，最终提出基于BP神经网络算法的商品库存控制模型。研究结果表明：该控制模型能够准确高效控制超市商品库存，可以为合理控制库存提供决策支持，有效提高库存控制的效率。
BP Neural Network Based on Artificial Bee Colony Algorithm%基于人工蜂群的BP神经网络算法
Institute of Scientific and Technical Information of China (English)
李卫华; 徐涛; 李小梨
2012-01-01
The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Artificial Bee Colony Algorithm, which based on foraging behavior of honeybee swarms, is a new heuristic bionic algorithm and a typical kind of swarm intelligence algorithm. It is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony Algorithm was proposed to optimize the weight and threshold value of BP neural network. The result shows that the new algorithm improves the precision and expedites the convergence rate.%传统BP神经网络存在容易陷入局部极小点、收敛速度慢等缺点.人工蜂群算法是基于蜜蜂群体的觅食行为而提出的一种新的启发式仿生算法,属于典型的群体智能算法.它为全局优化算法,该算法简单、实现方便、鲁棒性强.针对BP神经网络算法的不足,提出利用人工蜂群算法交叉优化BP网络参数的权值和阈值,实验证明该优化算法确实提高了解的精度,加快了网络收敛速度.
Realization of Chinese text classification by using BP neural network%用BP神经网络实现中文文本分类
Institute of Scientific and Technical Information of China (English)
火善栋
2015-01-01
文本分类是文本挖掘的一个重要内容，在很多领域都有广泛的应用。为了实现中文文本分类问题，先采用分词技术和TF-IDF算法得到每一篇中文文档的特征向量，然后采用PB神经网络构造一个中文文本分类器。实验证明，采用BP神经网络进行中文文本分类时，虽然存在学习周期长，收敛速度慢等问题，但其分类速度和分类的正确率还是很高的。因此，采用BP神经网络进行中文分类是一个比较好的方法。%Text classification is an important part of text mining, and it has been widely used in many fields. In order to realize the Chinese text classification, the feature vector of each document is obtained by using the word segmentation technique and TF-IDF algorithm, and then a Chinese text classifier is constructed by BP neural network. Experiment results show that using BP neural network to Chinese text categorization, although there are problems such as a long learning period, slow convergence and so on, the classification speed and classification accuracy rate is quite high. Therefore, using BP neural network to classify Chinese is a good way.
Sales forecasting model based on improved BP neural network%改进的BP神经网络及其在销量预测中的应用
Institute of Scientific and Technical Information of China (English)
毕建涛; 魏红芹
2011-01-01
In the light of different factors affecting sales of products and the interaction between those factors,the theory of artificial neural network was introduced into the domain of sales forecasting.At the same time,BP neural netowrk was improved both from the aspects of sample data quality and initial parameters to overcome its limitation by combining principal components analysis（PCA）,BP neural network and particle swarm optimization algorithm（PSO）.Finally,an example analysis was made in order to verify the validation of this model.The results showed that the suggested model simplified the architecture of BP network and improved forecast accuracy.Thereby,the effectiveness of this model was validated.%针对影响产品销量的因素众多,并且影响因素之间相互作用等特点,将人工神经网络理论引入产品销量预测领域.同时,为了克服BP网络的局限性,提出将主成分分析方法、BP神经网络以及粒子群优化算法相结合,分别从样本质量和初始权值两个方面对BP神经网络进行改进.最后,对某品牌服装产品的月销售量进行了实例研究.结果表明,所提出的模型简化了BP网络结构的同时,提高了网络的预测精度,从而验证了模型的有效性.
基于LADT-BP算法的心电图快速分析%A NEW ALGORITHM FOR ECG ANALYSIS BASED ON LADT-BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
李刚; 叶天宇; 何峰
2001-01-01
本文提出了一种应用LADT(Linear Approximation Distance Thresholding)压缩算法进行预处理的BP(Backpropagation)网络算法(我们称为LADT-BP算法)。实验证明该算法与现有的算法相比，在运算速度及正确识别率等方面，均有大幅度的提高。%A new algorithm for ECG analysis was proposed, with combination of LADT compression technique and BP neural network method. The Basic principles of the algorithm and its applications were also discussed. The experiment result showed that the new algorithm was faster in convergence and more accurate in recognition than that of the others.
Research and Application of Improving BP Neural Network%改进BP神经网络的研究及应用
Institute of Scientific and Technical Information of China (English)
周凌翱; 车金庆
2012-01-01
The artificial neural network has a strong nonlinear mapping ability, has been applied to various fields such as pattern recognition, intelligent control, image processing and time series etc., in this paper, the heuristic improvement of BP algorithm was proposed aimed at the deficiencies of BP algorithms, and a common type of improvement was introduced aimed at the main drawback of the genetic algorithm through analysis and research on genetic neural network model and its algorithm.%人工神经网络具有强大的非线性映射能力,已经被应用于模式识别、智能控制、图像处理以及时间序列分析等各种领域.本文针对BP算法的不足,提出了BP算法的启发式改进,通过对遗传神经网络模型及其算法进行分析和研究,针对遗传算法的主要缺陷介绍了一种常用的改进类型.
基于BP神经网络的数字识别研究%Study of Digital Recognition Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
苏睿; 张晓杰
2013-01-01
比较了各种数字识别方法,采用BP神经网络设计了一个数字识别系统.首先对数字图像进行二值化处理,构造输入向量矩阵;接着通过选取初始权值、隐层节点数和权值学习算法,创建BP神经网络,对样本数据进行训练;之后对加有噪声的样本再次进行训练,以提高网络的鲁棒性;最后制作了图形用户界面进行实验.测试结果表明,该系统对噪声系数小于0.85的字符识别率可达96%,且网络训练时间可以接受.%@@@@This paper compares several methods to recognize various digits and designs a digital recognition system based on the Back Propagation neural network.Firstly,the digital image is processed in binary to construct the input vector matrix.Secondly,by choosing the initial weight,the number of hidden nodes and the learning algorithm of weight,a perfect BP neural network is created to train the sample data.Then the sample with noise is trained once more so as to enhance the network robustness.Finally.graphical user interface is made to test this system.The result shows that the digital recognition rate of this BP network is possible to reach 96%with the noise factor less than 0.85,and the training time is acceptable.
BP神经网络在解决电力消耗问题中的应用%Application of BP Neural Network in Reducing Power Consumption
Institute of Scientific and Technical Information of China (English)
傅军栋; 喻勇; 黎丹
2015-01-01
The BP neural network has great advantages in solving nonlinear complex system. This paper, using the population, economy and power consumption data during 1991-2011 in Jiangxi province as the research object, builds up electricity consumption forecasting models based on BP neural network. Model 1 adopts the annual popu⁃lation, economy and power consumption data during 1991-2009 as training samples, with those of 2010-2011 as test samples to verify the accuracy of the network. Then according to the historical data, it forecasts the power con⁃sumption based on factors of population and economy. Model 2 determines the multiple linear regression for non⁃linear multivariable functions through the regression analysis method, predicting electricity consumption through the parameters of the regression model. Results show Model 1 has good convergence with small prediction absolute error while Model 2 with the traditional method has larger errors. It proves that BP neural network is feasible in forecasting electric power consumption.%由于BP神经网络在解决非线性复杂系统中存在很大的优势，以江西省1991—2011年人口、经济和耗电量等数据为研究对象，利用BP神经网络构建耗电量预测模型。模型一利用1991—2009年人口、经济和耗电量等数据作为训练样本，以2010—2011年作为测试样本来验证网络的准确性，再根据历史人口、经济等数据来预测历史耗电量；模型二采用传统的多元回归分析法，对非线性多元函数进行多元线性回归，通过回归模型得到的参数来预测耗电量。结果表明，模型一收敛性较好，所得预测结果绝对误差较小，而模型二传统方法得到的预测结果误差较大，因此，利用BP神经网络预测的结果具有非常大的参考价值，证明BP神经网络应用在电力消耗中的应用是可行的。
Institute of Scientific and Technical Information of China (English)
关学忠; 白云龙; 高哲
2014-01-01
Brushless DC motor speed servo system is multivariable ,nonlinear and strong coupling .Its performance is easily influenced by the parameter variation ,the cogging torque and the load disturbance .To solve the deficiency ,the paper presents the algorithm of active‐disturbance rejection control(ADRC) based on back‐propagation(BP) neural network .The ADRC is independent of accurate system and its extended‐state observer can estimate the disturbance of the system accurate‐ly .However ,the parameters of Nonlinear Feedback(NF) in ADRC are difficult to obtain .In this paper ,these parameters are self‐turned by the BP neural network .The simulation results indicate that the ADRC based on BP neural network can im‐prove the performances of the servo system in rapidity ,control accuracy ,adaptability and robustness .%无刷直流电机调速系统是多变量，非线性强耦合的非线性系统。它的齿槽转矩和负荷扰动性能很容易被参数的变化所影响。为了解决这个不足，论文将BP神经网络算法应用到自抗扰控制系统。自抗扰控制器是独立的精确的控制器，其扩张状态观测器可以准确地估计该系统的扰动。然而自抗扰控制器的非线性反馈参数是很难获得的，在文章中这些参数是来自BP神经网络。仿真结果表明，基于BP神经网络的自抗扰控制器能改善该伺服系统的快速性、控制精度适应性和鲁棒性。
Equipment Failure Ratio Prediction Based on BP Neural Network%基于BP神经网络的装备失效率预测研究
Institute of Scientific and Technical Information of China (English)
桑亮
2014-01-01
As the use time of equipment prolongs,failure ratio ceaselessly elevates. Therefore,predicting equipment failure ratio accurately is important to evaluate equipment performance to carry on condition-based on maintenance. Considering nonlinear maping capability of BP neural network,the equipment fail-ure ratio is predicted by this model. The best neural cell number of input layer and middle layer in BP model is analyzed. The variance of factual and predicting value is 0. 0387,and demand is accomplished.%装备伴随使用时间的增长，失效率会不断升高；因此准确预测装备失效率，对于及时准确评估装备性能，开展视情维修具有重要的指导意义；鉴于BP神经网络的高度非线性映射能力，利用此模型对装备失效率进行预测；分析得到了BP模型的输入层和中间层的最优神经元数；此时实际值与预测值的方差为0.0387，达到要求。
Research of BP neural network optimizing method based on Ant Colony Algorithm%一种基于蚁群算法的BP神经网络优化方法研究
Institute of Scientific and Technical Information of China (English)
王沥; 邝育军
2012-01-01
BP 神经网络是人工神经网络中应用最广泛的一种多层前馈神经网络。针对它容易陷入局部极小值及隐层节点大多利用经验试凑来确定的缺点，本文提出了一种基于蚁群算法的BP神经网络结构及参数优化方法，利用蚁群算法的全局寻优能力克服BP神经网络存在的不足。最后，将该方法用于短时交通流预测，实验结果表明：利用蚁群算法优化神经网络是有效的，预测结果也有较高精度。%BP neural network is the most widely used multilayer feedforward artificial neural networks, however,it is vulnerable to be trapped in local minimum and there is no systematic method to determine the number of hidden layer nodes thus usually done empirically. This paper introduces a method to optimize the structure and parameters of BP neural network which integrates ant colony algorithm with BP neural network to overcome shortcomings of traditional BP neural networks. The proposed method has been applied in short-term traffic flow forecasting. Simulation results demonstrate that the new BP neural network based on ant colony algorithm is more effective and can provide higher precision in traffic flow forecasting.
基于BP神经网络的GFSINS角速度预测%Prediction of the angular velocity of GFSINS by BP neural network
Institute of Scientific and Technical Information of China (English)
韩庆楠; 郝燕玲; 刘志平; 王瑞
2011-01-01
针对无陀螺捷联惯导系统(GFSINS)中传统角速度算法解算精度不高的问题,提出一种可避免复杂代数运算的反向传播(BP)神经网络算法来求解角速度.基于一种十加速度计构型方案,选择10个加速度计输出、采样周期和臂杆距离等12个已知量作为网络输入,以对数法得到的角速度值作为期望输出,针对5 000个样本在不同的隐含层层数、单层神经元个数以及学习步数等情况下进行网络训练,构建了一个含有30个隐含层神经元的3层BP网络模型.采用此模型对角速度进行实时预测,结果表明:网络具有很好的适应能力和实时性,角速度实时预测时间与对数法相当,且其预测精度比对数法提高大约3倍.%Aimed at low precision for traditional angular velocity algorithms in gyro-free strapdown inertial navigation system (GFSINS), a BP (back-propagation)neural network algorithm without complex mathematic computation was put forward to calculate angular velocity. Based on a ten-accelerometer configuration scheme, the accelerometer output, sample interval and fixed position were chosen as input, angular velocity got by lognormal algorithm was chosen as output, and 5 000 samples were trained in several conditions with different hiding layers, neural cells and training steps. Then a threelayer BP network model with 30 hiding layer neural cells was built. Finally, the angular velocity was predicted in real time by the model. Results demonstrate that network has strong adaptive capability and instantaneity, and compared with lognormal algorithm, prediction time is almost the same, but the prediction precision of angular velocity is nearly improved by 3 times.
Institute of Scientific and Technical Information of China (English)
Xu; Quan-xi
2001-01-01
Based on the basic principles of BP artificial neural network model an d the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serv ing as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute t he runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Co mparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.
Institute of Scientific and Technical Information of China (English)
蒋莉; 黄华东; 王先义; 陈桦深
2016-01-01
The BP neural network model,with charging amount of blasting cut,blasting center distance and blasting velocity as main factors,is established based on Leyenberg-Marquardt (LM)calculation method;and the blasting vibration velocity is predicted and analyzed.The charging amount of blasting cut is calculated by means of critical blasting vibration velocity in related criteria.The calculation results show that LM-BP neural network method is superior to traditional method in terms of prediction of blasting vibration velocity;the blasting cut charging amount calculated by means of critical blasting vibration velocity inverse calculation method is rational and effective.%为了对隧道爆破振动灾害的危险状态进行有效地预测，实验采用基于 Levenberg-Marquardt（LM）算法改进的 BP 算法，建立以实测隧道爆破掏槽眼装药量、爆心距和爆破振速为主要爆破影响因素的神经网络模型，对振速进行预测分析，预测结果与实测数据吻合良好；继而引用 GB 6722—2014《爆破安全规程》所规定的临界安全振速反向预测掏槽装药量，通过反向预测计算得出满足安全振速要求的临界掏槽装药量。预测结果表明：LM-BP 算法相比传统的经验模型在振速预测上表现更好，通过反向的预测运算，能有效预知临界装药参数，对爆破振动安全预测及控制有积极的意义。
Institute of Scientific and Technical Information of China (English)
葛建坤; 李小平; 罗金耀
2016-01-01
通过田间试验,对温室膜下滴灌茄子冠层叶片蒸腾速率的变化规律进行了深入研究。通过分析温室内地面温度、相对湿度、植株冠层温度、气压、水面蒸发、太阳辐射等6个环境参数与茄子蒸腾速率的综合影响关系,确定了网络拓扑结构为6-9-1。并应用 MATLAB 软件,选择 Levenberg-Marquardt (L-M)优化算法,建立了基于 Back Propagation(BP)神经网络的温室膜下滴灌茄子蒸腾速率预测模型。经模型验证得出,BP 神经网络模型预测值与蒸腾速率实测值间拟合效果较好,平均相对误差为0.0298,达到预测精度要求。该研究成果对温室膜下滴灌作物需水规律及需水量研究具有较好的参考价值。%In order to reveal the law of crop transpiration in greenhouse,a field experiment on transpiration rate of greenhouse egg-plant with drip irrigation under mulch was taken in a Venlo type greenhouse in North China University of Water Resources and Elec-tric Power.Through the analysis on the combined influence between eggplant transpiration rate and 6 indoor environmental factors (greenhouse ground temperature,relative humidity,plant canopy temperature,air pressure,evaporation and solar radiation),topol-ogical structure of the model was discussed and determined (6-9-1).And a prediction model of greenhouse eggplant transpiration rate was established based on BP Neural network of L-M optimizing algorithm,by using MATLAB.After the model validation,the re-sults indicated that,the BP neural network prediction model has a high precision,the predicted value fits the measured value well, and average relative error is only 0.0298,which meets the precision requirement.The research result has a certain reference value to the study on crop water requirement in greenhouse with drip irrigation under mulch.
Institute of Scientific and Technical Information of China (English)
叶斌; 刘知贵
2009-01-01
Based on the research of BP neural network and CELTS-22,this paper constitutes the guide line system refer to the main evaluation standard in CELTS-22.Applying the three layers BP neural network structure, it designs the computer assistant evaluation model that can simulate the expert, remedies the facticious misplay in the process of evaluating.%基于对人工神经网络和CELTS-22的研究,建立了以CELTS-22中主要评价规范为参照的指标体系.该系统应用三层BP神经网络结构,设计出能模拟专家进行评价的计算机辅助评价模型,可以弥补评价过程中的人为失误.
Institute of Scientific and Technical Information of China (English)
李媛媛; 常庆瑞; 刘秀英; 严林; 罗丹; 王烁
2016-01-01
spectrometer (SVC HR-1024i), and at the same time, chlorophyll content of maize leaves was obtained by using SPAD-502. There were totally 120 samples collected, two thirds of which were utilized as the training set and remaining one third as the validation set. The model constructed relied on the training set and the validation set was evaluated, respectively. The correlation between first derivative spectra, hyperspectral characteristic parameters and SPAD values were analyzed. Then single variable linear and nonlinear fitting traditional regression models respectively based on first derivative spectra and hyperspectral characteristic parameters were established to estimate the SPAD values. Besides, taken the first derivative values at 763 nm, the maximum first derivative values within blue edge (Db), red edge position (λr) and blue edge area (SDb) as the input parameters, the measured SPAD values as the output parameters, BP neural network model was built. By using the same input parameters, principal component regression (PCR) and partial least squares regression (PLSR) were used to estimate the SPAD values, too. Then we compared the predictive power of traditional regression models, PCR and PLSR models to BP neural network model. Some critical conclusions were made based on the study. First, the maximum correlation coefficient between SPAD values and first derivative spectra located at 763 nm (R=0.901) and the polynomial model was better than the linear model. As to the hyperspectral characteristic parameters, the variable among which the maximum first derivative values within blue edge (Db) was significantly related with SPAD values(R=-0.850) and its linear model was the best model of SPAD estimation models established by the hyperspectral characteristic parameters. The coefficients of determination for the calibration set of the two traditional regression models were 0.868 and 0.711, and the corresponding values of root mean square error (RMSE) were 3.069 and 4.340; for
BP神经网络的样本拓展及旅游人数预测%Sample Expanding of BP Neural Network and Forecasting Number of Tourists
Institute of Scientific and Technical Information of China (English)
于红斌; 梁广颖; 潘逸飞; 杨云飞
2011-01-01
城市旅游人数预测是一个城市建设规划中的重要决策因素,神经网络恰能描述其非线性的特点.为了使算法在对不同城市的预测上具有通用性,只根据往年旅游人数这一单一指标进行预测,有效地避开了其他因素对其的影响.同时,通过对网络的样本拓展,增加了预测数据的有效性.实验结果表明,该预测模型可以解决城市旅游人数的预测问题,验证了模型的可行性和通用性.%Forecasting number of tourists is an important factor in decision - making of urban construction. This forecasting is a nonlinear question, just can be described by BP neural network. In order to algorithm proposed by this paper can apply to different cites' forecasting, BP neural network only choose one single factor, namely the number of tourists about past year. This avoid the influence of other factor effectively. Meanwhile, availability of forecasting data be increased through the sample expanding. The experiment results show that the model is well - suited for forecasting the number of tourists, and the feasibility and effectiveness of its is verified.
基于BP神经网络的数码相机颜色特征化%Color Characterization for Digital Camera Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
蒋飞飞; 徐兰萍; 郑立扬
2012-01-01
Color characterization method for digital cameras based on BP neural network was proposed after comparison of color characterization methods using the color theory and neural network theory.Color space conversion from RGB to XYZ and Lab was realized by using MATLAB.The precision of the two models was compared and the main causes of training and test error were analyzed statistically.The results showed that RGB to Lab color space version is better than RGB to XYZ.%以色度学理论和神经网络为基础,在综合比较数码相机颜色特征化方法之后,采用了基于BP神经网络的颜色特征化方法。利用MATLAB实现了从RGB到XYZ及Lab的颜色空间转换,比较了2种转换模型的精度,统计分析了该模型产生训练误差和测试误差的主要原因,验证了RGB→Lab转换算法是一种较优和可靠的方法。
Directory of Open Access Journals (Sweden)
Schwindling Jerome
2010-04-01
Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
Institute of Scientific and Technical Information of China (English)
王赟松; 刘钦龙; 高卫中
2004-01-01
标准BP神经网络算法收敛速度慢是限制其广泛应用的主要原因.为此,以标准BP算法为基础,应用最小二乘法理论,提出了一种收敛速度快的BP算法--NLMSBP算法.仿真结果表明,和标准BP算法及其它改进形式比较,NLMSBP算法收敛速度大大提高,稳定性并未降低,这为BP神经网络应用于实时性要求高的场合提供了算法基础.该算法缺点是计算量大,所需计算机内存大,不适于大型网络的计算.%That standard backpropagation(BP) algorithm for training neural networks converges slowly is the main reason why it cannot be used widely in practical applications. Therefore, a new kind of BP algorithm, called the NLMSBP algorithm for short, is put forward in this paper by using solutions for a nonlinear least mean square problem. The experimental results have proved that the algorithm converges very fast and has good stability compared with the standard BP algorithm and the other modifications. It is suitable for training the network with a few thousands of weights and offsets and high training precision demand. If the computer memory is enough, the superiority of the algorithm over the others is very notable. Indeed, it is worth popularizing.
Institute of Scientific and Technical Information of China (English)
王东; 徐超; 万强
2015-01-01
Modeling of mechanic joint is a challenge for the complex multi - scale,multi - physics and nonlinear physics behaviors on the interface,introducing additional flexibility and damping to the overall structural dynamics. The Iwan model is applied to model and simulate the joint beam system. The nonlin-earity characteristics are extracted by EMD method and applied to train the backpropagation neural net-works. Then,the nonlinear mechanic model is identified by the experimental nonlinearity of jointed beam,which is applied to simulate the joint interface invested by the result of experiment. The results show that:based on the BP neural networks,the nonlinear characteristics can be applied to establish the nonlinear mechanic model of joint interface and the simulation and experimental results have a good coher-ence.%连接界面上存在的多尺度、多物理场和非线性的物理机理是引起结构能量耗散和刚度非线性的主要原因。采用 Iwan 模型模拟连接结构进行连接梁的动力学仿真，利用 EMD（Empirical Mode Decomposition，EMD）提取时域信号的非线性特征训练 BP 神经网络，再设计连接梁实验辨识连接界面的非线性力学模型参数，将辨识建立模型运用在连接结构中进行数值仿真并与实验结果对比。结果表明：利用 EMD 非线性特征进行 BP 神经网络训练能够建立有效的连接界面非线性力学模型，仿真结果与实验结果具有较好的一致性。
基于BP神经网络的油松人工林生长模型%Growth Model of Pinus tabulaeformis Plantation Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
徐步强; 张秋良; 弥宏卓; 常亮; 春兰; 吴彤
2011-01-01
A BP artificial neural network model for the growth of tree height of Pinus tabulaeformis artificial forests in Mount Daqing, Inner Mongolia was established, with the help of logsig, tansig, trainlra and other functions in Matlab neural net work toolbox, using average forest age, canopy density and site class of the inventory data for management as the input lay er, and average tree height as the output layer. Result shows that the fitting precision of the model is 99. 98% , and the testing accuracy is 98. 62% , which means that the model has good forecasting ability for the growth of local artificial forests of P. Tabulaeformis.%以内蒙古大青山油松人工纯林作为研究对象,利用Matlab神经网络工具箱中的logsig、tansig、trainlm 等函数,采用二类调查数据中的平均年龄、郁闭度、地位级作为输入层,平均高为输出层,建立油松人工林树高生长BP人工神经网络模型.结果表明,模型的拟合精度为99.98％,检验精度为98.62％,说明该模型对当地的油松人工林生长具有良好的预测能力.
Research of Image Compression Based on Quantum BP Network
Directory of Open Access Journals (Sweden)
Hao-yu Zhou
2013-07-01
Full Text Available Quantum Neural Network (QNN, which integrates the characteristics of Artificial Neural Network (ANN with quantum theory, is a new study field. It takes advantages of ANN and quantum computing and has a high theoretical value and potential applications. Based on quantum neuron model with a quantum input and output quantum and artificial neural network theory, at the same time, QBP algorithm is proposed on the basis of the complex BP algorithm, the network of a 3-layer quantum BP which implements image compression and image reconstruction is built. The simulation results show that QBP can obtain the reconstructed images with better quantity compared with BP in spite of the less learning iterations.
Road Roughness Detection and Simulation based on BP Neural Network%基于BP神经网络的路面不平度检测与仿真
Institute of Scientific and Technical Information of China (English)
崔丹丹; 张才千; 韩东
2014-01-01
The road roughness increases the vibration of the vehicle which seriously affects the life of the road and ride comfort. In order to identify and analyse road surface power spectral density, a method based on BP neural net-work to detect road roughness was proposed. The four degrees of freedom vehicle vibration model was used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS/CAR as the input samples and the road surface power spectrum density as output samples, the nonlinear mapping was found by the application of BP neural network. Another simulation input data were used in the trained network as the road spectrum identification. The results show that this method has better abil-ity of anti-noise and ideal identification accuracy, and the road surface spectrum of identification fits the imitated road surface spectrum.%在对路面不平度优化检测问题的研究中，由于路面不平使车辆振动加剧，严重影响了路面的使用寿命和乘坐的舒适性。功率谱密度是评价路面不平度的常用指标，为了识别分析路面功率谱密度，提出了一种采用BP神经网络的路面不平度检测方法。以四自由度车辆振动模型为基础，把ADAMS/CAR中车辆平顺性仿真得到的汽车质心垂直加速度谱和俯仰角加速度谱为输入样本，以路面功率谱密度为输出样本，应用BP神经网络建立非线性映射。将仿真数据代入已训练好的网络中进行路面功率谱识别，仿真结果表明：上述方法识别出的功率谱密度与实际功率谱密度的平均误差仅为1.23%，具有较强的抗噪声能力和较理想的识别精度。
The Application of BP Networks to Land Suitability Evaluation
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods' limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.
Institute of Scientific and Technical Information of China (English)
宋宇辰; 何玮; 张璞; 韩艳
2014-01-01
Resource-based city sustainable development prediction system is a complex system and influ-enced by social ,economic and environment factors .So it is difficult to predict it by traditional methods .In this paper ,the back propagation algorithm (BP) was introduced to predict the sustainable development prediction system in Baotou city .We built a 5-8-1 BP neural network prediction model and used the Matlab tool to analysis and forecast the urban sustainable development index system .The results showed that the relative error is little and precision is high .We use this model to predict Baotou city comprehensive index in the next five years .Interrelated analyses conclude that the sustainable development ability is rising .Fi-nally ,the suggestions about the sustainable development of resource-based cities are discussed .%资源型城市可持续发展预测系统受到社会、经济、环境等各种因素的影响，采用传统方法对其预测比较困难。鉴于BP 神经网络在非线性领域预测中的广泛应用，文章以包头市为研究对象，构建一个5-8-1结构的BP神经网络预测模型，借助M atlab工具对城市可持续发展指标进行了分析预测。结果表明，BP 神经网络预测结果与实际数据的相对误差较小，精度较高。运用此模型预测包头市未来五年可持续发展水平是波动上升的。最后根据预测结果提出资源型城市可持续发展的建议。
Vonk, E.; Jain, L.C.; Veelenturf, L.P.J.
1995-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
Institute of Scientific and Technical Information of China (English)
朱正平; 吴仁喜
2015-01-01
为了改进传统的电离层频高图手动度量方法，提出了基于BP神经网络的数字频高图F层自动度量方法，该方法首先采用阈值去噪、多次回波和泄漏去除，并使用A45方法将频高图转换成单元点；然后采用BP神经网络的自适应训练拟合单元点图获取O波和X波F层描迹；最后根据描迹斜率变化寻找F1和F2层的分界点以及采用曲线拟合技术补偿F2层临界频率区域完成参数判读。该技术应用于中南民族大学新研制的数字测高仪测量的频高图自动度量中，获得了初步结果，度量的fo F2和h′F2准确率分别达到81％和78％，结果表明：该方法具有较好的可靠性。%In order to improve the traditional manual scaling of ionograms, a new method for automatic scaling the F layer parameters of ionograms based on BP neural network is presented in this paper.This method firstly uses threshold denosing, multiple echo and leakage removal and A45 method to turn ionograms into point cells.Then adaptive training of BP neural network is adopted to fit the point cells so as to obtain O and X traces.Finally the cut-off points between F1 and F2 layer are found out according to the variation of the slope of traces and the curve fitting is used to fill the missing of F2 cusp ultimately to complete reading the F layer parameters.This method is applied to the automatic scaling of ionograms collected with the newly developed portable digital ionosonde ( PDI) by South-Central University of Nationalities and some preliminary results are obtained.The results reveal that the accuracies of the scaled fo F2 and h′F2 are 81 percent and 78 percent respectively and this method is practical.
应用BP神经网络预测油页岩含油率%Application of BP neural network in oil content prediction
Institute of Scientific and Technical Information of China (English)
胡启华; 范晶晶; 张新
2014-01-01
Method of△logR and advanced method of△logR arre usually adopted to calculate oil content of oil shale with log data. These methods easily cause some errors in the process of calculating parameters, and these methods are based on linear relation between oil content and characteristic log values. However, it was absolutely a nonlinear relation between them in the actual heterogeneous stratum. Therefore, BP neural network based on LM ( Levenberg-Marquardt ) algorithm was adopted to calculate the oil content in Jurassic strata of northern Qaidam basin. Firstly, mathematical statistics distribution feature of log data were analyzed with Matlab; Ssecondly, oil content values were predicted with BP neural network based on LM algorithm after the excellent samples had been chosen; finally, a matrix composed of 40 link weights and 11 thresholds was the parameter interpretation model of oil content. Results of the BP neural network prove that theoretical calculating values match well with the core experimental measuring values, and the mean square error can be controlled within 0. 191 8. Therefore, this parameter interpretation model can be promoted in the area of the same geology background.%根据测井资料计算油页岩含油率多采用△logR法或改进的△logR法，这些方法中参数获取过程中易产生诸多误差，且这些方法是建立在油页岩含油率与特征测井曲线值是线性关系的基础上的，而在实际非均质性地层中，测井对油页岩含油率参数的响应在本质上必然是非线性的。基于此，运用BP神经网络来预测柴达木盆地北部地区侏罗纪油页岩含油率。首先分析研究区段测井数据的数理统计分布特征，在优选学习样本的基础上再采用一种基于LM（ Levenberg-Marquardt）算法的BP神经网络进行含油率预测，最后得出一组由40个连接权值与11个阈值组成的含油率参数解释模型，油页岩含油率预测值与岩心实验室
Application of BP neural network in evaluation of artistic voice%BP神经网络在评价歌唱艺术嗓音中的应用
Institute of Scientific and Technical Information of China (English)
李小武; 罗兰娥
2012-01-01
The singing voices were recorded from 30 young music students who come from Hunan University of Science and Engineering. Their acoustic parameters, such as Fl, F3, F0, vocal range,jitter, disturbance of Fl, disturbance of F3 and average energy were extracted by the way of voice analysis, BP Neural network analysis was used to evaluate the singing voices objectively. The results were then compared with those of the subjective evaluation performed by the experienced professionals. The error between the two evaluation approachs was within 3.4%, The results show that the neural network analysis can be used as an objective instrument to evaluate the singing quality of artistic voices. This is helpful to instruct, select and train professional singers.%录制湖南科技学院30名无喉病、无上呼吸道感染的声乐专业青年大学生专业训练歌声信号,利用语音分析技术提取歌声声学参数第一共振峰、第三共振峰、基频、音域、基频微扰、第一共振峰微扰、第三共振峰微扰、平均能量,使用BP神经网络方法客观评价歌声质量,并与资深声乐专业教师的主观评价进行比较,误差在3.4％之内.结果表明BP神经网络方法利用评价参数能正确客观评价歌声质量,有助于科学地指导选拔和训练艺术嗓音人才.
基于BP神经网络的番茄干重预测研究%Prediction study of tomato dry weight based on BP neural network
Institute of Scientific and Technical Information of China (English)
王丽艳; 郭树国
2012-01-01
以番茄干重作为正交试验指标,研究温室内番茄生长的环境参数(温度、相对湿度、光照强度)对番茄干重的影响,建立BP神经网络模型,运用MATLAB对试验数据进行训练和模拟,为检验预测的可靠性,采用10-折交叉验证,准确率为95.32％.结果表明,利用BP神经网络得出预测值与实测值接近,具有较好的预测性,可用于干重的预测,能够为温室环境调控提供科学依据.%Orthogonal experimental was carried out using the tomato dry weight as experimental objective, and the impact of environmental parameters (temperature, relative humidity, light intensity) on tomato dry weight was analyzed through experiment. A neural network calculation model was established based on experimental data and the test were made by using MATLAB software, and to better verify effectiveness of the approach, a 10-fold cross validation method was used. Through 10-fold cross validation, model achieved the predictive accuracy of 95. 32%. The results showed that the predicted values were in good agreement with the experimental values. This method has high prediction precision, which can be used theory instruction in environment control, and the predicted dry weight of tomato with the BP neural network method was feasible.
Institute of Scientific and Technical Information of China (English)
谢香峰; 雷电; 孙承波
2012-01-01
This paper analyzes the practical significance of fault diagnosis method researching of switching power supply. For switching power supply circuit characteristics, the combination parallel processing method of functional BP neural network and submodule BP network base on BP neural network data fuzzy and functional test theory is presented in this paper. Firstly, the establishment of large BP neural network is avoided in this method. Secondly, it is used to solve the low efficiency of serial processing function modules of large-scale analog circuit. The test and simulation results show that the proposed method can effectively diagnose faults of analog circuit functional modules and components.%分析了开关电源故障诊断方法研究的现实意义,以BP神经网络、数据模糊化和功能测试理论为基础,针对开关电源电路特征提出了采用功能BP网络与子模块BP网络相结合并行处理方法,首先避免了建立庞大的BP网络,其次解决了大规模模拟电路功能模块串行处理效率低的问题.测试和仿真结果表明,所提出的方法能够有效地诊断模拟电路故障模块和故障元件.
Identification of Mining Road Roughness Based on GA-BP Neural Network%基于 GA-BP 网络的矿山路面不平度辨识
Institute of Scientific and Technical Information of China (English)
谷正气; 朱一帆; 张沙; 马骁骙
2014-01-01
BP neural network optimized by GA was used to identify the mining road.A fourteen degree-of-freedom vehicle vibration model was set up.The vehicle seat acceleration obtained by simu-lation was regarded as an ideal input sample of neural network,and the fitting road roughness was re-garded as an ideal output sample of neural network based on inverse transformation principles,then the nonlinear mapping model between them was built by network training.Road roughness was iden-tified under the conditions of different grade roads through fitting,various pit roads and different loads of dump truck.Identification ability was verified for complex mining roads due to high correla-tion coefficient and small relative error in this method.The accuracy of the method was verified through vehicle road test.Compared with simulation results of ride comfort under common C-class roads,it is shown that identification road is more closer to actual one,so as to achieve the purpose of improving the simulation accuracy of the models.%提出利用经遗传算法优化的 BP 神经网络辨识矿山路面的方法。建立了14自由度自卸车仿真模型，将仿真得到的座椅加速度作为网络理想输入样本，基于逆变换原理拟合出的路面不平度作为网络理想输出样本，通过网络训练，建立了两者之间非线性映射模型。对拟合出的不同等级路面、各种凹坑路面及自卸车不同载重下路面不平度进行辨识，辨识路面与测试路面相关系数高、相对误差小，验证了该方法具有对复杂矿山路面的辨识能力。通过整车道路试验，证明了该方法的准确性。与自卸车常用 C 级路面下的平顺性仿真结果的对比显示，采用该方法得到辨识路面更加接近实际路面，达到了提高模型仿真精度的目的。
基于BP神经网络的水果分级研究%Classification of fruit based on the BP neural network
Institute of Scientific and Technical Information of China (English)
姚立健; 边起; 雷良育; 赵大旭
2012-01-01
This paper introduced one classification method for fruit grade based on BP neural network. Fruit images were preprocessed by using digital image processing method. The mean color value and variance of fruit surface were selected to express fruit color features. An ellipse that has the same normalized second central moments with the fruit region were adopted to approximately represent fruit shape. It simplified the complex degree of the shape description. The optimum structure parameters of the BP neural network which had 9 hidden layer neurons were determined by RP training algorithm. Results showed that average accuracy for fruit classification can reach 92. 5% by using this model , and the executing time of microcomputer for grading of one apple is 10. 3 ms. This method has the characteristics of high accuracy and good real-time performance.%文章介绍了一种基于BP神经网络的水果分级方法.采用数字图像处理的方法对图像进行预处理,选择水果表面颜色的均值和方差来表示水果的颜色特征,采用一个与水果目标具有同样二阶矩的椭圆来近似表示水果的形状,简化了果形描述的复杂程度.通过RP算法训练,可以得到一个具有9个隐层神经元的BP神经网络结构参数.试验表明:采用该模型对水果等级进行分级,平均正确率为92.5％,分级一个水果的时间为10.3 ms.说明采用BP神经网络技术可实现对水果等级的自动判定,该方法具有正确率高、实时性好的特点.
基于BP神经网络的客户信用风险评价%Customer credit risk assessment based on BP neural network
Institute of Scientific and Technical Information of China (English)
于彤; 李海东
2014-01-01
The banks in China shouls pay more attention to the customer credit risk assessment because the commercial bank credit risk management in China is insufficient,which has seriously affected the development of banks. The formation cause of the bank credit risk and the problems existing in the assessment are analyzed. The customer credit risk assessment in-dex system was established on the basis of financial situation of enterprises. The 160 samples in listed companies in Chinese manufacturing industry were selected randomly,including 36 ST companies and 144 non ST companies,and then tested based on three-layer BP neural network training. It is found in the research that the BP neural network is suitable for the credit risk as-sessment,and its accuracy is better than that of Logistic regression model. Some suggestions and countermeasures to the credit risk management of Chinese commercial banks are put forward.%我国商业银行信用风险管理不足，已经严重影响银行的发展，因而银行需要重视客户信用风险评估。分析了银行信用风险的成因及评估存在的问题，从企业的财务情况出发，建立了客户信用风险评估指标体系。随机选取了我国制造业的160个上市公司样本，包括36个ST企业和124个非ST企业，并基于三层BP神经网络对样本进行训练及仿真测试，研究发现BP神经网络适用于信用风险评估，且其准确性优于Logistic回归模型。最后，从银行、企业、政府三个角度出发，对我国商业银行信用风险管理提出了一些建议及对策。
Institute of Scientific and Technical Information of China (English)
李德富; 翁克瑞; 杨娟; 诸克军; 李志; 曹洪
2012-01-01
目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别.为克服这一问题,结合模糊C均值算法和BP神经网络实现难采储量的分类.首先基于效益指标运用模糊C均值算法自动搜索储量的最佳类别,再利用BP神经网络建立储量效益指标类别与储量属性指标之间的关系表达式.在已知储量指标值的情况下,通过此关系式即可求得储量的类别.最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策.%Currently, the classification and evaluation criterion of reserves were determined through the scope of the, criteria value, which required all criteria values were just right in the existing range of criteria. Otherwise it would be difficult to divide the reserves category. To overcome this problem, this paper combined with Fuzzy C-Means clustering algorithm (FCM) and BP neural network method to classify difficult recoverable reserves. First use FCM to automatically search for the optimal category of reserves, based on performance indicators. And then establish the relational expression between the reserves category and reserves properties by BP neural network. So in the case of the criteria value known, the categories of reserves can be obtained through this relational expression. Finally take the case of an oil field in the 10th Oil Production Plant of PetroChhm Daqing Oilfield LLC, and evaluate the recoverable reserves, which conducts the rolling development of recoverable reserves.
Target Recognition Algorithm Based on BP Networks and Invariant Moments
Directory of Open Access Journals (Sweden)
Gao Tian
2013-02-01
Full Text Available On the basis of multi-sensor fusion algorithm, a target recognition algorithm based on Back Propagation (BP neural networks and invariant moments was proposed. Invariant moment takes advantage of overall information of the targets. It has good differentiating effect and high identification technique. On the other hand, BP neural networks not only have the adaptive learning ability, but also are insensitive to imperfection of input mode. Therefore, it has proper classification and extensibility. It is effective for the algorithm based on BP neural networks and invariant moments that decrease the adverse impacts for the images, which are always subject to the changes of imaging distance, direction and position. Simulation results show that the algorithm has strong recognition capability for surface targets from infrared image sensors.
Application of improved BP-neural network in the molecular distillation process%改进BP-神经网络在分子蒸馏过程中的应用
Institute of Scientific and Technical Information of China (English)
陶权; 孙浩津; 刘克平; 姜长泓
2013-01-01
针对分子蒸馏过程多变量、非线性、内部机理复杂、建模困难等问题，基于神经网络自学习、自适应及强非线性映射能力，提出了改进的BP神经网络产品纯度预测模型，深入探讨了神经网络在分子蒸馏过程中的应用。实验证明所提出的模型可以用来预测产品纯度。%The molecular distillation process has the features of multiple variables , nonlinearity , complex internal mechanism and difficult modeling . Based on self-learning , self-adapt and strong nonlinear mapping properties of the modified BP neural network ,a estimation model for the product purity is put forward and applied into the molecular distillation process .The experiments verify that the model is suitable for the product purity estimation .
BP Network Based Users' Interest Model in Mining WWW Cache
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP(back propagation) neural network. By this method, the users' interest in the WWW cache can be described and the neural network of users' interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users' interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.
基于BP神经网络的语音情感识别研究%Speech Emotion Recognition Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
徐照松; 元建
2014-01-01
随着科技的迅速发展，人机交互越来越受到人们的重视，语音情感识别更是学术界研究的热点。将BP神经网络算法用于语音情感识别研究，并在汉语情感数据集上进行了相关实验，识别的准确率达到了91．5％，相较于SVM算法分类精度提高了5％。%With the rapid development of technology ,human-computer interaction more and more suffer people’s attention . Research on speech emotion recognition is the focus of academic .In this article ,we use the BP neural network algorithm to research on speech emotion recognition and conducted experiments on chinese sentiment data sets ,recognition accuracy rate reached 91 .5 percent ,compared to the SVM accuracy is improved by 5% .
Institute of Scientific and Technical Information of China (English)
阎兴頔; 杨文; 马贺贺; 侍洪波
2012-01-01
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammo- nia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the pro- duction efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
Liu, Fei; He, Yong; Wang, Li
2007-11-01
In order to implement the fast discrimination of different milk tea powders with different internal qualities, visible and near infrared (Vis/NIR) spectroscopy combined with effective wavelengths (EWs) and BP neural network (BPNN) was investigated as a new approach. Five brands of milk teas were obtained and 225 samples were selected randomly for the calibration set, while 75 samples for the validation set. The EWs were selected according to x-loading weights and regression coefficients by PLS analysis after some preprocessing. A total of 18 EWs (400, 401, 452, 453, 502, 503, 534, 535, 594, 595, 635, 636, 688, 689, 987, 988, 995 and 996 nm) were selected as the inputs of BPNN model. The performance was validated by the calibration and validation sets. The threshold error of prediction was set as +/-0.1 and an excellent precision and recognition ratio of 100% for calibration set and 98.7% for validation set were achieved. The prediction results indicated that the EWs reflected the main characteristics of milk tea of different brands based on Vis/NIR spectroscopy and BPNN model, and the EWs would be useful for the development of portable instrument to discriminate the variety and detect the adulteration of instant milk tea powders.
Application of BP Neural Network on Health Warning of Forest Ecosystem%BP神经网络在森林健康预警中的应用
Institute of Scientific and Technical Information of China (English)
卞西陈; 陈丽华; 王鹏; 王萍花
2011-01-01
以河北省北沟林场的森林生态系统健康预警为主线,首先通过定量分析和定性分析相结合的方法对森林健康预警指标进行筛选,确定了预警的指标体系;进而基于BP神经网络的基本原理建立了森林生态系统健康预警模型,对森林生态系统健康状况进行了预警。结果表明,北沟林场森林生态系统整体处于绿色和蓝色警戒内,健康状况良好。%Taking forest ecosystem health early warning in the North Ditch,Hebei Province as the main line,early warning indicators of forest health were firstly filtered by the method of quantitative analysis,then early warning indicator system was identified.Next,early health warning model of forest ecosystem based on the basic principles of BP neural network was established,and then it was used to make early warning on the health of forest ecosystems.The results showed that North Ditch forest ecosystem was in good health,in the whole,the North Ditch in the range of the green and blue alert.
基于BP神经网络模型的国内旅游人数预测%Prediction of Domestic Tourists Based on BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
郭庆春; 孔令军; 崔文娟; 史永博; 张小永
2011-01-01
旅游人数的分析和预测是旅游规划与管理的关键性、基础性工作.目前旅游人数预测主要采用基于传统研究方法的预测方法.提出了一种基于BP神经网络模型的国内旅游人数预测新方法,对国内旅游人数的变化趋势进行了综合分析与预测,结果表明该方法具有较高的精度,该模型在旅游人数预测中的应用是可行的.%Analysis and prediction of tourist population are the key and basis work of tourism planning and management. At present, prediction of tourist population is mainly based on traditional research approach. The paper proposes a new forecast approach based on BP neural network model and makes comprehensive analysis and prediction of the changing trend of tourist population. Forecast results indicate that this approach is more precise. The model is feasible in the forecast of tourists.
Institute of Scientific and Technical Information of China (English)
张佳伟; 张自嘉
2012-01-01
In order to improve the photovoltaic power forecasting accuracy,the influencing factors of photovoltaic power system's output are analyzed and a particle swarm optimization algorithm is built for BP neural network prediction model of photovoltaic power forecasting. The particle swarm optimization algorithm is used to optimize the internal connection weights and thresholds of neural network in this model. Combining the advantages of the particle swarm optimization and BP neural model, the model achieves a better convergence speed, generalization performance and prediction accuracy. Taking photovoltaic power plant historical data and weather conditions as samples, the model completes training and prediction. The prediction results show that with the particle swarm optimization, BP neural network model prediction accuracy is higher than typical BP neural network, which verifies the effectiveness of the method.%对光伏发电影响因素进行了分析,建立了粒子群算法优化的前向神经网络光伏系统发电预测模型.该模型利用了粒子群算法来优化神经网络内部连接权值和阈值,兼具粒子群和BP神经模型的优点,具有较好的收敛速度,泛化性能与预测精度.将光伏电站发电历史数据与天气情况作为样本,运用所建立的模型进行了训练与预测.结果表明,经过粒子群优化的BP网络模型预测精度高于典型BP网络,验证了该方法的有效性.
Institute of Scientific and Technical Information of China (English)
许璟; 南敬昌
2013-01-01
In the communication system-level simulation, it is extremely important for the design and optimisation of RF power amplifiers to build accurate behavioural models .Based on the BP neural network model , we use a hybrid algorithm which combines the genetic algorithm with particle swarm algorithm to optimise the network , build GAPSO_BP amplifier behavioural model , and simulate the model by using Doher-ty structure amplifier input and output voltage data .Through the comparison between the root mean square error of voltage and the conver-gence rate, it eventually comes to a conclusion that the model based on GAPSO has a better fit than the model based on original two algo -rithms, the mean square error between the PA actual output and the modal output reaches 0.0011, and thus it is more accurate to describe the nonlinear characteristics of RF PA .%在通信系统级仿真中，精确构建行为模型对设计和优化射频功率放大器是极为重要的。在BP神经网络模型的基础上，提出一种由遗传算法和粒子群算法结合的混合算法对网络进行优化，构建GAPSO＿BP功放行为模型，利用Doherty结构的功放输入输出电压数据对模型进行仿真。通过对电压均方根误差以及收敛速度的比较，最终得出基于GAPSO算法构建的模型比基于原有两种算法构建的模型有更好的拟合性，功放实际输出和模型输出之间的均方误差达到了0．0011，进而可以更精确地描述射频功率放大器的非线性特性。
Institute of Scientific and Technical Information of China (English)
易洪雷; 丁辛
2001-01-01
Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.
Institute of Scientific and Technical Information of China (English)
吴雄喜
2013-01-01
Based on BP neural network,the mechanical properties parameters of AZ91 magnesium alloy under different annealing conditions were obtained by homogenizing annealing. The results show that BP neural network can map relationship between heat treatment process and material properties very well,and prediction accuracy is very good.%基于BP神经网络法，利用均匀化退火工艺改善AZ91镁合金的组织结构，获得了不同退火状态下材料的力学特性参数。结果表明，BP神经网络能够很好地映射热处理工艺与材料性能间的关系，实验值与预测值重合度很好。
Institute of Scientific and Technical Information of China (English)
李春生; 谭民浠; 张可佳
2011-01-01
In order to ensure oil field production for sustainable development, aim at oil field production, the predictive model based on improved BP neural networks is put forward. The structure of traditional BP neural networks and its training algorithm are studied, some weak points of it are found, such as easy to fall into local minimum value and slow convergence. The BP neural networks improved by L-M algorithm is put forward. At the end, simulation experiment of the predictive model based on improved BP neural networks is illustrated. The result of which proves the practicability and the feasibility of this algorithm and high use value in the oil well production prediction.%为了保证油田生产持续稳定地发展,针对油田单井产量提出了基于改进型BP神经网络的预测模型.对传统的BP神经网络的结构和训练算法进行了研究,发现它存在易于陷入局部极小,收敛速度慢等问题.提出了使用LM算法的改进型BP神经网络.最后给出了基于改进型BP神经网络的单井产量预测模型仿真实验.结果证明该算法的实用性和可行性,在油井产量预测方面有一定的实用价值.
Research on Armored Equipment Demand Forecasting Based on BP Neural Network%基于遗传BP神经网络的装甲装备器材需求预测
Institute of Scientific and Technical Information of China (English)
可荣博; 王铁宁; 宋宁波
2015-01-01
Armored equipment material support has some particular features,including large scale, time urgency,large consumption,a lot of uncertain factors and difficult decision. Accurate demand forecasting is an important prerequisite to implement an initiative and refinement equipment protection. In this paper,BP neural network learrning and self-adaptive ability is used to learn the law of equipment demand,genetic algorithm is used to improve BP neural network convergence speed. A genetic algorithm improved BP neural network algorithm is proposed for forecasting equipment demond. The experiments show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.%装甲装备器材保障具有规模大、时间紧、消耗大、不确定因素多、决策难度大等特点。准确的需求预测是实施主动的、精细化的器材保障的重要前提条件。利用BP神经网络较强自学习能力和自适应能力对器材需求规律进行学习，并借助遗传算法提高BP神经网络的收敛速度，设计了一种基于遗传算法改进的BP神经网络模型预测方法，对装甲装备器材进行需求预测。通过实例计算表明，该方法比单纯BP神经网络方法具有预测精度高、收敛速度快的优点。
基于改进的BP神经网络的柴油发动机故障诊断%Research of diesel engine fault diagnosis based on improved BP neural network
Institute of Scientific and Technical Information of China (English)
巴寅亮; 王书提; 谢鑫
2015-01-01
Diesel engine with high pressure common rail fuel injection technology, improves the com-prehensive performance of diesel engine, but the high pressure common rail diesel engine electronic con-trolled system is more complex, increasing the difficulty of diesel engine fault diagnosis. This paper intro-duce the BP neural network and LM algorithm, and carry on the research on fault diagnosis of engine e-lectronic controlled system based on improved BP neural network. Taking the Great Wall Harvard GW 2. 8TC engine as the experimental object, keeping the engine at idle speed condition, setting up some fault assumption for the engine, collecting the failure data flow of the engine by kinder KT600 fault diag-nosis instrument, using improved BP neural network to establish diagnosis model. The diagnosis results show that the convergence rate of improved BP neural network is quickly, it is effective to diagnose elec-tronic controlled system fault of diesel engine by improved BP neural network.%柴油发动机采用高压共轨燃油喷射技术，提高了柴油机的综合性能，但高压共轨柴油机电控系统比较复杂，增大了柴油机故障诊断的难度。该文介绍了BP神经网路及LM算法，并利用改进的BP神经网络对发动机电控系统故障进行诊断研究。以长城哈佛GW2.8 TC发动机为实验对象，让发动机在怠速状态下，对发动机进行故障设置，利用金德KT600故障诊断仪采集发动机的故障数据流，运用改进的BP神经网络建立诊断模型，诊断结果表明改进的BP神经网络的收敛速度快，运用改进的BP网络诊断柴油机电控系统故障是行之有效的。
Institute of Scientific and Technical Information of China (English)
辛菊琴; 蒋艳; 舒少龙
2013-01-01
Personal recommendation is very effective to find the useful information from database of products for customers in electronic commerce. The paper investigates personal recommendation algorithms based on customer preference model and BP neural networks. In details, a customer preference model is proposed and BP neural network is used to train the model. Movielens database is used to verify the validity of BP neural network model. A content-based personal recommendation algorithm is proposed.%个性化推荐是目前解决电子商务中产品信息过载问题的有效工具之一.对综合用户偏好模型和BP神经网络的个性化推荐算法进行了研究.具体讨论了如何建立用户偏好模型,采用神经网络训练得到目标用户的偏好模型,通过Movielens数据库验证该模型的有效性.提出了一个基于内容的个性化推荐算法.
Detection of Fraudulent Financial Statements Based on BP Neural Network%基于BP神经网络的虚假财务报告识别
Institute of Scientific and Technical Information of China (English)
邓庆山; 梅国平
2009-01-01
针对虚假财务报告的特点,设计了一种基于BP(反向传播)神经网络的虚假财务报告识别模型.根据1999～2002年的年度审计报告意见,从上市公司中,选择确定了44家虚假财务报告样本,并按照一定的标准选择了44家真实财务报告样本,这88个样本构成训练数据集.类似地,从2003～2006年的上市公司中,选择了73家虚假财务报告样本和99家真实财务报告样本,这172个样本构成测试数据集.10个财务指标被选择为识别变量,使用训练数据集对BP神经网络模型进行训练,并将训练后的模型对测试数据集进行测试,取得了较好的实验结果.%Considering the characteristics of fraudulent financial statements(FFS),this paper designs a FFS detection model based on BP neural network.To carry out the experiment,we choose 44 FFS according to the auditing reports and 44 true financial statements according to some specific standards during 1999-2002 as training data set.Similarly,73 FFS and 99 true financial statements during 2003-2006 are chosen as testing data set.Ten financial ratios are chosen as detection variables.We train the model by using training data set and apply the trained model to the testing data set,good experimental results are obtained.
基于改进的BP神经网络的Overlay网络流量预测%Overlay network traffic prediction based on advanced BP neural network
Institute of Scientific and Technical Information of China (English)
傅秀文; 郑明春
2012-01-01
With the increase of the scale of Internet, network traffic prediction on Overlay has become a research focus gradually. Compared with traditional networks, the features of Overlay network mean that traditional predictions are out of its demand. It proposes a new method that is based on neural network using particle swarm-based simulated annealing, and applies reverse calculation, starts from the ideal optimal value and finds the optimal solution through the shortest path. This method increases the probability of success to find the optimal solution and cut off the running time. Through the simulation it deduces that the proposed method is better than the traditional one obviously.%随着网络规模的增长,Overlay网络流量预测已经日渐成为研究热点.与传统网络相比,Overlay网络本身的特性决定了传统的预测方法已不能适应它的要求.提出一种基于模拟退火的粒子群神经网络来预测Overlay网络的流量,运用反向计算方法,从理想最优值出发,近距离寻找最优解,缩短了求解时间并加大了找到最优解的几率.通过实验仿真可以看出,改进的BP神经网络方法的预测效果要明显好于传统的BP神经网络.
Tactile Pattern Recognition Based on BP Neural Network%基于神经网络的触觉感知方向识别研究
Institute of Scientific and Technical Information of China (English)
周嵘; 吴皓莹
2016-01-01
触觉感知信息的模式识别可以有效提高人机交互的效率,为此设计了一种触觉传感单元功能模块,可以在2D平面内识别人机接触的方向信息. 采用PCA算法来提取触觉感知数据特征,从而去除数据的噪音并且降低维度;采用BP神经网络对人机接触方式进行识别分类,提高鲁棒性. 对于不同实验对象的训练样本和测试样本进行数据采集,结果表明该方法可以实现93 .1%的模式识别正确率.%To improve the efficiency of communication in human-robot cooperation through tactile information, this paper proposes a method to recognize human intended direction in 2-D using an equipment with tactile arrays.The PCA method is em-ployed in this study to extract essential information thus reduce computation complex and increase robustness.BP neural network is implemented for classifying the intended direction of human operators.Three members of the project team were involved in the study.The efficiency of proposed algorithm is investigated.Experimental results shows that the proposed method could achieve 93.1%recognition accuracy if both the training data and validation data contain tactile images from all the users.
Manger, R
1998-01-01
Holographic neural networks are a new and promising type of artificial neural networks. This article gives an overview of the holographic neural technology and its possibilities. The theoretical principles of holographic networks are first reviewed. Then, some other papers are presented, where holographic networks have been applied or experimentally evaluated. A case study dealing with currency exchange rate prediction is described in more detail.
Institute of Scientific and Technical Information of China (English)
林志贵; 姚芳琴; 冯林强; 杜军兰; 李建雄
2015-01-01
针对目前营养盐检测主要是通过化学方法实现，无法获得在线检测的问题，利用营养盐与其影响因子之间的关系，提出结合自适应遗传算法与弹性BP神经网络的预测模型。利用改进的自适应遗传算法，通过交叉、变异获取弹性BP神经网络的初始权值与阈值，加速预测过程。该模型通过营养盐影响因子数据，预测亚硝酸盐浓度。仿真结果表明：基于弹性BP神经网络的预测模型预测营养盐浓度是可行的，其预测得到的亚硝酸盐浓度值的相对误差主要集中于0~30%；结合自适应遗传算法与弹性BP神经网络的预测模型的预测效果好于基于弹性BP神经网络的预测模型。%Currently nutrients are detected by the chemical method. A chemical method cannot get online detection. To solve the problem, based on the relationship between nutrients and their impact factors, a prediction model which combined Adaptive Genetic Algorithm and Elastic BP Neural Network is put forward in this paper. Using the improved Adaptive Genetic Algorithm, the initial weights and thresholds of Elastic BP Neural Network are obtained by the crossover and mutation to accelerate the prediction process. The imporoved model predicts the nitrite by using the data of its impact factors. Simulation results show that it is feasible to predict the nutrient concentration by using the prediction model based on the Elastic BP Neural Network. The relative error of nitrite concentration value mainly focuses on 0-30%. The prediction model based on Adaptive Genetic Algorithm and Elastic BP neural network is better than that based on Elastic BP Neural Network.
Institute of Scientific and Technical Information of China (English)
王秀坤; 张晓峰
2001-01-01
In this paper,a new architecture of single output multilayered feedforward neural networks array and a learning algorithm based this architecture are proposed. This architecture can be used to re-place of a multiple outputs multilayered feedforward neural networks. Theoretical analyses show this mothed outperforms the traditional multiple outputs multilayered feedforward neural networks.
Energy Technology Data Exchange (ETDEWEB)
Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)
1996-12-31
The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.
Quasi-BP neural network inversion of gravity gradient tensor%重力梯度张量的拟BP神经网络反演
Institute of Scientific and Technical Information of China (English)
郭文斌; 朱自强; 鲁光银
2011-01-01
基于重力梯度张量是反映重力场空间变化率的参数,比传统的重力异常具有更高的分辨率和更丰富的信息,将改进的BP神经网络算法应用于重力梯度张量的反演中并分析其反演效果.该算法是一种基于RPROP算法的拟BP神经网络反演算法,采用三层神经网络结构,用隐层神经元表示物性单元的密度值,根据RPROP算法自动修改各单元密度值,从而得出场源空间的密度分布.研究结果表明:采用这种算法对重力梯度张量进行反演计算,收敛速度快,对初始模型依赖性小,可准确反映出异常体形态特征和密度特征.%Based on the fact that gravity gradient tensor is a parameter which can reflect the spatial variation of gravity field, and that it has a higher resolution compared to the traditional gravity anomaly, a method for interpretation of gravity gradient tensor was proposed. The method is a kind of quasi-BP neural network algorithm which is based on RPROP algorithm. A three-layer network and the hidden layer neurons denote physics value were used. The physics value was automatically modified according to RPROP algorithm, and the physical distribution of field source was gotten. The results show that the method has a fast convergence speed and little dependence on initial model used in the inversion of gravity gradient tensor date, and can reflect the shape and density characters of anomalous body.
Institute of Scientific and Technical Information of China (English)
徐量; 张勤
2012-01-01
针对误差反向传播（BP）神经网络易陷入局部极小值的问题,将遗传算法（GA）与BP神经网络相结合,先以遗传算法全局最优的特性对初始化的BP网络的权重和阈值进行优化,再将优化的权重和阈值作为初值带入BP网络训练得到最优解.运用此改进的BP神经网络对竹盖山隧道初期支护钢拱架内力进行预测,取得了良好的效果,精度高、收敛快,为指导和控制工程施工提供了有效的依据.%This paper combined the genetic algorithm(GA)with the Back Propagation(BP)neural network to overcome the problem that BP algorithm is prone to local minimum.First,using the genetic algorithm global optimization characteristic optimized the weights and biases of a initialized BP network,then put the weights and biases as initial values into BP network for further training for searching optimum solution.It works well on the prediction of the stress about steel arch in the tunnel initial support of Zhugai Shan with this optimized BP neural network,which has high convergent speed and good prediction precision,and it is helpful to provide basis for controling the engineering construction.
面向轻汽油醚化的BP神经网络的模型预测控制%Light Gasoline Etherification Predictive Control with BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
程换新; 伊飞
2012-01-01
针对催化裂化轻汽油（Fcc轻汽油）醚化的过程提出了BP神经网络的模型预测控制，通过控制Fcc轻汽油的流速，来实现重油量浓度指标的控制。应用BP神经网络建立该过程的预测模型，并采用迭代优化的控制算法，根据相应的性能指标，不断地修正神经网络的权值，从而整定下一批次的控制信号。通过Matlab里的神经网络工具箱，建立一个有参考模型的神经网络预测控制器，观测最终的实际输出。%Predictive control with BP Neural Network model for etherification of Fcc light petrol is proposed. The control of heavy fuel oil concentration is realized by controlling the flow rate of Fcc light petrol. The prediction model for the process is built up with BP Neural Network with adopting iterative optimization algorithm, and the weights of neural network is corrected continuously based on the performance indicators to determine the next batch controlling signal. Neural network predictive controller is built with a reference model by using Neural Network toolbox in matlab, and observes the actual output.
Institute of Scientific and Technical Information of China (English)
姚明海
2013-01-01
The characteristics of genetic algorithm and BP neural networks are compared. As evolutionary algorithm neural net-work and genetic algorithm have same goal but they have different methods. The necessity of the combination genetic algorithm and neural networks is expounded. This paper puts forward a kind of improved genetic algorithm to optimize BP neural network weights, using the global random searching ability of genetic algorithm to make up the question that neural network is easy to fall into local optimal solution. At the same time, the crossover method of genetic algorithm is changed. The same generation does not cross. The parent and son are crossed. Genetic algorithm premature loss of evolutionary ability is averted.%对遗传算法和BP神经网络的特点进行了比较，作为进化算法神经网络与遗传算法的目标相近而方法各异。阐述了遗传算法与神经网络结合的必要性。提出了一种改进的遗传算法优化BP神经网络的权值，用遗传算法的全局随机搜索能力弥补了神经网络容易陷入局部最优解的问题。同时，在遗传算法中改变传统的同代交叉机制，采用父代与子代进行交叉，避免了遗传算法过早丧失进化能力。
Institute of Scientific and Technical Information of China (English)
梁哲浩; 鲁伟
2015-01-01
目的：探讨超声结合人工神经网络技术在女童中枢性性早熟诊断中的应用价值。方法选用170例性早熟女童进行常规超声检查子宫、卵巢，以其中130例的子宫体积、卵巢体积以及双侧卵巢最大卵泡内径为输入变量，以中枢性性早熟或非中枢性性早熟为输出变量，建立反向传播（BP）神经网络，并对另40例性早熟病例分类。结果利用 BP 神经网络结合常规超声检查对中枢性性早熟诊断的敏感性、特异性和准确率分别为95．0％、85．0％、90．0％。结论神经网络结合超声检查对中枢性性早熟的诊断和鉴别诊断具有较大的价值。%Objective To explore the value of ultrasonic combined with Back‐propagation artificial neural network in the diagnosis of central precocious puberty .Methods In 170 girls with precocious puberty ,the uterine and ovarian were ex‐amined with ultrasound ,in which 130 cases of uterine volume ,ovarian volume and bilateral ovarian follicles biggest diame‐ter were taken as inputs ,the central precocious puberty or non‐central precocious puberty as output variable .The back‐propagation (BP) neural network was established using such data .The other 40 cases were sorted by this BP neural net‐work .Results The diagnostic sensitivity ,specificity and accuracy of the BP neural network combination of ultrasound were 95 .0% ,85 .0% and 90 .0% ,respectively .Conclusion The BP neural network in combination of ultrasound is help‐ful in diagnosing central precocious puberty .
Cancer classification based on gene expression using neural networks.
Hu, H P; Niu, Z J; Bai, Y P; Tan, X H
2015-12-21
Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.
基于BP神经网络的盘管泄漏检测方法研究%Study of the coil-leak detective method based on the BP neural network
Institute of Scientific and Technical Information of China (English)
袁寅; 袁昌明; 王强
2011-01-01
The present paper is devoted to the study of the coil-leak detective method based on the BP neural network in hoping to extract its boundless application prospect. As a matter of fact, with the ever-increasing chemical safety demands, traditional offline pipe leak detection methods, such as pressure-keeping methods, which fail to meet the needs of on-line detection and control of the leakage of water coil of the reaction kettle for their poor real-time up-to-date performance. Flow balance method, though still effective in online leak-detection, also fails to meet the challenges of the fast-changing working conditions. Therefore, scientists began to face the challenge by using the flow balance method combined with neural network. In order to study the validity of this detection method, we have established an experimental platform of coil leak detection based on S7 - 300PLC. The platform can not only be able to simulate the leak of water coil, collect the flow data, but also produce warning alarms and help to control some sudden, unexpected leakage. Therefore, we have made an analysis of the flow changes in the inlet and outlet of the water coil of the reactor by means of a series of simulated experiments with the coil leakage, including the fast changing situations of working conditions and the leakage variations, we have also extracted characteristic signals (RMS) from the flow signal to protract RMS curve of flow. Careful comparison of the RMS curves of normal, leak and fast changing situations of working conditions, has offered us possibilities-to make clear the features of some quite different conditions. We have extracted RMS of the flow to construct the input matrix of the neural network. Through searching for a large number of experimental data to train the BP neural network, it becomes possible to work out the optimal neural network structures by comparing the network training error results of various structures. It is the BP neural network model we have
Directory of Open Access Journals (Sweden)
Vijaypal Singh Dhaka,
2010-02-01
Full Text Available Objective of this paper is to study the character recognition capability of feed-forward back-propagation algorithm using more than one hidden layer. This analysis was conducted on 182 different letters from English alphabet. After binarization, these characters were clubbed together to form training patterns for the neural network. Network was trained to learn its behavior by adjusting the connection strengths on every iteration. The conjugate gradient descent of each presented training pattern was calculated to identify the minima on the error surface for each training pattern. xperiments were performed by using one and two hidden layers and the results revealed that as the number of hidden layers is increased, a lower final mean square error is achieved in large number of epochsand the performance of the neural network was observed to be more accurate.
Institute of Scientific and Technical Information of China (English)
高述涛
2013-01-01
In order to improve the prediction accuracy of short time traffic flow,this paper proposes a network traffic prediction model based on Cuckoo Search algorithm and BP Neural Network(CS-BPNN).The time series of short time traffic flow is recon-structed to form a multidimensional time series based on chaotic theory,and then the time series are input into BP neural net-work to learn which parameters of BP neural network are optimized by cuckoo search algorithm to find the optimal parameters and establish the short time traffic flow prediction model.The performance of CS-BPNN is tested by the simulation experiments. The simulation results show that the proposed model improves the prediction accuracy of short time traffic flow and can more describe network traffic complex trend compared with reference models.% 为了提高短时交通流量的预测精度,提出一种布谷鸟搜索算法优化 BP 神经网络参数的短时交通流量预测模型(CS-BPNN)。基于混沌理论对短时交通流量时间序列进行相空间重构,将重构后的时间序列输入到 BP神经网络进行学习,采用布谷鸟搜索算法找到 BP 神经网络最优参数,建立短时交通流量预测模型,通过具体实例对 CS-BPNN 性能进行测试。仿真结果表明,相对于对比模型,CS-BPNN 提高了短时交通流量的预测精度,更加准确反映了短时交通流量的变化趋势。
Institute of Scientific and Technical Information of China (English)
莫东序
2011-01-01
In order to improve the effect of predicting GDP simply by using ARIMA model or BP neural network model,this essay uses the GDP of Guangxi from 1978 to 2008 as the sample,firstly establishes the ARIMA model to get the fitting error sequence and the initial forecast of the Guangxi GDP from 2009 to 2015,and then builds up the BP neural network for the error sequence to get the error predictive value from 2009 to 2015,finally uses the error predictive value to revise the initial predictive value to get the revised predictive value.The result shows that the application of ARIMA and BP Neural Network Mixture Model in the estimation of Guangxi＇s GDP is significantly better than the forecasts by single model.%为改进单纯使用ARIMA模型或BP神经网络模型对GDP预测的效果,笔者以1978—2008年的广西GDP为样本,首先建立ARIMA模型,得到拟合误差序列及2009—2015年的广西GDP的初始预测值,再对误差序列构建BP神经网络并得到2009—2015年的误差预测值,最后,用误差预测值对初始预测值进行修正,得到修正后的2009—2015年广西GDP的预测值。结果表明,ARIMA与BP神经网络混合模型的预测结果显著优于单一模型的预测。
Institute of Scientific and Technical Information of China (English)
郭亮; 陈维荣; 贾俊波; 韩明; 刘永浩
2011-01-01
针对光伏电池复杂难以建模的非线性特性,本文提出一种基于粒子群算法(PSO)的反向传播(BP)神经网络建模方法.神经网络具有很强的非线性拟合能力,但同时也存在收敛速度慢、容易陷入局部极值、建模精度不高等缺点.本文采用粒子群算法来优化神经网络的内部连接权值,以改善神经网络的性能,并基于这种改进的神经网络构建光伏电池动态模型.测试及仿真结果表明,通过此法建立的光佚电池模型辨识精度高,收敛速度快,取得了较好的效果.%In view of serious complexity and nonlinearity of photovoltaic array, its modeling is very difficult, therefore a Back Propagation (BP) Neural Network model based on Particle Swarm Optimization (PSO) algorithm is proposed. Neural Network has excellent nonlinear fitting ability, but it also has some shortcomings, such as low convergence speed, easy to fall into local optimal value, and low accuracy, etc. PSO algorithm is used to optimize the inner connection weights of Neural Network; therefore the performance of Neural Network is promoted. Modeling of photovoltaic array is based on this improved Neural Network. Test and simulation results showed the high identification accuracy and high convergence speed of this model. Key words: photovoltaic array; neural network; particle swarm optimization algorithm Constructing stable nodes to suppress common-mode EMI for power converters Abstract: In this paper, a simple approach to suppress common mode conducted electromagnetic interference (EMI) is proposed. The method is explained in detail with Boost converter adopted as a typical example. Most of common mode conducted EMI due to the high dv/dt nodes in the converter can be suppressed by constructing stable potential nodes ( dy/dr almost zero) in the circuit, which is implemented by changing placement of the boost inductor and using the common-anode diode instead of the common-cathode one. It is verified by
Chaotic diagonal recurrent neural network
Institute of Scientific and Technical Information of China (English)
Wang Xing-Yuan; Zhang Yi
2012-01-01
We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure andlearning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.
Institute of Scientific and Technical Information of China (English)
朱凤林; 韩卫
2013-01-01
On the basis of the nonlinear reflection ability of artificial neural network, we established three multi-layer feedforward neural network models in Matlab 7.1 simulation platform to monitor the Baishi reservoir deformation in Liaoning Province. The three models adopt different modified BP algorithms, i. e. LM algorithm, BR algorithm, and GDX algorithm. According to the fitting and prediction results, we compared the application results of the three models and concluded that the BP network based on LM algorithm was more suitable for building dam' s displacement monitoring model to realize real-time and effective monitoring.%基于人工神经网络的非线性映射能力,应用Matlab7.1网络仿真平台,结合辽宁省白石水库多年大坝位移实测数据,建立了3种不同改进BP算法的多层前馈神经网络模型.并通过LM算法、BR算法、GDX算法的BP网络模型的拟合、预报结果,对3种模型的应用效果进行了比较分析,得出了LM算法的BP网络更适合用于建立坝顶位移监控模型的结论,以实现对大坝位移实时、有效的监控.
Institute of Scientific and Technical Information of China (English)
江萍; 刘勇
2013-01-01
It is an important aspect to evaluate the ecological benefits of forestry projects. Using observed seasonal meteorological data of April, July and October (2010) in different density of Pinus tabulaeformis plantations in Yan-qing county, Beijing, we established the BP neural network model and MLR model for stereoscopic hydrothermal space of the forest edge-farmland and forest-farmland. The possibility of quantitatively evaluating and forecasting the farmland microclimate by these models were studied. The results showed; ( 1) The precision of forest edge-farmland BP neural network model was higher than that of forest-farmland BP neural network model in congregate microclimate-gradient (CMG) ;(2) The observed-simulated correlation (OSC) of forest edge-farmland BP model was higher than that of forest-farmland BP model during the whole growing season; while the higher OSC of forest edge-farmland MLR model only occurred in October, but forest-farmland MLR model had the higher OSC in April and July. (3) The precision order of these two types of forest edge-farmland model followed as July > October > April; The forest-farm- land BP model of density Ⅱ had the highest precision in April, but forest-farmland BP model of density Ⅰ was the highest in July and October. The precision of forest-farmland MLR model of density Ⅱ remained highest during the whole growing season. (4) The BP neural network model had high precision with few parameters, and was able to extrapolate when enough observed data were provided.%利用北京市延庆县不同密度抚育后林分、林缘和农田在2010年4月、7月及10月的季节性小气候监测数据,构建了林缘—农田和林内—农田的立体水热空间的BP定量预测模型和MLR模型,拟达到定量评价林业生态工程生态效益、预测农田小气候进而服务林业生产的目的.结果表明:(1)对于集合小气候环境梯度CMG,林缘—农田的BP模型预测精度整体高于林内—
Subbatch learning method for BP neural networks%BP神经网络子批量学习方法研究
Institute of Scientific and Technical Information of China (English)
刘威; 刘尚; 周璇
2016-01-01
针对浅层神经网络全批量学习收敛缓慢和单批量学习易受随机扰动的问题，借鉴深度神经网基于子批量的训练方法，提出了针对浅层神经网络的子批量学习方法和子批量学习参数优化配置方法。数值实验结果表明：浅层神经网络子批量学习方法是一种快速稳定的收敛算法，算法中批量和学习率等参数配置对于网络的收敛性、收敛时间和泛化能力有着重要的影响，学习参数经优化后可大幅缩短网络收敛迭代次数和训练时间，并提高网络分类准确率。%When solving problems in shallow neural networks, the full⁃batch learning method converges slowly and the single⁃batch learning method fluctuates easily. By referring to the subbatch training method for deep neural net⁃works, this paper proposes the subbatch learning method and the subbatch learning parameter optimization and allo⁃cation method for shallow neural networks. Experimental comparisons indicate that subbatch learning in shallow neural networks converges quickly and stably. The batch size and learning rate have significant impacts on the net convergence, convergence time, and generation ability. Selecting the optimal parameters can dramatically shorten the iteration time for convergence and the training time as well as improve the classification accuracy.
Institute of Scientific and Technical Information of China (English)
丁硕; 常晓恒; 巫庆辉
2014-01-01
Three common numerical optimization algorithms are first studied, including conjugate gradient algorithm, quasi-newton algorithm and LM algorithm. The three kinds of algorithms are used to improve BP neural network respectively and the corresponding classification models based on BP neural network are established. Then the models are used in pattern classification of two-dimensional vectors, and their generalization abilities are also tested. The classification results of different classification models based on BP network are compared with each other. Simulation results show that for small or medium scale networks, BP neural network improved by LM algorithm has the most precise classification result, the fastest convergence speed and the best classification ability. The one improved by conjugate gradient algorithm has the biggest error, slowest convergence speed and worst classification ability. While the classification precision, convergence speed and classification ability of quasi-newton algorithm lie between the above two algorithms.%研究了共轭梯度算法、拟牛顿算法、LM 算法三类常用的数值优化改进算法，基于这三类数值优化算法分别对BP神经网络进行改进，并构建了相应的BP神经网络分类模型，将构建的分类模型应用于二维向量模式的分类，并进行了泛化能力测试，将不同BP网络分类模型的分类结果进行对比。仿真结果表明，对于中小规模的网络而言， LM数值优化算法改进的BP网络的分类结果最为精确，收敛速度最快，分类性能最优；共轭梯度数值优化算法改进的BP网络的分类结果误差最大，收敛速度最慢，分类性能最差；拟牛顿数值优化算法改进的BP网络的分类结果误差值、收敛速度及分类性能介于上述两种算法之间。
The comprehensive evaluation of software quality based on LM-BP neural network%基于LM-BP神经网络的软件质量综合评价
Institute of Scientific and Technical Information of China (English)
郑鹏
2016-01-01
由于传统软件质量评价存在主观性等缺陷。针对这种情况，提出基于LM‐BP神经网络的软件质量综合评价方法。算法以ISO／IEC 9126为软件质量度量标准，解决了标准BP算法存在的问题，建立了LM-BP神经网络软件质量综合评价模型，为软件质量综合评价提供了一种新的方法。实验结果表明，LM-BP神经网络的软件质量综合评价能客观、定量、快速且准确得到软件质量综合评价结果，该评价模型具有客观性和实用性。%Because traditional software quality evaluation has some defects such as subjectivity , we proposed a method based on levenberg marquardt-back propagation(LM-BP) neural network software quality comprehensive evaluation .Based on ISO/IEC 9126 software quality model ,the algorithm solves the problems existing in the standard BP algorithm ,establishes the LM-BP neu‐ral network software quality comprehensive evaluation model ,and offers a new method for com‐prehensive evaluation of software quality .Experimental results show that the LM-BP neural net‐work software quality comprehensive evaluation is objective ,quantitative ,fast and accurate .The evaluation model is objective and practical .
Institute of Scientific and Technical Information of China (English)
马俊文
2009-01-01
The urban noise pollution influence people's life and work seriously,use the appropriate method to carry on the appraisal and forecast of the noise pollution, then proposed the effective prevention measures is the key question about noise pollution prevention, the article have selected the primary factors which influence the urban environment noise through the gray correlation analysis, used the Back propagation (BP) neural networks to carry on the appraisal and forecast of Beijing's urban environment noise from year 1994 to 2006,the confirmation result error was small, showed the BP neural network based on the gray correlation analysis is the effective way to appraise and forecast the urban environment noise pollution.%城市噪声污染严重影响着人们的生活与工作,采用合适的方法对噪声污染进行评价并预测,进而提出有效的预防及治理措施是噪声污染防治的关键问题,文章利用灰色关联分析选取了影响环境噪声的主要因素,采用BP(Back propagation,BP)神经网络对北京市1994～2006年的环境噪声污染进行评价并预测,验证结果误差较小,说明基于灰色关联的BP神经网络能够有效地对城市环境噪声污染进行评价和预测.
Institute of Scientific and Technical Information of China (English)
张永礼; 武建章
2015-01-01
At present , most technological innovation ability evaluation methods are established on the basis of the linear model , and the factors that affect the technological innovation capability are many , the multicollinearity may exist among variables . According to the above two reasons , the GA-BP neural network model was proposed in this paper . Genetic algorithm (GA) optimized the BP neural net-work model in the following aspects: ①neural network has the strong ability of dealing with nonlinear system . It avoided the disadvantages of the linear model . ②In order to remove the multicollinearity , the genetic algorithm was used to reduce evaluation index dimension . ③BP neural network used gradient descent algorithm that modified weights and thresholds , and it was easy to fall into local optimal solution . Genetic algorithm was introduced to search the BP neural network weights and thresholds in global scope . Finally , the technical innovation data of industrial enterprises above designated size in the 31 provinces , and cities were selected from year 2008 to 2013 , 124 of them are regard as training samples , others as testing samples . Empirical conclusion shows that forecast accuracy of GA -BP neural network is higher than BP neural network .%针对当前技术创新能力评价方法大多建立在线性模型的基础上，且技术创新能力影响因素较多，可能存在多重共线性的缺陷，本文提出了遗传算法优化的BP神经网络模型。GA－BP神经网络模型在以下几方面做出了改进：①利用了神经网络强大的非线性关系映射能力，避免了传统线性模型的缺陷。②利用遗传算法对评价指标进行了降维，去除了多重共线性。③使用遗传算法从全局搜寻BP神经网络权值和阀值向量，优化了BP神经网络模型，避免了BP神经网络由于使用梯度下降算法，容易陷入局部最优解的缺陷。本文最后选取2008～2013年全国31个省市规模
Optimizing neural network forecast by immune algorithm
Institute of Scientific and Technical Information of China (English)
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
2006-01-01
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
Recognition of Continuous Digits by Quantum Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper describes a new kind of neural network-Quantum Neural Network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15 percent error reduction is achieved on a speaker-independent continuous digits recognition task compared with BP networks.
Institute of Scientific and Technical Information of China (English)
林虹江; 周步祥; 冉伊; 詹长杰; 杨昶宇
2015-01-01
In the constant pressure control method , there is a big error when the BP neural network is adopted to pre-dict the voltage at the maximum power point .In view of this problem , the genetic algorithm was proposed in this paper to optimize the BP neural network , and then the optimized algorithm was used to predict the voltage at the maximum power point ofthe photovoltaic system and this value was substituted for the constant voltage parameter of the MPPT control algorithm for the photovoltaic power generation system based on constant voltage .At the same time , in combi-nation with the constant voltage control method , a simulation model of the improved constant voltage photovoltaic sys-tem MPPT control based on the GA-BP neural network learning algorithm was built .Finally the simulation results of examples proved that the proposed photovoltaic system MPPT control algorithm based on GA-BPNN could track down the photovoltaic maximum power point quickly and accurately , and compared with the BP neural network algorithm , the perturbation and observation method and the FUZZY control algorithm , the MPPT control algorithm had better sta-bility and higher precision .%针对恒压控制法中采用BP神经网络预测最大功率点处电压存在较大误差的情况，提出了用遗传算法来优化BP神经网络，然后用优化后的算法来预测光伏系统最大功率点之处的电压，并以此值代替基于恒电压的光伏发电系统MPPT控制算法中的恒电压参数；同时结合恒电压控制法建立了基于GA-BP神经网络学习算法的改进恒压型光伏系统MPPT控制的仿真模型。最后算例仿真结果证明所提的基于GA-BPNN的光伏系统MPPT控制算法能够快速准确地进行光伏最大功率点跟踪，并且相比于 BP 神经网络算法、干扰观察法及FUZZY控制算法其稳定性更好、精度更高。
Institute of Scientific and Technical Information of China (English)
2015-01-01
针对金融市场的非线性特征，将BP神经网络与符号时间序列分析方法相结合，利用历史数据对金融波动进行预测。采用上证综指2011—2014年间隔为5 min的高频数据为样本，首先将波动序列符号化，然后利用BP神经网络对样本进行训练和检验，检验结果表明，该方法可有效预测下一时点波动变化情况，达到了其在金融波动方面的预测效果。%According to the nonlinear characteristics of the financial market, a new method of BP neural network combined with symbolic time series analysis was put forward to forecast financial volatility. High frequency data whose sampling intervals were 5 minutes from Shanghai Stock Exchange Composite Index from 2011 to 2014 was used as a sample. Firstly, the volatility series need to be symbolized. Then BP neural network was used to train and test the samples. Finally the symbol value of next time point was effectively predicted. The effect of the method of forecasting financial volatility was proved.
Institute of Scientific and Technical Information of China (English)
陈丽娟; 余隋怀; 初建杰; 卢慧颖
2011-01-01
在分析了影响维护人员素质的因素后提出了基于BP神经网络对维护人员综合素质进行评估的方法.利用BP神经网络建立了维护人员综合素质的评价模型,并通过使用改进算法进行了训练.训练测试的结果表明,该方法具有较高的可行性和有效性,为全面评估维护人员的素质提供了一种方法.%The factors that affect the equipment maintenance personnel are comprehensively analyzed, and a method is proposed to evaluate comprehensive quality of equipment maintenance personnel based on BP neural network.Evaluation model of equipment maintenance personnel is established by using BP neural network, and the improved algorithm is used for training.Training test results show that this method has a high feasibility and effectiveness, and provides a way to assess the quality of equipment maintenance personnel comprehensively.
Institute of Scientific and Technical Information of China (English)
郑玉军; 田康生; 邢晓楠; 丰坤
2016-01-01
运用动态静态结合的方法以及厂家测试数据、查阅外军资料等手段获得多功能相控阵雷达指标参数，采用BP神经网络建立威力评估模型。研究表明：所建立的BP神经网络多功能相控阵雷达威力评估模型具有很高的评估精度，可以很好地反映雷达二级指标和雷达威力之间复杂的非线性关系，为多功能相控阵雷达威力评估提供了一种准确有效的方法。%The multifunctional array radar indexes are achieved with dynamic and static method, test data of manufacturers,as well as foreign materials,and power evaluation model is established with BP neural network. According to the research,the established multifunctional array radar power evaluation model of BP neural network has high evaluation prevision,and it can reflect the complicated nonlinear relationship between the second-level index and power of radar,which offers a more accurate and effective method for the power evaluation of multifunctional array radar.
Institute of Scientific and Technical Information of China (English)
郭琼霞; 黄娴; 黄振
2012-01-01
利用GIS技术和BP神经网络模型开展假高粱适生性气候分布预测.根据假高粱对气候条件的要求,确定假高粱的适生性气候区划指标；利用全国570个气象台站的气候资料和1∶25万地形数据,通过BP神经网络的仿真预测,对78个站点的空缺区划指标因子数据进行插值；采用GIS技术划分出假高粱适生区、潜在适生区和非适生区.%Basd on CIS and BP neural network, the suitable climatic distribution of Sorghum halepense was predicted. According to the climatic condition for Sorghum halepenae, the fitness index for suitable division of 5. Halepense was determined; using the climatic data of 570 national weather stations and the landform data on the scale of 1:250000, by the prediction of BP neural network, the interpolation for the absent data of 78 weather stations was accomplished; by the method of CIS, the Johnson grass's suitable district, potential suitable district and unsuitable establishment area were divided.
Institute of Scientific and Technical Information of China (English)
赵晨飞; 韩卿; 兀旦晖
2012-01-01
计算机配色可提高油墨配色的速度和精度,具有重要作用.基于两种颜色空间和BP神经网络的计算机配色系统被研究,实验结果表明基于光谱颜色空间配色系统的配色精度更高,可应用于目前的各种印刷方式中,基于BP神经网络的配色系统具有很强的适用性,推进了印刷的数字化流程.%Computer color matching can improve the ink s color matching speed and accuracy, which plays an important role. In this paper the two kinds of computer color matching system based on two color spaces and BP neural network are disscussed. The experimental results show that the color matching system based on the spectral color space is of higher accuracy which can be applied to the current various printing way. The color matching system based on BP neural network has strong applicability, can improve the printing's digital workflow.
Institute of Scientific and Technical Information of China (English)
张成凤; 徐长生
2012-01-01
For the structure design and calculation of floating crane arm, BP neural network algorithm is used to simulate the relations between the arm frame structure optimization design variables and arms frame structure stress and displacement, and calling the minimize function of MATLAB optimal toolbox to optimized the arm constraint conditions and the objective function. Comparing and analyzing the result after optimization and the values before optimization, the BP neural network optimization algorithm is fully verified.%针对起重船臂架结构设计计算,采用BP神经网络算法模拟出臂架结构优化设计变量与臂架结构应力、位移之间的映射关系,并调用MATLAB优化工具箱中的最小化函数对臂架结构的约束条件以及目标函数进行优化处理,并将优化后的结果与优化之前的数值进行对比分析,充分地验证了BP神经网络优化算法的优越性.
Research on Risk Assessment of Software Project Based on BP Neural Network%BP神经网络在软件项目风险评估中的应用
Institute of Scientific and Technical Information of China (English)
李华; 曹晓龙; 成江荣
2011-01-01
Dealing with software project risk assessment. The traditional evaluation method uses qualitative and quantitative evaluation method, and the evaluation results is charging with the influence of subjective assessment. In order to eliminate subjective factors in the project risk assessment, in this paper, we proposed the BP neural network software project risk assessment model. Firstly, building a software project risk assessment index system; secondly, according to the evaluation system of software project assessment, building the BP neural network structure. Finally, training with the MATLAB neural network tools and testing the test data, the BP neural network software project risk assessment model is proved feasible and effective in risk assessment, which has broad application prospect.%关于准确地识别软件风险因素,深入研究软件项目风险评估问题,由于软件项目的复杂性和软件风险因子的不确定性和模糊性,无法采用传统数学方法建立准确软件项目风险评估模型.由于传统的数学评估模型的评估准确率比较低,为了提高软件项目评估准确率,提出一种BP神经网络的软件项目风险评估方法.软件项目风险评估方法采用专家系统构建软件项目风险评估指标体系,后对评估体系进行预处理,消除评估体系之间重复和无用的信息,并将非线性学习能力优异的BP神经网络输入,通过BP神经网络自适应学习得到的最优软件项目评估模型,在MATLAB平台上进行验证性仿真.结果表明,算法提高了软件项目风险评估的准确率,克服了传统数学评估模型的缺陷,评估的结果更具科学性,在软件项目风险评估中提供了有效的方法.
基于BP神经网络的我国农民收入预测模型%A Prediction Model of Farmers' Income in China Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭庆春; 何振芳; 李力; 孔令军; 张小永; 寇立群
2011-01-01
According to the related data affecting the farmers' income in China in the years 1978 -2008, a total of 13 indices are selected, such as agricultural population, output value of primary industry, and rural employees. According to standardized method and BP neural network method, the farmers' income and the artificial neural network model are established and analyzed. Results show that the simulation value agrees well with the real value; the neural network model with improved BP algorithm has high prediction accuracy, rapid convergence rate and good generalization ability. Finally, suggestions are put forward to increase the farmers' income, such as promoting the process of urbanization , developing small and medium - sized enterprises in rural areas, encouraging intensive operation, and strengthening the rural infrastructure and agricultural science and technology input.%依据1978～2008年影响我国农民收入因素的相关数据,选取从事农业的人口、第一产业产值、乡村就业人员数等13个指标,依据标准化方法和BP神经网络方法,建立了关于农民收入的人工神经网络模型,并进行具体分析.结果表明,模拟值与真实值吻合较好,改进BP算法的神经网络模型预测精度高,收敛速度快,具有良好的泛化能力.在此基础上,提出了增加农民收入的建议:一是推进城镇化进程;二是发展农村中小企业;三是鼓励集约经营;四是加强农村基础设施建设和农业科技投入.
Institute of Scientific and Technical Information of China (English)
南敬昌; 任建伟; 张玉梅
2011-01-01
Neural network can simulate random nonlinear system. In view of nonlinearity and memory effect of the RF power amplifier, a behavioral model for RF power amplifier based on PSO_BP neural network was established. The proposed model was simulated using input and output data extracted from Freescale's semiconductor transistor MRF6S21140 model and designed circuit in ADS circumstance, and fitting curve of output voltage amplitude and root mean square error (RMSE) were obtained, which was compared with that of the BP neural network model. Results showed that the proposed model had high precision and better approaching capability, so it can simulate the characteristics of power amplifier accurately, and therefore it is useful for construction of system simulation.%神经网络具有模拟任意非线性系统的优势.考虑到射频功放的非线性和记忆效应,在BP神经网络模型的基础上,提出一种基于PSO的BP神经网络射频功放行为模型.利用飞思卡尔(Freescale)半导体晶体管MRF6S21140器件模型及设计的电路,从ADS中导出输入输出数据,对模型进行了仿真实现,得出输出电压幅度的拟合曲线以及均方根误差,并与BP神经网络模型进行比较.仿真结果表明,所提模型具有较高的精度和较好的逼近能力,可以精确模拟功率放大器的特性,对系统仿真的构建具有重要的应用价值.
Institute of Scientific and Technical Information of China (English)
刘继清; 徐明
2011-01-01
In view of the weakness of current intrusion detection system, a new intrusion detection system model based on the combination of KPCA technology and BP Neural Network is put forward. Against the high dimensions problem of complicated network data, KPCA technology as a method of characteristics extraction is used to decrease the dimensions and simplifie the size of neutral network and reduces the operations work. A large a-mount of experiments with KDD99 dataset have been conducted and the results show that the new system is with higher adaptable ability and higher speed detection rate in nowadays complicated network circumstances than the intrusion detection system only uses BP neural network.%针对当前入侵检测系统的弱点,将KPCA技术和BP神经网络相结合,提出了一种多核入侵检测分类系统的设想.该系统针对一些复杂网络数据维数较高的特点,引入核主成分分析技术对其进行降维处理,从而简化了神经网络规模,降低了神经网络的运算量.通过对KDD99数据集进行仿真实验表明,与仅使用BP神经网络的入侵检测系统相比,该系统具有很强的泛化能力和较高的检测效率.
Application of BP Neural Network in Distortion Correction of Large FOV Display%BP神经网络用于大视场显示设备的畸变校正
Institute of Scientific and Technical Information of China (English)
田立坤; 刘晓宏; 李洁
2012-01-01
Geometric distortion may appear in large Field-of-View ( FOV) electro-optic image display equipment, which is caused by the optical system. To improve image distortion correction effect and overcome the limitations of the traditional BP algorithm, such as local minimum and slow convergence speed, Levenberg-Marquardt algorithm based on optimizing theory was used. Then the distortion correction method based on BP neural network containing two hidden layers was proposed, which could achieve high precision of mapping between distortion image and original image self-adaptively without knowing the mathematic model. The algorithms were analyzed and compared in depth in the Matlab platform. The simulation result shows that the BP neural network algorithm with double hidden layers can be realized easily, achieve high precision, and has good data processing ability. Compared with the distortion correction model based on polynomial fitting method, all the precision indexes of the distortion correction model based on BP neural network with double hidden layers are improved observably.%大视场光电成像显示设备中会出现光学系统引起的图像几何畸变现象.为了提高显示设备畸变校正效果,并克服传统BP算法存在局部极小点、收敛速度慢等缺点,采用了基于优化理论的LM算法来改进传统BP神经网络算法.提出一种含有双层隐含层的BP神经网络畸变校正方法,可在不确知畸变数学模型情况下,实现自适应地建立畸变图像与原始图像之间的高精度映射关系.在Matlab平台上进行算法的深入分析和比较.仿真结果表明,双隐含层BP神经网络算法易于实现、数据处理能力强、校正精度高.与多项式拟合方法的畸变校正模型相比,基于双隐含层BP神经网络算法的畸变校正模型的各项精度指标提升显著.
Institute of Scientific and Technical Information of China (English)
Xiao-rui Wang; Yuan-han Wang; Xiao-feng Jia
2009-01-01
Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and, mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively unproved the recommended values in the original prospecting data. This is of
Institute of Scientific and Technical Information of China (English)
李净; 冯姣姣; 王卫东; 张福存
2016-01-01
(Levenberg-Marquardt) algorithm is used to optimize the BP neural network (LM-BP neural network is abbre-viation of BP neural network for the optimization). This article simulates solar radiation using LM-BP neural network, H-S and A-P climate models at Urumqi, Kashi, Hami, Xining and Guyuan radiation stations and uses MPE, MBE and RMSE indexes of accuracy assessment to test the three models. The results indicate that LM-BP neural network has the highest accuracy in model simulations, showing satisfactory performance com-pared with the simulation results of traditional two climate models, simulated and observed values of fitting de-gree model is superior to H-S and A-P climate models. So we selects the LM-BP neural network model to simu-late solar radiation in Northwest China. Basing on the meteorological data from 159 weather stations in North-west, we apply the BP neural network optimized LM (Levenberg-Marquardt) algorithm to simulate the total month solar radiation during 1990-2012 in these meteorological observation stations. Then the solar radiation value of the 159 weather stations and the measured radiation data of the 25 radiation observation station to ob-tain the spatial-temporal distribution of annual average solar radiation by interpolation, and analyzes. These re-sults indicate that average annual total radiation in 1990-2012 in Northwestern ranges from 262 MJ/m2 to 643 MJ/m2, presenting the distribution pattern of high in the middle, low on both end. LM neural network is a prom-ising method for solar radiation simulation, which can be used in the simulation of solar radiation in the area of no radiation observation.
Institute of Scientific and Technical Information of China (English)
简晓春; 王利伟; 闵峰
2012-01-01
为实现对汽车排放污染物CO的实时检测，提出采用神经网络软测量技术，以BP神经网络基本原理为基础，引入LM优化算法。选用发动机运转参数中的转速和节气门开度为变量，建立面向LMBP神经网络的汽车排放污染物CO的检测模型，并对该神经网络进行学习训练和模拟验证。结果表明：该方法可行、有效，仿真结果非常接近实测数据，且LMBP算法收敛速度快、预测精度高。同时，也可将该神经网络模型应用于CO的实时控制中，提高控制的实时性和精度。%To realize real-time detection of the CO emission of Vehicle, the article put forward using neural network soft measurement, based on BP neural network basic principle, and brought in LM optimization algorithm. The engine running parameters： the rotation speed and the throttle percentage were chosen as variables; LMBP neural network detection model for the CO emission of Vehicle was estabished, and the neural network was trained and simulated. The results showed that this method was feasible and effective. The simulation results were very close to the measured data, and the convergence speed and the forecast precision of LMBP algorithm was high. In the meantime, it could also be applied in the real-time control of CO,which improved the instantaneity of control and the precision.
Research and Application of Rough Set-BP Neural Network Based on MEA%基于MEA的粗糙集神经网络研究及应用
Institute of Scientific and Technical Information of China (English)
高金兰; 高骞
2011-01-01
将思维进化算法、粗糙集和神经网络相结合,提出一种基于MEA的粗糙集神经网络,用于变压器故障诊断.此模型采用思维进化算法全局寻优的特点,搜索粗糙集属性约简离散断点的位置以及神经网络的连接权值和阈值,避免了常规粗糙集属性约简时复杂的手工试凑以及BP神经网络收敛速度慢、精度不高等缺点,有利于更快地收敛于全局最优解,提高系统的诊断速度和准确率.仿真结果表明了方法的有效性.%The mind evolutionary algorithm is combined, the rough set and the neural network, and a rough set-neural network based on MEA is proposed applying in transformer fault diagnosis. This model uses global optimization characteristics of the mind evolutionary algorithm to search rough set discrete breakpoints and neural network connection weights and thresholds, it avoids the conventional rough set complex handwork reduction and slow convergence and low precision of BP neural network, and benefits to find the global optimal solution quickly and improves the diagnostic speed and accuracy. Simulation experiment verifies the validity of this method.
Soil Suitability Evaluation Based on BP Neural Network%基于BP神经网络的土壤适宜性评价——以溪洛渡水电站嘎勒移民安置区为例
Institute of Scientific and Technical Information of China (English)
陈琨; 赵小蓉; 王昌全; 黄萍萍; 赵燮京
2009-01-01
人工神经网络具有大规模并行处理、分布式储存、自适应性、容错性等特点,可以解决复杂的非线性问题.本文将BP人工神经网络应用到溪洛渡水电站嘎勒移民安置区土壤适宜性评价中,构建了影响土壤适宜性的评价因子训练集,对隐层神经元数量的选择、训练过程的建立等问题进行了探讨.通过MATLAB神经网络工具箱对专家样本的学习,建立具有泛化能力的土壤适宜性评价BP神经网络模型,确定网络模型结构为9-7-1,均方误差为0.00033,并对预测地块进行评价,得出评价区域以中等适宜性的土壤为主的结果.%Artificial neural network with the characteristics of massively parallel processing,distribuion storage,self-adaptive,fault tolerance and etc.,could be used to solve complex nonlinear problems.Therefore,BP artificial neural network was applied for soil suitability assessment,the impact on soil suitability to build evaluation factors,the training set,and the number of hidden layer neurons in the choice of the establishment of the training process and other issues were discussed. Through the neural network study of samples,the generalization ability of neural network model was established to evaluate the prediction block obtained in line with the actual results of the evaluation.
Neural Networks: Implementations and Applications
Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.
1996-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
基于BP神经网络的变压器内部故障保护%Internal fault protection based on BP neural network transformer
Institute of Scientific and Technical Information of China (English)
苏美玲; 邹晓松; 何杰
2016-01-01
本文研究了基于BP神经网络方法的变压器内部故障保护。运用MATLAB/SUMILINK对变压器励磁涌流、励磁涌流与故障电流的差异进行了数字仿真。利用MATLAB的人工神经网络工具箱，建立了BP神经网络模型，对励磁涌流和故障电流的样本进行训练及测试并对训练好的网络进行验证。表明BP神经网络可以较为正确地区分励磁涌流和故障电流，用于变压器内部故障保护。%This paper discussed a transformer protection based on Back- Propagation Network .Digital simulation were made on the inrush current of the transformer and on the comparison between the inrush current and the fault current of the transformer. Back- Propagation Network model was set up by using the MATLAB artificial neural network toolbox. The results show that the Back- Propagation Network almost can correctly distinguish between excitation inrush current and the fault current of the transformer.
Institute of Scientific and Technical Information of China (English)
方石; 张坚; 陈朝宏
2011-01-01
Taking aim at the low SNR in detecting magnetic signal of ship, a detection algorithm based on wavelet transform and BP neural network was proposed. Based on the characteristic of magnetic signal of ship, firstly the signal was decomposed by wavelet transform, and the low frequency components in last level were taken out to filter out the high frequency noise. Then the low frequency components were processed by BP neural network to pick up characteristic signal of ship target. The results of experiment by ship model showed that the algorithm increases SNR markedly, and enhances the detection ability of magnetic signal of ship.%针对水中兵器探测舰船磁场信号时信噪比较低的问题,提出了一种小波变换结合反向传播(backpropagation,BP)神经网络的检测方法.根据舰船磁场信号的时频特征,首先对信号进行小波分解,提取最后一层的低频分量,滤除高频噪声;再采用BP神经网络对低频分量进行学习,提取舰船目标特征信号.将此算法应用于船模实测实验,结果表明,该算法可以显著提高信噪比,增强了对舰船磁场信号的检测能力.
Remote Sensing Image Segmentation with Probabilistic Neural Networks
Institute of Scientific and Technical Information of China (English)
LIU Gang
2005-01-01
This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.
Institute of Scientific and Technical Information of China (English)
冯楠; 王振臣; 胖莹
2011-01-01
为了对纯电动汽车的电池剩余电量进行准确的预测,在分析了影响电池剩余容量的多种因素后,应用了BP神经网络建立了电池模型,并应用遗传算法对其权值阈值进行了优化.最后,用MATLAB编写了仿真程序进行了多组数据的测试,并与纯BP网络进行了对比,结果表明,优化后的网络具有训练时间短,精度高的特点,对电池容量的预测是有效的.%For predicting the state of charge (SOC) of pure electric car battery precisely, BP neural network was adopted to predict the state of charge of battery, to create the model and to utilize GA to optimize its weights and bias, analyzing many factors that affecting the battery residual capacity. Finally, the emulation program written by MATLAB multiple sets of data were tested and compared with pure BP network. The results show that the optimized network has a short training time and high accuracy, and the prediction of the battery capacity is effective.
Layered learning of soccer robot based on artificial neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.
Institute of Scientific and Technical Information of China (English)
马草原; 孙富华; 朱蓓蓓; 尹志超
2015-01-01
For current tracking control problems in active power filter (APF), a BP neural network adaptive PI controller based on improved gradient algorithm is designed. It combines the neural network technology with PI controller structure. Compared with the traditional PI controller, it has a simple structure, and easy to on-line adjustment. Meanwhile, in order to overcome the local minimum and slow convergence problem when using neural network algorithm to weight correction coefficient, the gradient algorithm is improved and the algebraic method instead of gradient descent method is used to solve the problem of the local minimum arise, and makes convergence faster. Simulation experiments show that the improved adaptive neural network PI controller has faster response and higher compensation accuracy, thus to make the system more stable, and the harmonic distortion of grid current is lower.%针对有源电力滤波器的电流跟踪控制问题，设计了一种基于改进梯度算法的BP神经网络自适应PI控制器。该控制器将神经网络技术与PI参数设计相结合，与传统的PI控制器相比，该控制器具有结构简单、易于在线调整等优点。同时，为了克服采用神经网络算法修正权值系数时，会存在局部极小、收敛速度慢的问题，对 BP 神经网络采用的梯度算法进行改进。利用代数法代替梯度下降法，从而解决了易出现局部极小问题，且使收敛速度更快。仿真实验表明，改进后的神经网络自适应PI控制器较传统的PI控制器有更快的响应速度和更高的补偿精度，从而使系统更稳定，而且电网电流的谐波畸变率更低。
Institute of Scientific and Technical Information of China (English)
陈桂; 陈耀忠; 林健; 温秀兰
2014-01-01
针对采用传统反向传播( BP)神经网络算法进行逆运动学求解收敛速度慢的问题，提出将微分进化( DE)与粒子群优化( PSO)算法相结合，对用于机器人逆运动学求解的BP神经网络进行优化。基于机器人正解映射建立优化算法的目标函数，在PSO过程中，引入DE操作优化粒子进化方向，并将此混合算法用于BP神经网络权值与阈值的优化。对KUKA机器人进行仿真实验，结果表明：采用该文方法对机器人逆运动学问题的求解精度高，求得的关节角度误差小于0.1°；逆运动学求解结果所对应位姿矩阵的位置误差在0.1 mm数量级，具有较好的泛化能力。该文方法满足机器人位置和姿态方面的精度要求。%Aiming at the problem of slow convergence speed of traditional back propagation ( BP ) neural network algorithms, differential evolution ( DE ) and particle swarm optimization ( PSO ) are combined to optimize BP neural network for robot inverse kinematics. An objective function of the op-timization algorithm is formulated based on the mapping of robot forward kinematics. DE operation is employed to optimize particle evolution direction in PSO,and the weights and thresholds of the BP neural network are optimized. A simulation experiment is proposed for a KUKA robot,and the result shows that:the solution accuracy of robot inverse kinematics of the algorithm proposed here is high, and the joint angle error is below 0 . 1 °;the position error between the initial pose matrix of the robot and that solved by the algorithm proposed here is of the order of magnitude of 0. 1 mm,and has good generalization ability. The algorithm proposed here satisfies the accuracy requirements of robot locations and postures.
Institute of Scientific and Technical Information of China (English)
谢军华; 刘知贵; 任立学; 张活力
2012-01-01
The paper presents feature parameter analysis and processing in fission time-dependent signal of induced uranium components based on BP-Neural Networks through the analysis of the measuring princi- ple and signal characteristics of induced uranium components fission signal. The auto correlation functions and cross correlation functions are calculated by using unbiased estimate, and then the feature parameters of fission signal in different status are extracted by using feature abstraction method, comparative method and derivative method, and then applied to training and prediction by means of BP-neural networks based on pattern recognition. Theoretical analysis and the results show that, it is effective to obtain feature pa- rameters of induced uranium component fission signal via comparative method and derivative method. Using BP neural network to tiveness and reasonability of recognize patter of fission signal, we got good results that verified the effec the method.%在对诱发铀部件裂变信号的测量原理及特点分析的基础上,开展了基于BP神经网络的诱发铀部件裂变时间关联信号特征参量分析处理的研究工作。采用无偏估计方法,计算信号的自相关函数和互相关函数,再利用比较法和导数法两种特征量提取方法,提取出不同状态下裂变信号的特征参量,借助于BP神经网络模式识别应用原理进行训练和预测。理论分析和研究结果表明：基于比较法和导数法获得的特征参量能较好地反映诱发铀部件裂变信号的特征;用BP神经网络对裂变信号进行模式识别,取得了较高的正确率,验证了此方法的有效性和合理性。
Study of Traffic Accident Prediction Method Based on BP Neural Network%基于BP神经网络的交通事故预测方法研究
Institute of Scientific and Technical Information of China (English)
唐阳山; 葛丽娜; 黄子龙; 杨培菲
2016-01-01
在分析道路交通事故影响因素的基础上，运用主成分分析法确定交通事故的9个主要影响指标，并以这9个指标为输入，以事故数、死亡人数、受伤人数及经济损失作为输出，建立交通事故BP神经网络模型。以1991-2011年的数据作为训练样本，2012年的数据作为检验样本，选取不同的训练函数，用matlab对网络进行训练，并对预测结果进行比较分析，结果表明BP神经网络对交通事故预测精度较高，且训练函数对预测精度有较大影响。%On the basis of analyzing the factors of road traffic accidents, the principal component analysis (PCA) to determine the nine main impact indicators of traffic accidents is used, and nine indicators taken as input, the number of injured, deaths, and economic loss in accidents as output, the BP neural network model is established. To take the data from 1991 to 2011 as the training sample, and the data of 2012 as the test sample, different training functions to train network are chosen through matlab and the predicted results are analyzed and compared, the results show that the BP neural network for traffic accident prediction accuracy is higher, and training function has a great influence on training function of prediction accuracy.
Institute of Scientific and Technical Information of China (English)
戴洪磊; 韩李涛; 陈传法
2012-01-01
矿山瓦斯突出与爆炸事故的预测预报是当前我国煤矿安全生产中急待解决的问题之一。引入BP神经网络的拟牛顿（Newton）优化算法,在保留空间实体相关和多种分布并存的前提下,讨论了建立拟牛顿优化算法BP神经网络瓦斯灾害预测预报模型的数学模型设计、网络结构设计和程序设计3个部分,并以济宁二号井为实例进行了测试。结果表明：该模型稳定、快速、预测精度高,能够较好地模拟矿山瓦斯突出与爆炸事故特征,对瓦斯灾害作出较准确的预测。%The forecasting of gas outburst and explosion accidents is one of the most pressing problem in current China's coal mine safety production.Introducing the Quasi-Newton optimization algorithm of BP neural network,this paper discusses the mathematical model,network architecture and programming design of establishing the gas disaster forecasting model of Quasi-Newton optimization algorithm BP neural network under the premise of keeping the relationship among the spatial entities and their distributions,and an instance of Jining No.2 coal mine is tested.The result shows that this model is stable,fast and high prediction accuracy,which can simulate the mine gas outburst and explosion accidents characteristics and make more accurate predictions on the gas disaster.
Institute of Scientific and Technical Information of China (English)
罗广恩; 崔维成
2012-01-01
Artificial neural network is an important method for predicting the fatigue crack growth rate. In this paper, the Bayesian regularized BP neural network is established to predict the fatigue crack growth rate of metal.The experimental data of each material at different stress ratio R are divided into two parts. One is used for training neural network, the other is used for testing the network. Experimental data of four different types of materials taken from literature were used in the analyses. The results show that the neural network has strong fitting and generalization capability. And the generalization capability of neural network is improved by reducing the training data near the threshold.So the neural network can be used for predicting the crack growth rate of different stress ratios R based on the existing data. Furthermore, it will provide a reliable and useful predictor for fatigue crack growth rate of different metals.%人工神经网络是进行预报裂纹扩展率的一个重要方法.文章针对不同金属的疲劳裂纹扩展速率分别建立贝叶斯正则化BP( Back Propagation)神经网络,将各材料在不同应力比R下的疲劳裂纹扩展速率试验数据分为两部分,一部分用来进行训练网络,另一部分用来测试训练好的网络,检验其泛化能力.将从文献中获取的4种不同金属材料的疲劳试验数据作为算例,来检验网络的性能.计算结果表明贝叶斯正则化BP神经网络不仅对训练样本有很好的拟合能力,而且对于未训练过的测试样本也有较好的预测能力,即有较强的泛化能力.同时,指出了建立网络时减少门槛值附近的试验样本点,可以提高网络的预测能力.研究结果表明,该方法可以方便地获得不同应力比R下的疲劳裂纹扩展速率,从而达到减少试验次数,充分利用已有数据的目的.并且可以进一步应用于其他金属的疲劳裂纹扩展速率的预报.
DEFF Research Database (Denmark)
Krogh, Anders Stærmose; Riis, Søren Kamaric
1999-01-01
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...
Institute of Scientific and Technical Information of China (English)
邱忠超; 张卫民; 果艳; 刘金; 成明明
2016-01-01
The basic principle of realizing quantitative evaluation of metal micro crack detection with using BP neural network optimized by genetic algorithm is introduced.Organic combination of genetic algorithm and artificial neural network not only improves global search performance,but also maintains good adaptability to nonlinear problems during magnetic flux leakage detection.Final experimental results show that the artificial intelligence algorithm applied in practical engineering can realize quantitative assessment of metal micro cracks based on magnetic leakage signals.%介绍了利用遗传算法优化 BP 神经网络，实现金属中微细裂纹漏磁检测定量化评价的基本原理。将遗传算法和人工神经网络有机结合，进行漏磁定量化检测，既提高了算法的全局搜索性，又良好地适应于非线性问题。试验结果表明，将该人工智能算法应用于工程实际，能有效实现基于漏磁检测信号的金属中微细裂纹定量化评价。
Neural networks for aircraft control
Linse, Dennis
1990-01-01
Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.
Institute of Scientific and Technical Information of China (English)
肖劲飞; 侯媛彬; 王瑞; 王勉华
2015-01-01
Aiming at the problems that the current amplitude and copper consumption of state stator is relatively large in the traditional fixed flux DTC speed control system with double closed-loop, a variable flux DTC speed control system with three closed-loops is put forward in this paper. The simulation results indicate that the torque ripple, current amplitude and copper consumption of SRM are obviously reduced by using this speed control system. Furthermore, aiming at the features that the control parameters of the variable flux DTC speed control system with three closed-loops are complex and the requirement for real time is high, this paper combines BP neural network PID controller and the traditional PI controller has been composited with the consideration of characteristics of this variable flux DTC speed control system, and the BP-PI controller are used as the rate fixer in this paper. The simulation results shows that this composited controllers can overcome the defects of single neural network PID controller with traditional PI controller to establish BP-PI controller. The simulation shows that the BP-PI controller effectively overcomes the defects in single BP network PID controller, has better dynamic and static performance than those of traditional PI controller, and obviously improves the adaptability and robustness of this control system.%文章针对传统定磁链双闭环DTC调速系统稳态时定子电流幅值较大、电机铜耗增加的问题，提出了变磁链给定的解决方案，建立了变磁链三闭环DTC调速系统。仿真结果证明，此调速系统动、静态性能良好，既能充分抑制SRM的转矩脉动，又能在稳态时降低定子电流的幅值，解决了电机高速时铜耗较大的问题。另外，针对变磁链三闭环DTC调速系统控制参数复杂，实时性要求高的特点，文章将BP神经网络PID控制器与传统PI控制器复合，构成了BP-PI控制器。仿真表明，BP-PI控制器有效克
DEFF Research Database (Denmark)
Hansen, Lars Kai; Salamon, Peter
1990-01-01
We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....
Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks
Institute of Scientific and Technical Information of China (English)
费翔; 何小燕; 罗军舟; 吴介一; 顾冠群
2000-01-01
Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.
Institute of Scientific and Technical Information of China (English)
王冬
2015-01-01
In this paper, on the basis of the BP neural network algorithm and the MATLAB software, we had an empirical analysis of the operational risks of the logistics enterprises, and found that the algorithm was of good accuracy and capable of effectively improving the efficiency of the operation and management of the logistics enterprises.%基于BP神经网络算法和MATLAB软件，对物流企业经营风险进行实证分析。结果显示，BP神经网络算法具有很好的预测精度，能有效地提高物流企业经营管理的效率。
基于BP神经网络的企业应急物流风险管理%Risk Management for Enterprise Emergency Logistics Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
张杰; 汤齐
2012-01-01
在客观分析企业应急物流风险的前提上,基于MATLAB工具箱--BP神经网络提出有效的评价方法,从应急物流的角度来评价突发事件的风险大小;同时建立了风险预警模型,最后提出对应的风险控制策略,为企业顺利应对突发事件提供行之有效的参考依据.%In this paper, we proposed an effective evaluation method of enterprise emergency logistics risks based on BP neural network, meanwhile established the corresponding risk warning system and finally gave the risk control strategy.
Institute of Scientific and Technical Information of China (English)
胡洪安; 孙要伟
2012-01-01
In this paper, combined with the existing human resource assessment model, we analyzed the characteristics of college human resources and provided a modified method of BP neural network to analyze university human resource value, This provide a theoretical support and practical information for human resources assessment of the college.%分析了高校人力资源的特点，结合已有的人力资源评估模型，提出了用改进的BP神经网络的方法对高校人力资源进行定量分析的算法，为高校人力资源评估提供一定的理论支持和实践参考．
Institute of Scientific and Technical Information of China (English)
马国巍; 佟光霁; 李天霄
2011-01-01
文章简要分析了BP神经网络的结构和学习过程，然后以1978—2009年的中国乳业规模发展数据为基础，以奶牛年末存栏数（千头）、奶牛养殖业产值、畜牧业总产值等三个变量作为输入、中国奶牛产量作为输出，采用MATLAB2009a中的BP神经网络工具箱构建了基于BP神经网络的中国乳业发展规模预测模型。研究结果表明：未来十年中国原奶产量将大幅度增长，乳业规模将迅速扩大，原料奶生产无组织管理已不适应乳业经济的迅猛发展，“十二五”期间必须转变原料奶总量的增长方式。研究成果对于奶牛养殖业管理部门、养殖业者科学合理地规划奶牛养殖规模具有重要的参考价值。%This article briefly analyzes BP Neural Network＇ s structure and its learning process, and then takes the quality of dairy herds at the end of the year, the value of dairy cow industry and total value of livestock industry as three input variables, dairy cow milk output in China as output, with the data of Chinese Dairy Industry scale from 1978 to 2009 as the base;it creates a model to predict Chinese dairy industry development scale upon BP Neural Network with the using of BP Neural Network tool box form MATLAB2009a. The research result shows that in the following 10 years Chinese raw milk output will increase heavily and dairy industry scale will expand rapidly, which requires more than non -organized raw milk production management. During ＂the 12th 5 -year -plan＂period, the increasing way of raw milk production must be changed. The result is of important reference for cow farming management department and cow farmers to plan and control cow farming scale.
Hedonic Housing Price Model Via BP Neural Network%Hedonic住宅特征价格模型的BP神经网络方法
Institute of Scientific and Technical Information of China (English)
司继文; 韩莹莹; 罗希
2012-01-01
In this paper, hedonic pricing model is used to assess the housing price in Washington, USA. For the pricing model, in this paper, the crime variables around the house are included. The model is built by hedonic pricing method through using traditional OLS method and neural network to simulate and with data modified by Box-cox transformation. The result shows the change in criminal rate makes the housing price change, and as the distance of crime to the housing and the types of crimes changes, the house price changes from -5. 78% to 2. 08%. In July of 2007 and the whole 2008, the influences of crime on housing price are different. It also shows that neural network is more accurate than the traditional OLS method with 5. 74% higher degree of approximation, and shows better features.%房地产在金融市场中占有举足轻重的地位,其价格变化对整个金融市场有着显著的影响.采用特征价格模型,对美国一线城市2007年6月及2008年的房价进行了相关定价研究.对传统特征价格模型的属性因子进行了扩充,加入房产周边犯罪率因子进行模拟；在数值方法计算方面,首先对数据进行了Box-cox变换,分别采用BP神经网络及传统的最小二乘法进行数值模拟分析,结果表明,房价随犯罪事件类型及发生距离房地产的远近有—5.78％～2.08％的变化；在2008年与2007年6月的不同时段内,犯罪率的变化对房价的影响有所不同.BP神经网络模拟的价格与实际交易价格曲线比传统最小二乘模拟的价格曲线精度高出5.74个百分点.
Critical Branching Neural Networks
Kello, Christopher T.
2013-01-01
It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…
Institute of Scientific and Technical Information of China (English)
张珏; 张建强
2012-01-01
According to historical data of municipal solid wastes quantity in central areas of Chengdu. The fitted value of his- torieal data was obtained by grey GM（ 1,1 ） prediction model with time as variable. The relations between gray correlation degree and municipal solid wastes quantity and its influencing factors were analyzed. Four factors were selected to establish a muhivari- able gray GM（ 1,5） prediction model and BP neural network model. The prediction accuracy of municipal solid wastes quantity with the two models was compared. BP neural network model whose prediction accuracy is the highest was adopted to forecast the waste quantity in following years. This paper provides theory basis for disposal plan of municipal solid wastes in the central city of Chengdu.%根据成都市中心城区垃圾产生量的历年数据，先用以时间为单变量的灰色GM（1，1）预测模型得到历年数据的拟合值，再分析垃圾产生量与其影响因素之间的灰色关联度，选出关联度最大的4个因素建立多变量的灰色GM（1，5）预测模型与BP神经网络模型，并对垃圾产生量的预测精确度进行了对比，用预测精度最高的BP神经网络模型对未来年份的垃圾产生量进行了预测，为成都市垃圾处理处置规划提供了理论依据。
Institute of Scientific and Technical Information of China (English)
周长英
2011-01-01
This paper studies the use of BP neural network for image segmentation.Traditional image segmentation methods often cause low resolution and definition.This paper presents a fuzzy BP neural network for image segmentation.Fuzzy set theory is used to reduce the regional characteristics of segmentation images and the dimensions of feature vectors.Based on the rules, the neuron number is dicided, and the classification decision - making is output.Finally, the experiment results showthat the proposed algorithm can effectively segment images, the segmentation has sharp edges.In additon, the algorithm can shorten the training time effectiveliy.%研究了使用BP神经网络方法进行图像分割问题.针对神经网络用于分割图像时需要大量的训练数据,由于数据量大,计算速度相当慢,不适合实时数据处理,造成图像分割分辨率低,清晰度不高等问题,本文提出了一种采用模糊BP神经网络的图像分割算法.采用模糊集理论来约减分割后的图像区域特征,降低特征向量的维数,依据规则构造神经元个数,从而输出决策的分类值,最后采用BP神经网络算法进行迭代,最终得到决策结果并输入分割的图像,最后实验证明本文提出的算法能有效的分割图像,图像分割边缘清晰,同时该算法有效的缩短了样本训练的时间.
Forecast Method of Road Freight Traffic Based on BP Neural Network%基于神经网络算法的公路货运量预测方法
Institute of Scientific and Technical Information of China (English)
王栋
2014-01-01
Shanxi Province was taken as an example for the road freight traffic forecasts by using gray correlation method. The predictors are GDP, the first industry, the secondary industry, industrial added value, per capita GDP ,total fixed asset investment and the total retail sales of social consumer goods. The prediction model of road freight traffic is established on base of BP neural network,and then verified with tests. The results show that road freight traffic can be predicted accurately by the model based on BP neural network,and the maximum error is less than 5 . 3%. It can improve the forecast ability of road freight traffic and provide a method for road freight traffic.%以陕西省为例，运用灰色关联分析法确定公路货运量的影响因素分别为地区生产总值、第一产业增加值、第二产业增加值、工业增加值、人均地区生产总值、全社会固定资产投资和社会消费品零售总额.将所确定的因素作为公路货运量的预测指标，建立基于BP神经网络的公路货运量预测模型，并对模型进行应用测试.结果表明：该模型具有较高的精度，最大误差为5.3%，可以提高公路货运量预测的准确度，为我国公路货运量的预测研究提供方法支撑.
BP神经网络在双伺服同步运动系统中的应用%Application of BP Neural Network in Double Servo Synchronous Motor System
Institute of Scientific and Technical Information of China (English)
郭丽; 石航飞; 陈志锦; 杨凯; 李勇
2011-01-01
In the double servo synchronous motor system, the current speed often surpasses or lags the given speed,however the traditional PID algorithm can't solve this problem. Therefore, introduce a new PID regulator which combines the traditional PID with BP neural network's PID algorithm, it can control the movement of two servo motors. The traditional PID is used for controlling the two axles during normal operation. However, BP neural network PID algorithm is used to modify the parameters of the position regulator and parameters of the speed regulator in the process of debugging.Through this method, we can achieve the axis B track the speed and orbit of the axis A accurately.%针对传统的PID调节器不能解决双伺服同步运动系统中经常出现的超调和滞后问题,提出一种将传统PID和BP神经网络的PID调节器相结合的方式来控制两伺服电机轴的运动.其中,传统PID算法用于系统正常运行时的控制,而BP神经网络的PID算法用于调试过程中修改位置调整器和速度调节器的参数.该方案能实现轴B准确地跟踪轴A的速度和轨迹而运动.
基于BP神经网络的大型客机经济性分析%The Economy Analysis of Large Air Bus Based on BP Neural Networks
Institute of Scientific and Technical Information of China (English)
陶金亮
2011-01-01
In order to make large air bus designers have a more accurate grasp of its economy, it proposes a new analysis model of economy of large air bus and a modeling procedure based on advanced BP neural networks, realizes a digital simulation with JAVA for simulation software to evaluate the neural networks model for the economy of large air bus and analyzes the error, proposes an advanced method. The results show that the model is effect, high - precision and very practical.%为使大型客机设计人员在飞机设计阶段便可对经济性有较为准确的把握,针对大型客机经济性,采用改进的BP神经网络理论建立了一种新的分析模型,并给出建模流程.利用JAVA语言实现核心算法进行数字仿真,对所建立大型客机经济性分析的神经网络模型进行了验证,并进行误差分析,提出改进方案.仿真结果表明,所建BP神经网络对大型客机经济性的估算是有效的,且该方法精度较高,实用性较强.
基于BP神经网络的冷却器购置费用估算%Purchase Expenses Estimated for the Cooler Based on the BP Neural Network
Institute of Scientific and Technical Information of China (English)
杨明
2011-01-01
Purchase expenses estimated for the cooler is an important part in LCC technique. If we can accurately estimate the expenses, we can control the expenses. There are some problems in the general estimated methods. Such as the workload is big, the accuracy of the estimate is not objective. This paper put forward a method based on the BP neural network. This method can use the machine learning, and built the neural network model to estimate the expenses of the cooler.%冷却器购置费用是寿命周期费用(ICC)的重要组成部分,对购置费用的准确预测有助于对寿命周期费用的有效控制.针对一般常用的费用估算方法存在费用估算工作量大、预测精度带有很强的主观性等问题,提出一种基于BP神经网络的费用估算方法,该方法能够利用机器学习,建立冷却器购置费用估算的神经网络模型.
基于BP神经网络的音乐情感分类及评价模型%Music emotion classification and evaluation model based on BP neural network
Institute of Scientific and Technical Information of China (English)
赵伟
2015-01-01
针对多音轨MIDI文件，提出一种多音轨MIDI音乐主旋律识别方法，通过对表征音乐旋律特征的音高、音长、音色、速度和力度5个特征向量的提取，构建基于BP神经网络的情感模型，并且用200首不同情感特征的歌曲对其进行训练和验证。实验结果显示取得了较好的效果。%The audio track of music melody includes a lot of useful information of music melody, which is the basic of music character recognition and also the premise work in the design of the performance plan of music foundation .Five eigenvectors:pitch, length, tone tempo and strength are extracted for the expression of music melody, by which, the basic music character recognition system can be set up. A emotion model is formed by using BP neural network.200 songs with different emotional characteristic songs will be used as the sample data for the training and validation of the neural network. The results of validation shows the effectiveness of the emotion model.
Institute of Scientific and Technical Information of China (English)
于丽
2014-01-01
考虑了经济环境对铁路货物运量的影响，利用GM(1，n)和BP神经网络模型建立了锦州站货运量组合预测模型。利用神经网络模型能趋近任意函数的特点和GM(1，n)的前期数据处理，使预测模型不受数据波动的影响幵具有更高的预测精度。事实证明，该模型能很好的用于锦州站运量预测。%Using GM (1, n) and BP neural network model to establish a cargo forecasting combination model considering economic environment. Using the characteristics of neural network model that can approach any arbitrary function and pre-processing data making use of GM(1,n), in order to avoid the impact of fluctuations in the data and make the forecasting model have higher prediction accuracy.Facts have proved that the model can be well used in Jinzhou station cargo forecasts.
Institute of Scientific and Technical Information of China (English)
崔东文; 金波
2014-01-01
This paper focuses on several key issues of a BP neural network when it is applied to the comprehensive evaluation of water conservancy in a state of relative prosperity. Based on the analytic hierarchy process (AHP), 30 representative indicators were selected out of more than 100 water conservancy indicators, in order to build up a comprehensive evaluation system of water conservancy in a state of relative prosperity and grading standards as well. In practical application, the BP neural network has shortcomings, including the slow convergence and likely occurrence of local extreme values. To overcome these shortcomings, an LM-BP neural network model was established for comprehensive evaluation of water conservancy in a state of relative prosperity. In this case, training and testing samples were generated between standard thresholds using the random interpolation method. A concept of network fitness is proposed as well. The performance of the proposed model was evaluated using the network fitness, the average relative error, and three other statistical indicators. After the evaluation of the model achieved the expected accuracy and generalization ability, it was applied to the comprehensive evaluation of water conservancy in a state of relative prosperity in Wenshan Zhuang and Miao Autonomous Prefecture, and compared with traditional BP and RBF models. The results are as follows: ( a) In both the training samples and testing samples, the LM-BP model had higher evaluation accuracy than traditional BP and RBF models by nearly an order of magnitude, indicating that the LM-BP model has high accuracy and generalization capability and is applicable to the comprehensive evaluation of water conservancy in a state of relative prosperity in Wenshan Zhuang and Miao Autonomous Prefecture. In addition, the LM-BP model has the advantages of fast convergence and a high degree of stability. (b) In the year 2010, water conservancy in a state of relative prosperity in Wenshan
The fault diagnosis of garbage crusher based on rough Set-BP neural network%基于粗糙集-BP神经网络的垃圾破碎机故障诊断
Institute of Scientific and Technical Information of China (English)
孙永厚; 李聪
2012-01-01
针对目前垃圾破碎机故障诊断效率低的问题,设计了一种基于粗糙集理论与BP神经网络的故障诊断系统.结合粗糙集理论和BP神经网络的优点,首先利用粗糙集对原始故障诊断样本进行处理,然后对条件属性进行约简,删除冗余的信息,减少神经网络输入端的数据,从而简化神经网络的结构.并将基于粗糙集-BP神经网络的故障诊断系统对垃圾破碎机进行故障诊断.利用粗糙集对故障知识进行约简,简化BP神经网络结构,提高故障诊断的速度及准确度.将此方法应用于某型号垃圾破碎机的故障诊断中,诊断结果表明所提诊断方法可简化神经网络结构,提高诊断效率.%To improve the fault diagnosis efficiency of garbage crusher ,a fault diagnosis system for garbage crusher is designed,which is based on rough set and BP neural network.First the rough set is used to process the original fault diagnosis sample.Then conditional attribute is simplified by deleting redundant information and lessening data at input of neutral network,thus the structure of the neutral network is sim~ plifhed.Meanwhile fault diagnosis for the garbage crusher is carried out by the fault diagnosis system based on rough set-BP neutral network.Afterwards fault knowledge is simplified by utilizing the rough set,and the structure of BP NN is simplified as well as the diagnosis speed and accuracy is improved.The proposed method is applied in the fault diagnosis of garbage crusher,which results show that the proposed method can simplify structure of BP NN,improves efficiency of the diagnosis.
Institute of Scientific and Technical Information of China (English)
孙晓红; 杜龙安; 刘弘; 张晓伟
2012-01-01
针对标准BP神经网络易陷入局部极小值的问题，本文结合全局随机搜索最优解的粒子群优化算法，建立了一种3D动漫造型评价模型，并将其应用到3D动漫造型的生成过程。该模型充分利用粒子群算法的全局寻优特性，优化BP网络的权值和阈值，使网络的均方误差小于或等于目标设定值。实验结果表明，本文方法在保证BP网络能收敛到全局最优解的前提下，加快了BP网络的收敛速度和收敛精度，并在3D动漫造型的进化中具有较好的评价性能，提高了造型的生成质量。%This paper constructs a 3D animation modeling evaluation model with Particle Swarm Optimization (PSO) algorithm and BP network in view of the issues of easy falling of standard BP neural network into local minimum and the global searching of PSO. We apply the model to the generation of 3D animation modeling. It fully utilizes the characteristic of global searching of PSO and optimizes the weights and thresholds of BP network, which makes mean-square error less than or equal to the preset value. Experimental results show that the approach improves the convergence rate and convergence precision of BP network based on the guarantee of the global optimization result. It has preferable evaluation capability in the evolution of 3D animation modelings and improves the quality of 3D animation modelings.
Term Structure of Interest Rates Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.
Institute of Scientific and Technical Information of China (English)
谭穗妍; 马旭; 吴露露; 李泽华; 梁仲维
2014-01-01
Super hybrid rice that is widely cultivated in China is mainly planted by transplanting techniques. In super hybrid rice transplanting, a seedling nursery is a preliminary but critical one. Complete rice seedling nursing operations include seed selection, seed germination, sowing of seeds onto seeded trays, and seedling hardening. The sowing process is performed on an automated seeder sowing test line. Matured seedlings are transplanted to the field by transplanters, which are specially designed for transplanting rice seedlings. In practice, some problems arise during the sowing process. Because super hybrid rice has strong tiller ability, it requires a precise and low seeding rate, which needs to ensure 2-3 grains in each cell in tray plugs. Furthermore, rice seed traits, such as length, shape, moisture, and weight change during the sowing process, which greatly affects the performance of the seeder sowing machine. As a result, seed distribution on the tray plugs is uneven, and cavity and single grain rates in tray plug cells are high, which is up to 20%. The low seeding rate will lead to a low seedling survival rate. Also, the estimation accuracy of grains that are overlapped and adhered is quite low. To solve the problem, it is necessary to add nursery cell tray sowing quantity estimation to an automated rice sowing test line. When cavities and single grains in tray cells are detected, artificial reseeding and working parameter adjustment to the seeder sowing machine are needed. After rice seeds are sowed onto the nursery trays, seed connected regions extracted from the acquired image may occur as the following situations: impurity, single grain, and grains that are overlapped and adhered. To a great extent, the shape features of each connected region can determine the grain quantity. In this paper, a method was presented to estimate the sowing quantity per cell in the tray plug, based on machine vision and a BP neural network. The method consisted of four
基于BP网络的血液气味识别模型的建立%Identification of blood odors based on BP artificial neural network
Institute of Scientific and Technical Information of China (English)
龙成生; 王辛; 单军; 吴德华; 宋珍华
2012-01-01
以血液气味样品的气相色谱质谱分析结果为基础,建立了一个基于BP人工神经网络的血液气味识别模型,并利用Matlab计算平台对此模型进行了优化、训练和测试.此模型的网络结构为9×13×1,隐含层传递函数为tansig,输出层传递函数为logsig,训练函数为trainrp.优化后的模型对血液样品的正确识别率为100％.%This paper proposed an identification model for blood odors based on Back Propagation Artificial Neural Network. The features were extracted from the data of blood samples which were analyzed by means of Gas Chromatography-Mass Spectrometer. Matlab was used to optimize the model, which then was trained and tested using human and animal blood odor samples. The architecture of the model was 9 × 13 × 1, the transfer functions of the hidden layer and the output layer were tansig and logsig, respectively, the train function was trainrp. The optimized model correctly identified the train and test samples with the accuracy of 100%.
Probabilistic runoff forecasting model based on BP artificial neural network%基于BP神经网络的概率径流预测模型
Institute of Scientific and Technical Information of China (English)
周娅; 郭萍; 古今今
2014-01-01
本文采用多元线性回归模型模拟贝叶斯分析的先验分布和似然函数,并结合反向传播神经网络(BackPropagation Neural Network)建立基于BP神经网络的贝叶斯概率径流预测模型,将模型应用于石羊河出山口六河水系的年径流预测中.为降低BP神经网络的“黑箱”特性对预测精度的影响,在实例应用中结合了区域的水文特性对数据进行预处理,结果表明该方法有效的提高了模型的预测精度;同时相对于确定性水文预测方法而言,贝叶斯概率水文预报定量地、以分布函数形式描述水文预报的不确定度,能向用户提供更多、更全面的信息,为决策提供更有价值的技术支持.
基于LM-BP网络的粮食产量预测%Forecasting Corn Production Based on LM-BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭庆春; 可振芳; 李力
2012-01-01
A corn production porecasting method based on improved LM-BP was proposed. According to measurement and a-gricultural significance principle, 9 factors of grain-sown area, fertilizer input, effective grain irrigated area, stricken area, rural electricity consumption, total agriculture mechanism power, the population engaged in agriculture, rural residents family productive assets, the average net income of rural households were extracted as the network input; corn production was extracted as the network output. The LM algorithm could minimize the error, and the modeling results were evaluated with the correlation coefficients, relative error, etc. For training sample set, the correlation coefficient between the simulated value and the actual value was 0.996, the average relative error was 0.47%; for testing sample set, the correlation coefficient between the forecasted value and the actual value was 0.994, the average relative error was 0.56%. The results showed that the improved LM-BP model could improve simulation precision and stability of the model. This method is effective and feasible for com production prediction.%利用Levenberg-Marquardt (LM)算法对BP神经网络法进行改进,提出了基于改进型LM-BP神经网络模型的粮食产量预测方法.提取了粮食作物播种面积、化肥施用量、粮食作物有效灌溉面积、受灾面积、农村用电量、农业机械总动力、从事农业的人口、农村居民家庭生产性固定资产原值、农村居民家庭平均纯收入9个因子作为输入因子构筑模型,粮食产量作为网络输出,通过LM算法使网络误差最小化,最后使用相关系数、相对误差等指标对模型的模拟结果进行检验.结果表明,训练样本集中模拟值和实际值的相关系数为0.996,平均相对误差为0.47％；检测样本集中,预测值和实际值的相关系数为0.994,平均相对误差为0.56％；该模型具有较高的拟合精度和预测精度,将此网络模
Institute of Scientific and Technical Information of China (English)
XU Min; ZENG Guang-ming; XU Xin-yi; HUANG Guo-he; SUN Wei; JIANG Xiao-yun
2005-01-01
Bayesian regularized BP neural network(BRBPNN) technique was applied in the chlorophyll-a prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS 11.0 software, the BRBPNN model was established between chlorophyll-a and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.00078426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll-a declined in the order of alga amount > secchi disc depth(SD) > electrical conductivity (EC) . Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-a concentration, total phosphorus(TP) and total nitrogen(TN) were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data(chlorophyll-a prediction) and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.
Single-tree biomass modeling of Pinus massoniana based on BP neural network%基于BP神经网络的马尾松立木生物量模型研究
Institute of Scientific and Technical Information of China (English)
王轶夫; 孙玉军; 郭孝玉
2013-01-01
The purpose of this study was to explore and verify the applicability of BP neural network model on the single-tree biomass estimation for Pinus massoniana. The optimal model had been built after the topology was determined through screening 12 algorithms and choosing the number of inputs, outputs and hidden nodes. To explain the impact of input variable number on the accuracy, double input BP model was compared with single one. Also, to explain the impact of output variable number on the accuracy, multiple output BP model was compared with single one. And to verify the feasibility, the optimal BP model was compared with allometric equation. The results showed that; 1 ) the algorithm of optimal model LM-DH-8-WtWaWr was Levenberg-Marquardt algorithm, with DBH and height as input variables, total weight, weight of above ground and weight of root as output variables, and the number of hidden nodes was 8. 2 ) Adding input and output variables would not decrease the accuracy of BP neural network model. 3) The optimal BP model LM-DH-8-WtWaWr had a good performance in estimating the biomass of P. massoniana and its accuracy was higher than the relative growth model. The BP model can be used to estimate several quantities at once, which makes the estimation of single-tree biomass more simply.%以马尾松为例,探索并验证BP神经网络模型在立木生物量估测上的适用性.通过12种算法的筛选、输入变量和输出变量的确定以及隐层节点数的选择,确定最优的模型拓扑结构,构建单隐层BP神经网络模型；对比单输入变量与多输入变量模型、单输出变量与多输出变量模型,并分析模型的输入变量数和输出变量数对模型估测精度的影响；将优选BP模型与传统相对生长模型进行对比以验证BP模型的可行性.结果表明:1)最优BP模型LM-DH-8-WtWaWr的训练算法为Levenberg-Marquardt算法,输入变量为D、H,输出变量为Wt、Wa、Wr,隐层节点数为8.2)输入变量
Lakra, Sachin; T. V. Prasad; G. Ramakrishna
2012-01-01
The paper describes some recent developments in neural networks and discusses the applicability of neural networks in the development of a machine that mimics the human brain. The paper mentions a new architecture, the pulsed neural network that is being considered as the next generation of neural networks. The paper also explores the use of memristors in the development of a brain-like computer called the MoNETA. A new model, multi/infinite dimensional neural networks, are a recent developme...
Neural Networks in Data Mining
Priyanka Gaur
2012-01-01
The application of neural networks in the data mining is very wide. Although neural networks may have complex structure, long training time, and uneasily understandable representation of results, neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. In this paper the data mining based on neural networks is researched in detail, and the key technology and ways to achieve the data mining based on neural networks are also researched.
Neural networks and graph theory
Institute of Scientific and Technical Information of China (English)
许进; 保铮
2002-01-01
The relationships between artificial neural networks and graph theory are considered in detail. The applications of artificial neural networks to many difficult problems of graph theory, especially NP-complete problems, and the applications of graph theory to artificial neural networks are discussed. For example graph theory is used to study the pattern classification problem on the discrete type feedforward neural networks, and the stability analysis of feedback artificial neural networks etc.
Institute of Scientific and Technical Information of China (English)
苏里坦; 玉米提; 宋郁东
2011-01-01
its application possibility with gradient descent method combining neural network. The modified BP network model has been established based on the adding gradient descent method of momentum to the traditional BP network in this study. A new method was tested for predicting vegetation evapotranspiration by taking meteorologic data, plant data and soil data, and the validity of model. Results show that non-linear function reflection relation between environmental factors such as meteorological factor, plant factor and soil factor, and evapotranspiration of Phragmites australis can be reflected with the help of combining the gradient descent method and the BP neural network. The Matlab software was used to predict vegetation evapotranspiration in arid area of Xinjiang. The results of prediction indicate that the maximum relative errors of the evaporation and the transpiration were 7.62% and 11.63% , respectively, and the maximum correlation coefficients were 0.936 9 and 0.957 4, respectively, representing a high prediction precision. Therefore the combination method is better than un-combination one, which eliminates the correlation index of samples and reduces the input dimension of neural network. It makes node numbers of generalized regression neural network input layer cut down to six from eight, and has the effects of simplifying structure and strengthening stability of neural network. The combination method indicates that the proposed modelling is more reliable and its performance is better than that of conventional method. The combination model can resolve the problem that multi-collinearity in factors can not be effectively identified and eliminated by regression of the least square method. The simulated results of modified BP network model show that the prediction precision is high under the condition of having no long-term climatic data. The model provides a newly effective and feasible way for the evaluation of natural vegetation evapotranspiration and the
I mproved neural network model application in fire detection%一种新型 BP 神经网络模型在火灾探测信息处理中的应用
Institute of Scientific and Technical Information of China (English)
赵望达; 李卫高; 熊涵予; 韩柯柯
2015-01-01
现阶段，神经网络模型在火灾探测信息处理应用中存在以下缺陷：选取火灾特征组合具有主观性；选取的神经网络类型缺乏对比；缺乏大量实验数据对神经网络泛化能力的验证。利用 NIST 机构所做一系列火灾探测研究实验数据样本，通过信息熵理论在火灾信号选取中的应用获取火灾复合探测信号特征选取的组合形式，并在此基础上建立火灾探测信息处理神经网络初始模型。经过一系列 Matlab 仿真实验，分析神经网络的模型结构、传递函数和训练函数对仿真结果的影响，提出一种基于 trainbr 训练函数、tansig 传递函数的3－7－1结构 BP 神经网络模型。采用网络训练时间、探测点、误报率和网络输出区间进行网络性能分析，验证所提出模型在火灾探测中应用具有训练速度快，结果稳定可靠，探测灵敏的特点。%In the processing of fire detection,some defects are contained in the use of neutal network model. These defects are the subjectivity of the selection of fire feature,the lack of comparison among the selected neural network types and the lack of adequate experiments.Based on a series of data samples of fire detection experi-ments given by NIST,the combination of signal feature selection for composite fire detection through the applica-tion of information entropy theory in the selection of fire signal is obtained,and the initial model of neural net-work for the fire detection information processing is established.After a series of exploratory experiments simula-ted by Matlab,the effects of the structure of the neural network model,transfer function and the training function on the simulation results are analyzed,and an improved BP neural network model based on trainbr training func-tion,tansig transfer function and the structure of 3 -7 -1 is proposed found.Through the network performance analysis of network training time,probe points,the false
Institute of Scientific and Technical Information of China (English)
闵武志; 韩谷静
2011-01-01
Proportion resonant (PR) control technology of power grid-connected inverter of current model control was researched, and the model of power grid-connected inverter of LCL filter of double current circles was analyzed. Aiming at the difficulty of realization with digital controller for new-style PR controller, this paper put forward a incremental PR control technology based on BP neural network. On the MATLAB platform, a mathematical model simulation was built. Simulation experiment confirmed that the output current of gird-connected inverter controlled by incremental PR control technology based on BP neural network had better dynamic and static performance on condition of current mutation. Control results of grid-connected current trained by neural network turned out to be good.%研究了电流模式控制的电力并网逆变器的PR( proportion resonant)控制策略,针对提高并网供电功率优化逆变换器设计,分析了含LCL滤波器的电流双环控制电力并网逆变器模型.目前采用的比例谐振控制器难以用数字控制器实现的问题,提出了BP神经网络的增量式PR控制技术.在MATLAB平台上,建立数学模型仿真.仿真结果证明,在电流发生突变情况下,采用BP神经网络的电力并网逆变器的增量式PR控制,电流波形具有更好的动态性能与静态性能.对神经网络训练进行仿真,结果表明,并网供电控制取得良好的供电效果,为设计提供了参考依据.
Decoupling Control Method Based on Neural Network for Missiles
Institute of Scientific and Technical Information of China (English)
ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao
2005-01-01
In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.
Introduction to neural networks
International Nuclear Information System (INIS)
This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix
Institute of Scientific and Technical Information of China (English)
秦晏旻; 李雪英; 任静; 蒋洪德
2011-01-01
Film cooling is necessary for modern gas turbine.Its cooling effectiveness is sophisticated influenced by multi parameters.The BP neural network is applied to predict the adiabatic film cooling effectiveness of the cooling system with multi geometry and flow parameters.The input parameters of neural network are chosen as blowing ratio,density ratio,free stream turbulence intensity,area ratio and length ratio.A database covering the real operation range is build up.Prediction from the neural network trained by Bayesian Regulation backpropagation is compared to an existing correlation.The result shows a good accuracy and wide application range of the neural network model.It implicates that the developed model is promising to be applied on the film cooling system.%气膜冷却作为当代燃机高温透平中必需的冷却手段,其冷却性能在多种参数的影响下表现复杂。采用BP神经网络模型对多种几何、流动参数变化下的气膜冷却系统的绝热气膜冷却效率进行预测。选择气膜冷却系统的吹风比、密度比、主流湍流度、面积比和长径比作为神经网络的输入参数,以燃气轮机透平叶片气膜冷却的实际运行工况为范围建立数据库。计算结果表明,采用贝叶斯归一化法训练后建立的气膜冷却神经网络模型在预测精度上要优于经验公式法,而且参数适用范围更广,具有良好的发展应用前景。
Research on Stock Price Reversal Points Prediction Based on BP Neural Network%基于BP神经网络的股票价格反转点预测
Institute of Scientific and Technical Information of China (English)
王建国
2015-01-01
The stock market has an important role in the whole financial market and the prediction of the stock price reversal point is one of the most abstractive and the most significant research topic. And the BP neural network which has been proved to be the ability of achieving any nonlinear mapping function whatever its complexity ,is very suitable for resolving the problem with a complex internal mechanism such as stock price prediction. Aims to achieve stock price reversal points prediction with BP model.%股票市场在整个金融市场中起着很重要的作用。而股票价格反转点的预测是最具有吸引力并且有意义的研究问题之一。 BP神经网络作为已被证明为具有实现任何复杂非线性映射的功能的多层预测模型特别适合于求解股票预测之类的内部机制复杂的问题。旨在利用BP神经网络模型的预测能力实现对股票价格的反转点预测。
Neural networks in seismic discrimination
Energy Technology Data Exchange (ETDEWEB)
Dowla, F.U.
1995-01-01
Neural networks are powerful and elegant computational tools that can be used in the analysis of geophysical signals. At Lawrence Livermore National Laboratory, we have developed neural networks to solve problems in seismic discrimination, event classification, and seismic and hydrodynamic yield estimation. Other researchers have used neural networks for seismic phase identification. We are currently developing neural networks to estimate depths of seismic events using regional seismograms. In this paper different types of network architecture and representation techniques are discussed. We address the important problem of designing neural networks with good generalization capabilities. Examples of neural networks for treaty verification applications are also described.
Institute of Scientific and Technical Information of China (English)
张载龙; 茹亮
2013-01-01
With the improvement of people's living standards,the safety of food and medicine is becoming the focus of attention. Temper-ature monitoring is the key factor to ensure the material safety and reduction of economic losses in the logistics transportation. Especially for dairy products,plasma,vaccines and other temperature-sensitive items,more stringent is required. Currently,the refrigerated trucks that lack of a better way in intelligent control can not be achieved for the effective temperature monitoring. Using BP neural network to predict the change in temperature of the items can achieve good control effect. In this paper,a novel method of BP learning algorithm to improve the convergence rate in BP neural network is proposed. The method is used to predict the cold chain temperature. Matlab simula-tion shows that the algorithm has a fast convergence rate theoretically.%随着人们生活水平的提高，食品和医药安全逐渐成为社会关注的焦点。温度监控是保证物流运输中物品安全、减少经济损耗的关键。尤其是乳制品、血浆、疫苗等温度敏感性物品对运输环境中的温度要求更严格。当前冷藏车温度监控在智能控制方面缺乏较好的方法，无法达到对于温度敏感性物品的有效监测。通过BP神经网络对物品的温度变化进行预测可以达到很好的监控效果。针对BP神经网络中存在的收敛速度慢的问题，文中提出了一种自适应的学习速率的新方法，并将其应用于冷藏车温度预测中，通过Matlab仿真表明该算法具有很好的预测效果。
第三方网上支付企业核心竞争力评价%Third-Party Online Payment Core Competence Evaluation Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
王拉娣; 史亚伟
2012-01-01
Through the online payment industry environment, industry chain and the analysis of major enterprises, built with third-party online payment core competence evaluation index system including with 14 evaluation index. BP neural network model is designed to select a sample of six training companies, three companies to test, and the use of BP neural network model quantitatively identify third-party online payment the strength of the core competence of enterprises. Studies have shown that: compared with the traditional linear model, BP evaluation mode is more dynamic and self-learning nature, the error evaluation of the results of small, high precision, fully reflects real situation of the third-party online payment enterprise's core competence, for third-party online payment to build the core compete-tiveness of enterprises to provide a benchmark, while the third-party online payment company for quantitative evaluation of core competencies has opened up a new way.%通过对网上支付行业环境、产业链和主要企业的分析,构建了具有14个评价指标的第三方网上支付企业核心竞争力评价指标体系.设计了BP神经网络模型,选择了6家样本企业进行训练、3家企业进行测试,并运用BP神经网络模型定量识别第三方网上支付企业核心竞争力强弱.研究表明:BP评价模型与传统的线性评价模型相比,具有更高的动态性和自学习性,评价结果误差小,精度高,能充分反映第三方网上支付企业核心竞争力的真实状况,为第三方网上支付企业核心竞争力的打造提供了基准,同时对第三方网上支付企业核心竞争力进行定量评价开辟了一条新途径.
A Modified Algorithm for Feedforward Neural Networks
Institute of Scientific and Technical Information of China (English)
夏战国; 管红杰; 李政伟; 孟斌
2002-01-01
As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.
Institute of Scientific and Technical Information of China (English)
崔东文
2013-01-01
分析BP神经网络应用于水质评价中存在的问题和目前水质评价中的不足，基于地表水环境质量分级标准和L-M算法原理，提出LM-BP神经网络水质综合评价通用模型。利用随机内插方法在地表水环境质量分级标准阈值间生成训练样本和检验样本，采用顺序和随机两种方法选取训练样本和检验样本进行随机模拟；利用平均相对误差、最大相对误差等统计指标评价LM-BP模型性能，并构建传统BP 、RBF模型作为对比模型；以某水质评价实例进行模型验证，并与灰色关联分析法、模糊综合评判法和TOPSIS法评价结果进行比较。结果表明：LM-BP通用模型具有评价精度高、泛化能力强、收敛速度快、算法稳定和通用性能好等优点，可应用于任意水质评价。在实际应用中仅需对通用模型的评价因子、输入维数和隐含层神经元数进行删减即可满足评价要求。%Existing problems and shortcomings in water quality evaluation using the BP neural network were analyzed. Based on surface water environmental quality grading standards and the principle of the L-M algorithm, a general model of the LM-BP neural network was developed for comprehensive assessment of water quality. First, the random interpolation method was used to generate training and testing samples at the surface water environmental quality grading standard threshold, and the order and random methods were used to select training and testing samples for random simulation. Then, statistical indices such as the average relative error and the maximum relative error were used to evaluate the performance of the LM-BP model, and the traditional BP and RBF models were constructed as the contrast models. Finally, the model was applied to water quality evaluation in a case study and compared with the gray correlation analysis method, fuzzy comprehensive evaluation method, and TOPSIS method. The results show that the
基于BP神经网络的试飞员驾驶技术评估%Test Pilot Driving Skill Assessment Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
张同斌; 李体方; 盛又文
2012-01-01
Research on the problem of test pilot driving skill assessment. Because of the complicacy of the test pilot driving skill and the nonlinear character of the influence factors, the traditional way had stronger subjective factor and lower assess accuracy. An evaluation index system was set up for test pilot driving skills through the pretreat-ment to cancel the useless information of the assessment system based on the influence factors analysis of the test pilot driving skill, and the best model of the test pilot driving skill assessment based on the treated data was also set up through the auto - learn BP network. The simulation result based on MATLAB show that the assessment method can raised the assessment veracity, and overcome, the deficiency of the traditional assessment method; the assessment result is more scientific. It also provides a new way to the test pilot driving skill assessment.%研究试飞员驾驶技术评估问题,由于试飞员驾驶技术的复杂性以及影响因子的非线性,传统的试飞员驾驶技术评估方法存在较强的主观因素,评估准确性较低,不利于试飞员驾驶技术的客观评估.为客观评估试飞员驾驶技术,提出一种BP神经网络的评估方法.首先在分析试飞员驾驶技术影响因素的基础上,通过预处理消除评估体系之间重复无用的信息,构建了试飞员驾驶技术评估指标体系,然后采用非线性学习能力强的BP神经网络对处理后的数据进行学习建模,通过BP神经网络自适应学习得到最优的驾驶技术评估模型,并通过MATLAB进行仿真验证.结果表明,BP神经网络方法提高了评估的准确性,克服了传统评估模型主观性强的缺陷,评估结果更具科学性,为试飞员驾驶技术评估提供一种新的途径.
汽车起重机力矩限制器算法模型的实现%The Application of BP Neural Network in Truck Crane Torque Limiter
Institute of Scientific and Technical Information of China (English)
姚立娟; 曾杨; 郑庆华; 刘涛
2011-01-01
利用BP网络的函数逼近性能与力矩限制器力矩平衡理论相结合的方法,根据现场采集的汽车起重机工况数据,设计BP网络结构,对数据样本进行训练,从而构建出力矩限制器中计算吊重的网络模型,并用大量的试验数据进行验证,结果表明该方法有效可行,为起重机力矩限制器算法模型的建立提供了一种新方法.%In this paper, a modeling method of truck crane torque limiter is presented via torque equilibrium theory and function approximation capabilities of BP neural network. The network structure is designed and the data sample is trained by studying and analyzing the working condition data of truck crane. And then the load computation network model of torque limiter is completed and verified with a lot of experiment data. The testing results prove the value of the modeling method, furthermore it provides a new method for modeling the algorithm of torque limiter.
Institute of Scientific and Technical Information of China (English)
闫岩; 孙彩堂; 周逢道; 刘长胜
2016-01-01
This paper is about a way to detect landmines based on BP neural network, put it more specifically,landmines are detected via landmine response curves acquired by electromagnetic detection. First, it tested the recognition effect of BP neural network upon four common curves namely sine wave, square wave, saw tooth wave and trapezoidal wave; second, simulation experiments are carried out to see how these curves are affected by changing the network parameters such as the number of hidden layer nodes and learning algorithms as well as by adding a certain proportion of noise in normal curves. Experimental results show that the recognition rate of all normal curves is 100% and that of the signals with noise less than 10% is also high. This technology has been applied to detect landmines and produced good results.%提出一种基于BP神经网络的地雷识别方法，利用电磁探测方法测得的地雷响应曲线对地雷进行识别。首先分析BP神经网络对4类常见曲线(正弦波、方波、锯齿波、梯形波)的识别效果，通过改变隐含层节点数、学习算法等网络参数以及对正常曲线加入一定比例的噪声，仿真分析它们对曲线识别的影响。实验结果表明：该方法对正常曲线的识别率几乎均达到100%，对于噪声约10%的信号也具有较高的识别能力。将该技术应用于地雷的识别中，取得比较好的识别效果。
Institute of Scientific and Technical Information of China (English)
刘垠杰; 黄强; 程玉强; 吴建军
2012-01-01
将云模型与BP（backpropagation）神经网络以串联方式有机结合，首先利用云变换方法进行网络的结构辨识和云模型的特征提取，同时通过在输入层引入单位延时环节描述发动机工作过程动态特性，研究提出了基于动态云BP网络的液体火箭发动机故障诊断方法.结合实际试车数据的验证结果表明，该方法能够准确识别发动机已有的3种故障模式，通过在试车数据中添加0期望、0.2标准差的随机噪声的方法来模拟环境噪声和测试过程中产生的随机噪声，根据持续性原则，方法仍能够正确进行故障检测与分类.方法单步运行时长为1.124x10-4,完全能够满足实时性要求.%A fault diagnosis method for liquid-propellant rocket engines was proposed based on the dynamic cloud-BP(back propagation) neural network in the way of the integration of cloud model and BP neural network.The Cloud transform method was used to identify the network configuration and to extract the cloud features.And a unit time-delay was also introduced into the input layer to describe the dynamic characteristics of the engine.Results with test data show that the method can isolate the existed 3 fault modes precisely.A 0 expectation,0.2 standard deviation noise was used to simulate the entironmental noise and stochastic noise,and the method can still detect and classify the fault accurately acount to lasting-rule.The method can run in real-time with the single processing time being 1.124×10-4 s.
基于BP网络的混凝土耗能器骨架曲线拟合%Based on BP Neural Network of Concrete Energy Dissipator Skeleton Curve Fitting
Institute of Scientific and Technical Information of China (English)
王文娟; 陈继光
2012-01-01
结合5种混凝土延性柱耗能器在低周期反复荷载作用下的试验数据研究,利用神经网络的工作原理,通过建立神经网络的输入层、隐含层、输出层,确定输入单元、输出单元和隐含层节点数,从而建立了BP神经网络的模型,并根据已有的部分试验数据数据.对网络进行训练,对各种混凝土延性柱耗能器骨架曲线进行了预测拟合,实现混凝土延性柱耗能器骨架曲线的数字化,使其成为具有分析和判断的拟合曲线功能,完整的描绘混凝土延性柱耗能器的骨架曲线,为后续混凝土延性柱耗能器性能研究的仿真模拟提供了可靠的数据模型.结果表明,这种方法是可行的.%Combined with 5 kinds of concrete ductility column energy dissipator at low cycle load test data research, the working principle of neural network, and by establishing a neural network's input layer, hidden and output layer, determine inputs unit, output unit and hidden node number, and to establish the BP neural network model, and part of the test data according to the existing data. Networks are trained to of all kinds of concrete ductility column energy dissipator skeleton curve fitting, forecast the realization concrete ductility column energy dissipator skeleton curve digital, make it become with analysis and judgment of the fitting curve function, complete description of concrete column energy dissipator ductility of skeleton curves, for the subsequent concrete ductility column energy dissipator performance simulation study provides the reliable data model. The results show that the method is feasible.
Research of neural network application in methane gas spectrum sensing system
Zhou, Meng-ran; Zhang, Haiqing; Wu, Hongwei; Yu, Gang
2010-10-01
Laser spectroscopy combined with neural network approach is a new method of monitoring coal mine gas. This research analyses of gas concentration and predicts the process of modeling using BP neural network finds changes law of concentration, gives the various parameters settings of neural network. Experimental results show that, BP neural network for early warning of gas concentration is feasible. The study meets an online, real-time, fast agreement of China's Coal Mine Gas monitoring systems.
Rule Extraction:Using Neural Networks or for Neural Networks?
Institute of Scientific and Technical Information of China (English)
Zhi-Hua Zhou
2004-01-01
In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.
Catalytic Oxidized Reaction of Paraffin Wax Based on BP Neural Network%基于BP神经网络的石蜡催化氧化反应的研究
Institute of Scientific and Technical Information of China (English)
黄玮; 丛玉凤; 郭大鹏
2012-01-01
The oxidized wax was prepared by catalytic oxidized reaction of paraffin wax which used BP neural network to build mathematical model of acid value and saponification value influenced by the amount of reactive catalyst and accessory ingredient, airflow rate, reaction temperature and time, and utilized the model of neutral network to calculate the technology condition of preparing oxidized wax through catalyzing and oxidizing paraffin wax. Consequently, optimum technology conditions were gained in order to achieve the objective of reducing experimental number of times.%在石蜡催化氧化反应制备氧化蜡的研究中,利用BP神经网络建立反应催化剂用量、助剂用量、空气流量、反应温度和反应时间对酸值和皂化值影响的数学模型,并利用该神经网络模型对石蜡催化氧化制备氧化蜡的工艺条件进行预测,从而获得最优工艺条件,达到缩短实验次数的目的.
Institute of Scientific and Technical Information of China (English)
范千; 许承权; 方绪华
2011-01-01
A novel model based on empirical mode decomposition ( EMD) and neural network for dam deformation prediction is presented in the paper. Firstly, considering that EMD has an advantage to do adaptive decomposition according to characteristics of the signal itself, deformation time series is decomposed into a series of intrinsic mode functions (IMF) in different scale space. Then, according to the change regulation of each IMF, they are forecasted by appropriate LM - BP neural networks. Finally, these forecasting results of each IMF are combined to obtain final forecasting result. The calculation result of a practical example shows that this model has higher forecasting precision and better adaptability.%提出一种基于经验模式分解(EMD)与LM-BP神经网络相结合的模型进行大坝变形预报的方法.先利用EMD具有根据信号本身特征进行自适应分解的功能将变形时间序列分解为一系列不同尺度的固有模式分量IMF,再根据各个IMF的变化规律采用相匹配的LM-BP模型进行预报,最后对各分量的预报值进行叠加得到最终的变形预报结果.实例分析表明,该方法具有较高的预测精度和较强的适应能力.
Institute of Scientific and Technical Information of China (English)
朱汝城; 王成勇; 王婉璐; 刘全坤
2011-01-01
依据A356咖啡机顶盖高压铸造特点,采用FEM仿真软件对铸件成型工艺进行数值模拟,以L16(45)正交试验和6个补充试验作为BP神经网络的训练样本,建立模具热应力与浇注温度、模具预热温度、压射比压、压铸速度四个压铸工艺参数的非线性映射关系.在所定的压铸工艺参数范围内,随机选取6组工艺参数组合,结合FEM模拟软件和已经训练好的BP网络,预测在不同工艺条件下模具的热疲劳趋势,为同类压铸件工艺参数的选择提供了参考.%According to the feature of high pressure diecasting of dome of Model A356 Coffee Machine,the diecasting process of coffee machine dome has been simulated by finite element simulate software.The L16 (45) -orthogonal experiments and six complementary experiments have been chosen as the trained samples of back propagation neural network for building up a non-linear mapping between thermal stress of diecasting die and each major processing parameters of diecasting as pouring temperature, die pre-heat temperature,injection pressure and injection speed. In the range of determined diecasting process parameters,6 groups of process parameters have been randomly selected. The trend of die thermal fatigue has been predicted by using well trained BP neural network and FEM simulation software, which could provide certain extent guidance on producing similar diecasting parts.
Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian － River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple－variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
Introduction to Artificial Neural Networks
DEFF Research Database (Denmark)
Larsen, Jan
1999-01-01
The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....
Applications of Neural Networks in Spinning Prediction
Institute of Scientific and Technical Information of China (English)
程文红; 陆凯
2003-01-01
The neural network spinning prediction model (BP and RBF Networks) trained by data from the mill can predict yarn qualities and spinning performance. The input parameters of the model are as follows: yarn count, diameter, hauteur, bundle strength, spinning draft, spinning speed, traveler number and twist.And the output parameters are: yarn evenness, thin places, tenacity and elongation, ends-down.Predicting results match the testing data well.
Institute of Scientific and Technical Information of China (English)
刘涛; 李永峰; 黄威
2012-01-01
In order to solve the problem of quantitative identification, BP neural network was implied to fit function to achieve quantitative identification. In pulsed thermography, the highest temperature difference and the best testing time was taken as input, and defect depth and diameter was taken as output, and then BP neural networks was used to fit the function relationship of them. Training samples of the neural network was provided by numerical method, and 30 times randomized computations were carried out. As the result analysis showed, the method had following characteristics: the precision of the calculation did not depend on network convergence speed; in network training process, the calculation precision error was not relative with whether calculation target was achieved; the calculation method had good stability. And then, according to distribution characteristics of calculation results, calculation method was designed to eliminate data with big calculating error. Finally, method of taking the average was taken to reduce the risk of a greater error, and to improve accuracy of calculation. As results show, in four parameters calculation, the biggest error is 3.32%, and the minimum error is 0.1%. And this proves that the method could be used to achieve defect quantitative identification calculation.%为实现红外热波检测对缺陷的定量识别,应用BP神经网络,拟合函数关系来实现定量识别.在脉冲热像中,以最佳检测时间和表面最大温差为输入量,以缺陷的深度和直径为输出量,利用BP神经网络拟合输入量与输出量之间的关系.借助数值计算的方法,提供样本训练神经网络,并进行了30次随机计算.通过结果分析,发现使用BP神经网络进行计算具备以下特点:网络收敛速度并不决定计算的精度；网络训练过程中,是否达到计算目标误差不会对计算精度带来较大影响；该方法具有较好的计算稳定性.针对计算结果分布特点,设计
BP神经网络在巴布剂涂布质量控制中的应用%Application of BP neural network on the quality control of cataplasm coating
Institute of Scientific and Technical Information of China (English)
马俊; 苏寒松; 任杏
2011-01-01
With regard to the automatic recognition control of cataplasm coating quality in the TDDS> a method is proposed to detect automatically the quality of the cataplasm coating with the BP neural network and the digital image recognition technology. The coating analog video signals scanned by the CCD camera, was converted by the video decoder into standard digital video signals, and then extract digital video signals feature value. The feature value processed by the cataplasm medicament thickness control closed-loop system, was sent to the 3-layer BP neural network for analysis, processing and recognition for achieving efficiently cataplasm coating medicament online testing and automatic control about uniformity of thickness and color as well as the possibility of air bubbles and wrinkles appear. Simulation experiment showed this method can achieved better results of thickness control and classification.%针对经皮给药系统( TDDS)中巴布剂涂布质量的自动识别控制问题,提出了1种利用BP神经网络和数字图像识别技术,对巴布剂质量进行自动检测的方法.对由CCD摄像头扫描的涂布模拟视频图像信号经视频解码器转换成标准数字视频信号,提取图像信号特征值.特征值经过巴布剂药剂厚度控制闭环系统处理,然后送入3层BP神经网络中进行分析处理和识别,从而实现对涂布药剂的厚度和颜色的均匀性以及有无气泡和折皱进行有效的在线检测和自动控制.仿真实验表明,该方法能较好的达到厚度控制和分类识别的效果.
Lambe, John; Moopen, Alexander; Thakoor, Anilkumar P.
1988-01-01
Memory based on neural network models content-addressable and fault-tolerant. System includes electronic equivalent of synaptic network; particular, matrix of programmable binary switching elements over which data distributed. Switches programmed in parallel by outputs of serial-input/parallel-output shift registers. Input and output terminals of bank of high-gain nonlinear amplifiers connected in nonlinear-feedback configuration by switches and by memory-prompting shift registers.
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Neural network fault diagnosis method optimization with rough set and genetic algorithms
Institute of Scientific and Technical Information of China (English)
SUN Hong-yan; XIE Zhi-jiang; OUYANG Qi
2006-01-01
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
BP人工神经网络在鱼糜挤压制品生产中的应用%Application of BP artificial neural network in extruded surimi product
Institute of Scientific and Technical Information of China (English)
张建友; 王嘉文; 吕飞; 丁玉庭
2012-01-01
采用反向传播（BP）人工神经网络和响应面法（RSM）模拟操作工艺参数（鱼糜含量、螺杆转速、III区加热温度）对双螺杆挤压机生产的鱼糜挤压制品的品质属性（持水性、膨润度、硬度和弹性）的影响,并比较了BP人工神经网络和RSM所建立的操作工艺参数与产品属性间关系模型的预测误差。试验结果表明,经训练的BP人工神经网络的模拟值和实际值的均方差（MSE）及和方差（SSE）均比RSM低,在模拟产品属性上具有更好的拟合度和准确性,采用此法确定的鱼糜挤压制品最佳工艺参数为：鱼糜含量45.70%,螺杆转速170r/min,III区温度106.2℃。%A back propagation （BP） artificial neural network （ANN） model was developed to predict the properties of extruded surimi products produced by a twin screw extruder. A BP-ANN model was established in MATLAB to simulate the relationships between running parameters of contents of surimi, screw speed and heating temperature of barrel III with the properties of surimi products such as water holding capability （WHC）, swelling degree （SD）, hardness （H） and springiness （S） during extrusion process. Using the experimental data from a quadratic general rotary unitized design, the neural network was trained and then validated with a validation subset. Besides, the method of response surface method （RSM） was also used to analyze and predict these properties. By comparing with mean squared error （MSE） and sum squared error （SSE） of BP-ANN and RSM models, it was showed that BP-ANN was more accurate than RSM in predicting the relationship between the responses and the running parameters. The BP-ANN was then used to search for a combination of running parameters resulting in maximal WHC, SD, S and minimal H. As a result, the optimal running parameters for content of surimi, screw speed and heating temperature was 45.70%, 170 r/min and 106.2 %, respectively.
Institute of Scientific and Technical Information of China (English)
吴秋芳; 王长辉; 唐亚勇
2013-01-01
作者以量价关系相关理论为基础，使用EGARCH模型和BP神经网络对中国股市的量价关系进行了实证研究．EGARCH模型的参数估计结果显示加入交易量的模型更优．然后作者得到了上证指数的如下量价关系特征：非预期成交量与股市波动性之间存在较强的正相关关系且我国股市收益率的波动存在明显的“杠杆效应”．因而加入交易量的BP模型具有更小的均方误差．%Based on the theory of the relationship between volume and price ,this article studys the rela-tionship between trading volume and price in China ’s stock market ,by using EGARCH model and BP neural network .The EGARCH model parameter estimation results show that the model with trading volume added in is better ,and we can receive some Shanghai Stock Index characteristics of relationship between volume and price ,and the positive correlation between unexpected trading volume and stock market volatility .The fluctuations of China’s stock market yield exist significant leverage effect .BP models with trading volume added in have smaller mean square errors .
Gupta, S; Gupta, Sanjay
2002-01-01
This paper initiates the study of quantum computing within the constraints of using a polylogarithmic ($O(\\log^k n), k\\geq 1$) number of qubits and a polylogarithmic number of computation steps. The current research in the literature has focussed on using a polynomial number of qubits. A new mathematical model of computation called \\emph{Quantum Neural Networks (QNNs)} is defined, building on Deutsch's model of quantum computational network. The model introduces a nonlinear and irreversible gate, similar to the speculative operator defined by Abrams and Lloyd. The precise dynamics of this operator are defined and while giving examples in which nonlinear Schr\\"{o}dinger's equations are applied, we speculate on its possible implementation. The many practical problems associated with the current model of quantum computing are alleviated in the new model. It is shown that QNNs of logarithmic size and constant depth have the same computational power as threshold circuits, which are used for modeling neural network...
Cargo throughput prediction of Luzhou Port Based on BP Neural Network%基于BP神经网络的泸州港货物吞吐量预测
Institute of Scientific and Technical Information of China (English)
唐飞
2015-01-01
Cargo throughput is an important index to reflect the port logistics demand.In the light of the historical data of the cargo throughput of Luzhou port,the prediction model of cargo throughput of Luzhou port were established by using BP neural network,and predicted the cargo throughput volume of Luzhou port for the next six years to provider reference for the design and development of Luzhou port.%货物吞吐量是反映港口物流需求的重要指标，根据泸州港货物吞吐量历史数据，运用BP神经网络构建泸州港货物吞吐量预测模型，预测出泸州港未来6年的货物吞吐量，从而为泸州港的规划和发展提供了决策依据。
Application of BP Neural Network in Packaging Cost Prediction of Chinese Liquor%BP神经网络在白酒包装成本预测中的应用
Institute of Scientific and Technical Information of China (English)
苏杰; 丁毅; 李国志
2011-01-01
The packaging cost of Chinese liquor differs from one to another in the market today. In order to estimate it, a BP neural network model was established for the packaging cost prediction of Chinese liquor based on MATLAB. According to the market research data, this paper established a prediction model in MAT LAB , and made use of procedures of MATLAB to realize training, simulation and verification of the model.This process can provide referent for packaging cost of Chinese liquor.%目前市场上白酒包装成本不一,为了估算白酒的包装成本,本文基于MATLAB构建白酒包装成本预测的BP神经网络模型,依据市场调查数据,在MATLAB中确立预测模型,从而实现预测模型的训练、仿真及验证,为白酒包装成本的确定提供决策方案.
Institute of Scientific and Technical Information of China (English)
李长荣; 赵浩文; 谢祥; 尹青
2011-01-01
BOF steelmaking is a very complex physical chemistry process; it is hard to achieve the target value of end-point by manual control. Multiple reblowing operations were usually necessary to taping off. Based on analyzing the influence major factors of phosphorus end-point in converter, the dominative factors of prediction model of endpoint for Conrerter smelting were fixed. A prediction model of end-point phosphorus content for BOF process is established based on Levenberg-Marquardt(LM) algorithm of BP neural network. The results show that the phosphorus content of end-point hitting rates could be reached 90％ if the accuracy of target error were ±0. 002％.%转炉炼钢过程是一个非常复杂的物理化学变化过程,人工控制很难一次达到终点目标值,通常需要经过多次补吹才能出钢.通过研究影响转炉冶炼终点磷含量的主要因素,确定了影响转炉终点磷含量的参数,建立了基于Levenberg-Marquardt(LM)算法BP神经网络转炉终点磷含量的预报模型.结果表明:在预报误差目标精度为土0.002%内,命中率达到了90%.
Institute of Scientific and Technical Information of China (English)
徐海成
2011-01-01
In the change-condition calculation of turbo-charger set, the problem which refers to repeat characteristic calculating of compressor according to input data have made the whole calculating process complicated and low efficiency.Applying BP neural network to working out the functional relation in the characteristic map of compressor, and then introduces it into Excel for the change-condition characteristic calculating of turbo-charger set.A given example showed that the efficiency and universality of the Excel for characteristic calculating of compressor is remarkable, and that can be expanded to the other place where the characteristic calculating of compressor involved.%在涡轮增压机组变工况热力计算中,因为涉及到需要依据输入数据反复计算压气机特性参数的问题,使得整个计算过程复杂繁琐、效率低下.应用BP神经网络求出压气机特性曲线的函数关系,并将其引入到Excel中,可以实现机组的变工况热力计算.实例表明,该计算表格的查值效率较高、通用性较好,可推广至其它涉及到压气机特性计算的地方.
Institute of Scientific and Technical Information of China (English)
朱宇明; 王宁; 杨晶
2012-01-01
Intelligent instnunent in industrial production and research are used more and more widely. In order to improve its performance so as to obtain a more accurate data, ensure the needs of industrial production and scientific research, the article puts forward that the BP neural network is applied to intelligent instrument on the original foundation, and to use its function approximation ability to simulate the input and output relationship so as to improve the data processing ability of intelligence apparatus for earning more accurate data.%智能仪器在工业生产和科研中应用越来越广泛，针对其性能的要求也随之提高。为了提高其性能以此来获取更精确的数据，保证工业生产和科研的需求，提出了在原有基础上将BP神经网络应用到智能仪器中，利用其函数逼近能力来模拟输入和输出的关系式，以此来提高智能仪器对数据的处理能力，同时获取更准确的数据。
A Stress Measurement and Compensation Model Based on PSO-BP Neural Network%基于PSO-BP神经网络的应力测量与补偿模型
Institute of Scientific and Technical Information of China (English)
郝纲; 庄毅
2015-01-01
For the problem that the stress distribution of flexible material is difficult to directly and accurately measure in the working process, this paper proposes a stress measurement and compensation model based on BP neural network with particle swarm optimization. In order to avoid trapping in local optimum, we use particle swarm optimization algorithm to optimize the model’ s initial weights and threshold in the process of the training of the model. Through the contrast experiment with flexible material’ s standard curve, effectiveness and accuracy of the model is verified when it is applied in stress measurement on the flexible fabric.%针对柔性材料在工作过程中受力情况难以直接并准确测量的问题，提出一种基于粒子群优化的BP神经网络应力测量与补偿模型。在模型的训练过程中，采用粒子群算法对模型中的初始权值和阈值进行优化，解决BP神经网络收敛速度慢的问题。通过与柔性材料标准曲线的对比实验，验证了该模型对柔性材料进行应力测量的有效性和准确性。
Researches on multimedia courseware evaluation based on BP neural network%基于BP神经网络的多媒体课件评价模型研究
Institute of Scientific and Technical Information of China (English)
杨妙妙; 赵葆华
2009-01-01
给出了一种评价多媒体课件效果的新方法.模型采用了基于认知心理学的评价指标,充分考虑了人的主体地位,同时运用BP神经网络的方法建立评价的数学模型.通过样本数据的训练、检验、输出结果的分析,表明该模型对课件效果评价结果与专家的评分结果基本一致.%This paper presents a new method for the evaluation of the multimedia courseware interface effect.A range of indexes based on cognitive theories combined with BP neural network method is designed to evaluate the performance of the multimedia courseware.Through training and checking up the sample data,the simulant results indicate that it is an effective method,and the results are consistent with experts' evaluation.
Institute of Scientific and Technical Information of China (English)
王学文; 魏彦凤; 单其帅; 王玉芬; 孙毅
2013-01-01
In order to solve the problem of poor result which is caused by the traditional forecasting methods on the real estate customer satisfaction, first, this paper established a customer satisfaction index system based on ACSI, combining with the industry characteristics of real estate. Second, a real estate satisfaction evaluation model was built based on BP neural network. Last, sample data would be learnt and practiced by MATLAB and a more efficient and practical methods would be provided for situation forecasting of real estate.%针对传统预测方法对房地产业顾客满意度预测效果差的问题，首先结合房地产业的行业特点，在ACSI的基础上建立顾客满意度指标体系。其次，构建基于BP神经网络的房地产业顾客满意度测评模型。最后，利用MATLAB软件对样本数据进行学习和训练，为房地产业顾客满意度测评提供一种更为有效和实用的方法。
Institute of Scientific and Technical Information of China (English)
彭源; 莫玉龙
2003-01-01
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using theback projection method.
企业市场营销策略组合的BP神经网络%BP neural network model for market sale strategy combination
Institute of Scientific and Technical Information of China (English)
曾旗; 刘明明; 徐君
2008-01-01
针对企业如何采取有效的市场营销组合策略来提高顾客忠诚度的问题,提出了基于BP(back-propagation)神经网络算法的企业市场营销组合策略分析方法;建立了基于4Ps理论的营销组合策略影响因素函数;通过对不同的营销组合策略影响因素进行加权、量化、各层之间权值的调整和迭代运算,最终构建了满足预定误差要求的BP神经网络模型;通过对十种手机品牌顾客忠诚度的实际调查值与网络模拟值相比较,得出了BP神经网络算法具有良好的模拟性,在此基础上证明了该方法在企业进行市场营销组合策略选择时具有良好的预见性和实用性.
Institute of Scientific and Technical Information of China (English)
黄立维; 符平; 张金接
2016-01-01
Grout density monitoring has important significance for the control and effect evaluation of grouting process in the process of grouting .Traditional differential pressure density sensor is influenced by the pressure fluctuation of the grouting pump ,the dynamic additional force ,the change of the fluid velocity and the low flow velocity ,and it may have a large error which means it is only suitable for stationary liquid and relatively stableslurry .At the same time ,if the water cement ratio is small and glueyness is high ,the monitoring results are not accurate enough ,in order to ensure the effective evaluation of the grouting process and the effect of grouting ,it is urgent to develop monitoring equipment with high accu-racy and applicability .In this paper ,in allusion to the defects of differential pressure density sensor ,considering factors such as pressure ,pressure loss and flow velocity ,combined with the density calculation of BP algorithm ,a differential pressure slurry density monitoring sensor with high accuracy and stability has been developed which can be applied to all kinds of slurry and process of grouting engineering .%灌浆过程中浆液密度监测对于灌浆过程控制和效果评价具有重要的意义。传统的差压式密度传感器受到灌浆泵的压力波动、调压阀调节引起的动态附加力、浆液流速的变化和低流速时水泥浆液析水沉淀等因素的影响可能存在较大的误差，适用于静止液体和流速相对稳定的浆液；同时对于水灰比小、黏稠度大的浆液监测的准确性也较差。为保证对灌浆过程及灌浆效果的有效评价，亟需一种密度监测精度高、适用性强的比重监测设备。针对传统差压式密度传感器的缺陷进行优化，考虑压力、压力损失及流速等因素的影响，结合BP算法进行密度计算，研发了基于神经网络的差压式浆液密度监测设备，具有较高准确性和稳定性，可适用于
Institute of Scientific and Technical Information of China (English)
曹勇; 李殿生; 朱景川; 刘勇; 来忠红
2011-01-01
对不同时效处理的3J33B马氏体时效钢进行硬度测试,获得了时效工艺（温度、时间）、硬度参数数据。利用BP人工神经网络建立起其关系网络模型。结果表明,所建立的网络可以很好地反映出材料的时效工艺-时效硬度之间的关系,网络模型可以用来预测不同时效条件下3J33B马氏体时效钢的时效硬度,并且利用粒子群优化,对3J33B马氏体时效钢的时效工艺进行优化,对实际生产具有有效的指导作用。%Parameters of processing（aging temperature,time） and aged hardness of maraging steel were obtained through mechanical properties examination,and their relationship network model was built by BP artificial neural network.The results show that the built model can reflect the relationships between the processing and the aged hardness very well and is certainly accurate.It can be used for predicting the properties of 3J33B steel under different aging process.Meanwhile,the optimized aging temperature and time can be obtained with particle swarm optimization.The model can serve as a guide for the aging treatment of maraging steel.
Institute of Scientific and Technical Information of China (English)
郎印海; 刘洁; 贾永刚; 崔文林
2011-01-01
Assessment index system of oil spill for the offshore oil platform was established for the first time by analyzing the influenced factors related to the degree of oil pollution. To solve the problem of non-samples, every assessment index was divided into several grades and the Rand function was used to generate enough training samples and test samples. A more reasonable network structure was established and a BP neural network model of the degree of oil pollution was finally set up. The results showed that the model had good generalization, and it not only could be used to evaluate unknown samples but also had a strong practical value.%通过分析与溢油污染程度有关的影响因素,首次构建了海上石油平台溢油污染程度评价指标体系.针对模型无样本的难题,对评价指标进行分级,利用Rand函数在各分级标准内随机生成足够数量的训练和测试样本,建立了较合理的网络结构,构建了石油平台溢油污染等级BP网络模型.研究结果表明BP网络模型具有很强的泛化能力,能够用于评判未知样本,具有较强的实用性.
BP Neural Network-Based Space Distance Cognition of Drivers at dusk%基于BP网络的驾驶员黄昏空间距离判识规律
Institute of Scientific and Technical Information of China (English)
赵炜华; 刘浩学; 陈昊
2012-01-01
In order to explore the laws of distances cognition variant with illumination decreasing in dusk, real road experiments were carried trough. Randomly selected 32 drivers percept absolute space distances and relative ones of red and green obstacles under different environment illumination and depth distance in the real road. Statistical methods was utilized to analyze cognition values and difference significance and character values. Variation results of cognition were simulated by BP neural network and the laws were studied. Results showed that there is significant difference between red and green obstacles. Variation trend can well be simulated by BP network. With the environment illumination decreasing absolute and relative distance cognitive values continuously augment. Absolute distance cognition values increase with depth distance increasing but relative ones increase a little.%为研究黄昏时段行车时,驾驶员对空间距离判识随环境照度的变化规律,进行实际道路试验.选用32名男性驾驶员,在不同自然环境照度下,判识不同空间深度距离时红、绿色障碍物的绝对距离和相对距离.统计分析被试判识结果,分析距离判识差异,获得空间距离判识特征值.运用BP神经网络,模拟距离判识结果,分析距离判识变化规律.结果表明:红、绿色障碍物黄昏时距离判识差异显著,BP网络可以很好拟合距离判识变化规律.绝对距离和相对距离判识结果,均随环境照度降低而增加；判识距离随深度距离增加也增加,相对距离增加较小.
Directory of Open Access Journals (Sweden)
Kapil Nahar
2012-12-01
Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.
Directory of Open Access Journals (Sweden)
Kapil Nahar
2012-12-01
Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems.Ann’s, like people, learn by example.
Neural networks for triggering
Energy Technology Data Exchange (ETDEWEB)
Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))
1990-01-01
Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.
基于BP神经网络人均猪肉需求量预测%Prediction of Per Capita Pork Demand Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
李双晶; 王福林
2014-01-01
近年来，由于猪肉安全事故和供求关系等因素的影响，导致猪肉市场价格波动大，养猪业难以持续稳定发展。科学预测猪肉需求量，对科学指导生猪生产和宏观调控猪肉市场价格意义重大。笔者根据历年数据，采用BP神经网络预测方法，实现在MATLAB中运行，通过对模型的多次训练，选择隐层神经元数目为6个，达到了期望效果。预测结果表明，2012~2016年，我国人均猪肉需求量分别为34.10、38.12、38.66、40.28和40.60 kg/a。%Now the pig industry in China is facing w ith many kinds of risks such as epidem ic disease, food security and m arket risk. Especially the m arketrisk influencesthe pig industry deeply, resulting in the pork price fluctuations.The scientific forecastofpork dem and can offer accurate inform ation on pork quantity dem anded, w hich is of significance to the macro-control of pig industry. A ccording to the related data over recent years, the back propagation neural netw ork m ethod is used in MATLAB to distinguish com plicated nonlinearity system characteristics, w ith high-speed self-study and self-adaption ability;and the per capita dem and for pork is 38.66kg, 40.27kg or 40.59kg in future three years,respectively.
Performance of Neural Networks Methods In Intrusion Detection
Energy Technology Data Exchange (ETDEWEB)
Dao, V N; Vemuri, R
2001-07-09
By accurately profiling the users via their unique attributes, it is possible to view the intrusion detection problem as a classification of authorized users and intruders. This paper demonstrates that artificial neural network (ANN) techniques can be used to solve this classification problem. Furthermore, the paper compares the performance of three neural networks methods in classifying authorized users and intruders using synthetically generated data. The three methods are the gradient descent back propagation (BP) with momentum, the conjugate gradient BP, and the quasi-Newton BP.
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Three-layer Adaptive Back-Propagation Neural Networks(TABPNN) are employed for the demodulation of spread spectrum signals in a multiple-access environment. A configuration employing three-layer adaptive Back-propagation neural networks is put forward for the demodulation of spread-spectrum signals in asynchronous Gaussian channels. The theoretical arguments and practical performance based on the neural networks are analyzed. The results show that whether the resistance to the multiple access interference or the robust to near-far effects, the proposed detector significantly outperforms not only the conventional detector but also the BP neural networks detector and is comparable to the optimum detector.
A Kind of BP Algorithm-Learning Wavelet Neural Network%一种基于BP算法学习的小波神经网络
Institute of Scientific and Technical Information of China (English)
陈哲; 冯天瑾; 陈刚
2001-01-01
This paper develops the Szu′s signal representation-based waveletneural network (WNN) and proposes a kind of multi-input-multi-out put WNN model. The sigmoid function and wavelet basis function satisfying the frame condition are employed as an activation function in the output and hidden la yer respectively, and the entropy error function is also used to accelerate the learning speed. The experimental results on parity problem and chaotic time seri es prediction demonstrated that this WNN has excellent functional approximation and generalization abilities, and the convergence speed and the mean-square-er ror (MSE) also show its superiority to a multilayer perceptron (MLP) model with the same architecture.%为发展Szu的基于信号表示的小波神经网络，提出一种多输入多输出的小波网络模型，网络隐层采用框架小波函数、输出层采用Sigmoid激励函数，并选用“熵误差函数”以加速网络的学习速度。奇偶判别和混沌时间序列预测例子的实验结果表明了它具有良好的函数逼近能力和推广能力，收敛速度和均方误差均优于相同结构的多层感知器模型。
Leg Amputees Pattern Recognition with BP Neural Network%BP神经网络大腿截肢者运动模式识别
Institute of Scientific and Technical Information of China (English)
刘磊; 杨鹏; 刘作军; 耿艳利
2014-01-01
在假肢运动优化控制的研究中，针对动力型假肢控制方面存在的运动模式识别准确性差的问题，搭建人体下肢运动信息系统获取下肢髋关节角速度信号和加速度信号。建立基于BP神经网络的大腿截肢者运动识别模型。研究了建模过程中输入输出数据预处理、网络结构设计、训练模式选择等问题。改进模型能有效识别平地行走、上楼、下楼、上坡和下坡5种运动模式，正确识别率达到了90.4%，已具备一定的实用性。%Lower limb amputation significantly affects the quality of the leg amputee's daily life. Recent advance-ments in electromechanical actuators have propelled the recent development of powered artificial legs. Accurately rec-ognizing the leg amputee's locomotion intent is required in order to realize the smooth and seamless control of prosthet-ic legs. The approach infers amputee's intents of upslope, downgrade, stairs ascent, stairs descent or level-ground walking without the need for instrumentation of the sound-side leg. Specifically, the intent recognizer utilizes the fea-tures extracted from accelerometer and gyroscope. The preprocessing of input and output data, design of network structure, training mode selection and other aspects were analyzed. This paper demonstrates via experiments the ef-fectiveness of the approach.
Villarreal, James A.; Shelton, Robert O.
1992-01-01
Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.
MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Ge Guangying; Chen Lili; Xu Jianjian
2005-01-01
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.
Institute of Scientific and Technical Information of China (English)
吕丹; 郑世跃; 欧阳勋志; 郭孝玉
2014-01-01
批量评估具有效率高、费用低且满足大量评估等优点。论文以中龄林为例，将BP神经网络应用于林木资源资产批量评估。通过比较学习算法、隐含层节点数，运用敏感性分析法确定影响因子对评估值的贡献程度，筛选输入层因子，从而优化了林木资源资产批量评估BP神经网络模型结构。结果表明：贝叶斯正则化法优于L-M算法；年龄、利率、蓄积、树种为强影响因子，这4个因子对评估值的贡献度超过60％；最优模型结构为BR 9-10-1，该模型平均绝对误差为32．46元/hm2，平均相对误差为1．28％，决定系数达0．9997，模型拟合精度高，泛化能力强，能够满足中龄林林木资源资产批量评估的要求。%Mass appraisal is of high efficiency,high precision,low cost,satisfies the needs of vast-amount evaluation.In this study,BP neural network was applied to mass appraisal of mid-age forest assets evaluation. By comparing different learning algorithms and the numbers of hidden layer nodes,selecting layer factors,using sensitivity analysis method which revealed the factors’ influence degree to the assessed value,the model struc-ture of BP neural network was optimized.The results showed that Bayesian regularization method was better than L-M algorithm;the contribution to the assessed values of the four factors including age,rate,accumula-tion,tree species was more than 60%;the best model structure was BR9-10-1.Its mean absolute error was 32.46 yuan/hm2 ,mean absolute percentage error was 1.28%,and decision coefficient was 0.999 7.The model has high fitting accuracy and generalization ability thus meets the requirement of mass appraisal of mid-age forest resource assets.
Instability Prediction of Slope Deformation Based on the BP Neural Network%基于BP神经网络的斜坡变形失稳预测研究
Institute of Scientific and Technical Information of China (English)
刘亚东
2014-01-01
本文在广泛的文献调研基础上，针对目前斜坡变形失稳研究中存在的主要问题提出改进方法，即分析斜坡变形时，考虑多种外在激发因素的作用。斜坡变形数据和激发因素难以用经验公式来描述他们之间的关系。本文在前人研究斜坡变形失稳预测预报方法的基础上，综合考虑斜坡位移数据和外在激发因素数据，利用 BP 神经网络对斜坡变形进行预测。通过具体实例表明：考虑激发因素的斜坡变形失稳预测，能较好的反映斜坡的变形趋势，并且精度较高，能够满足安全监测要求。%In this paper, on the basis of extensive literature re-search, in view of the present main problems in the study of slope de-formation and instability of improved method is put forward, namely the analysis of slope deformation, considering various extrinsic motiva-tors. Slope deformation data and motivating factor is difficult to use empirical formula describing the relationship between them. Based on previous study of slope deformation and instability prediction method based on considering the slope displacement data and extrinsic moti-vators data, using BP neural network model to forecast the slope defor-mation. Through specific examples show that the excitation factors of slope deformation and instability prediction, can better reflect the trend of slope deformation, and high precision, can satisfy the safety monitor-ing requirements.
Institute of Scientific and Technical Information of China (English)
李进贤; 莫文宾; 唐金兰
2011-01-01
固体火箭发动机中,药柱的结构完整性直接关系到发动机的结构完整性和可靠性,而推进剂的力学性能对保持药柱结构完整性起着重要作用,也是决定推进剂寿命的重要指标.为了预估固体推进剂的力学性能,提高系统的可靠性,将遗传算法和神经网络相结合,建立了预估固体推进剂力学性能的遗传神经网络(GA-BP)模型.利用模型预测了某固体推进剂在不同温度、湿度和时间下的抗拉强度、延伸率、弹性模量变化情况,并与试验结果进行了比较.结果表明,模型预估精度高,泛化能力强,仿真计算与试验在结果上有很好的一致性.从而为固体火箭发动机的结构完整性研究提供可靠依据.%Solid propellant grain structural integrity influences the structural integrity and reliability of solid rocket motor (SRM). Mechanical property of solid propellant plays an important role in grain structural integrity, which is critical criterion of solid propellant life. In order to predict mechanical property of solid propellant, a new mechanical property prediction model for solid propollant was established by means of combination of 8enetic algorithm with neural network (GA-BP). Using above model, the mechanical proporty of a solid propellant in conditions of different ternperature, humidity and time was predicted and compared with experiment results. The comparison results show high precision of the model and strong ability of generalization and with good consistency between prediction of model and experiment. The investigation provides reliable assistance for structural integrity research of SRM.
Institute of Scientific and Technical Information of China (English)
曾祥燕; 赵良忠; 蒋盛岩
2013-01-01
为提高槐花加工过程中芦丁的得率,以槐花为原料提取芦丁,采用正交试验设计收集试验数据,利用BP神经网络的自学习能力,通过仿真和评估,优化其提取工艺参数.结果表明:BP神经网络与正交实验方法相结合,能更好的利用已知信息,最佳仿真提取条件为:pH 9.50,乙醇体积分数60％,时间1.45 h,料液比1∶21,运用该提取条件用于实际研究中,测得实际得率为13.51％,试验结果优于正交试验,获得较为理想的目标值.%BP Neural Network (BPNN) combined with orthogonal array design was applied to optimize the extraction technology of rutin from Sophora japonica. The orthogonal testing data was used by applying BPNN. The experimental result showed that BPNN helper to increase the productivity of rutin. The optimized extraction conditions of rutin from Sophora japonica were as follows:pH 9.50,60% as the alcohol concentration, 1.45 h as the extraction time,and 1: 21 as the material/liquid ratio. The experiment was accomplished under optimized extraction conditions. The yield of rutin was 13.51%. In conclusion,BPNN provided a good technical basis for industrialization of the production of rutin.
Institute of Scientific and Technical Information of China (English)
韩震; 赵宁
2012-01-01
Using the LM-BP neural network and choosing the sea surface temperature, longitude, latitude and depth obtained from Argo data in 2007 as input parameters, the seawater temperature model of the Northwest Pacific Ocean was built. Using the root-mean-square error( RMSE) and the Pearson' s correlation coefficient (R) as test indices, the model was evaluated by the data in the period 2008 ~ 2009. The results were that the RMSE was 0.714 0 ℃ and R was 0.996 8 in 2008. The RMSE was 0.761 5 ℃ and R was 0.996 5 in 2009. lt shown this seawater temperature model was.%以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型.将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验.检验结果为:2008年均方根误差为0.7140℃,Pearson相关性系数为0.9968;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965.表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的.
Institute of Scientific and Technical Information of China (English)
李正学; 吴微; 高维东
2003-01-01
Some widely-used technical indexes of stock analysis are introduced as input of BP neural networks for the prediction of ups and downs of stock market, and better accuracy of prediction is achieved. A jump training strategy and three varying training ratio methods are used to accelerate the training iteration. An online prediction strategy is applied to monitor the training iteration procedure. The ratio of central distances of prediction examples is defined, in order to locate the un-stable prediction examples.%本文使用股市分析中常用的一些技术指标构造BP网络的输入样本向量,在此基础上,对沪市股指的涨跌进行了预测.数值实验结果表明,该方法能够提高网络预测的正确率.使用跳跃学习及三种变学习率、批方式的学习算法对BP网络进行了训练,节省了预测时间.运用"在线预测"的方法对预测过程进行了跟踪.针对预测样本在预测性能及预测结果方面存在的差异,引入预测样本中心距离比的概念对其进行简单的划分,得到一些富有启发性的结果.
Institute of Scientific and Technical Information of China (English)
卢红兵; 孔波; 钟科军
2011-01-01
为通过烟草香味成分评价其内在品质,采用水蒸气蒸馏法-气质联用法分析了38种烟叶样品的香味成分,评吸了其单料卷烟.并以30个烟叶样品作训练样本,8个样品作预测样本,采用遗传算法GA-BP神经网络法建立了烟草香味成分分析数据与其评吸得分的预测模型.结果表明:烟草样品中共鉴定出76种香味成分;通过GA法选择出28种与烟叶评吸总分显著相关的成分,由这些成分建立的GA-BP神经网络模型,其训练样本拟合误差＜2%,预测误差＜5%.%In order to evaluate the smoking quality of tobacco leaves by their aroma components, the aroma components in 38 tobacco leaf samples were determined by steam distillation and GC/MS, and the sensory quality of cigarettes made of each tested tobacco was evaluated by panel test.A predicting model was developed by the data of aroma components and sensory scores of tobacco samples with genetic algorithm (GA)-BP neural networks using 30 samples as a training set, and the other 8 samples served as a predicting set.The results showed that 76 aroma components were identified in the samples, of which 28 components significantly correlated with sensory score were selected by GA, the fitting errors of the established model were less than 2％ and its prediction errors were within 5％.
Institute of Scientific and Technical Information of China (English)
鲍珍珍; 朱沛
2013-01-01
According to the research results of the performance evaluation,combining the current situation of our nation’s third-party logistic companies with the chosen indicators,this paper aims to evaluate and analyze the comprehensive strength of the third-party logistic companies based on the BP neural network and AHP.Then we selected relevant data from several logistic companies and used them to have the evaluation done,which proves that our method is pragmatic and scientific and is able to provide important reference for the selection of the third-party logistic companies.%文中根据绩效评价体系的研究成果，结合我国第三方物流企业的现状，选取了适当的评价指标，并采用基于BP神经网络和层次分析法的评价方法，对第三方物流企业的综合实力进行评价分析。然后，收集了某几家物流企业的相关数据，对具体物流企业的综合实力进行评价，说明文中的评价方法具有实用性和科学性，对第三方物流企业的选择提供重要的参考。
Institute of Scientific and Technical Information of China (English)
詹裕河
2013-01-01
The paper analyzes the matching relation between HRM practices and organizational situation based on the BP neural network model, sets up the prediction model with good prediction effect. It found that the four main influencing factors of the HRM practice is the enterprise life cycle, regularization, vertical boundaries and level. And in the same organization situation, comparing the existing similar enterprises, the newly established enterprises are tend to be used in recruitment, training, performance appraisal, employee promotion four HRM practices, and are more likely to improve in the position channel, salary system, employee participation and communication.%本文通过BP神经网络模型来分析HRM实践与组织情境的匹配关系，建立了具有良好预测效果的预测模型。发现决定HRM实践的四个主要影响情境因素是企业生命周期、正规化、垂直边界、等级情况。并且在相同的组织情境下，对比已有的类似企业，新建立企业在招聘、培训、绩效考核、员工晋升四个HRM实践倾向于沿用，而对在职位通道、薪酬制度、员工参与、沟通方面则倾向于进行改良。
A Trust Evaluation Model for C2C E-Commerce Based on BP Neural Network%基于BP神经网络的C2C电子商务信任度评价模型
Institute of Scientific and Technical Information of China (English)
胡伟雄; 姜政军
2012-01-01
从卖家、网站、外部环境、网上信任等方面构建信任度评价指标体系；将影响网上信任的因素作为输入，将信任度综合得分作为输出，然后，运用BP神经网络技术，从买家的角度，构建一个C2C电子商务信任度评价模型。从实验来看，训练样本和检验样本的平均误差率和标准差均较低，模型的稳定性较好。因此，以此构建的C2C电子商务信任模型有很重要的价值，可以对信任度进行较为准确有效的评估。%First, the authors build the indicators according to the seller, the website and the external environment. Meanwhile, the factors affecting online trust are treated as input, and the trust composite score are treated as the output value. Then, the authors ap- plied BP neural network technology to build a trust evaluation model for C2C e-commerce from the perspective of the buyers. According to the experiment, the standard deviation and the average error rate of the training samples and test samples are low. Also, the stability of the model is well. Therefore, this C2C e-commerce trust model has very important value and can be used to assess trust accurately and effectively.
Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network
Institute of Scientific and Technical Information of China (English)
WU Xiao-guang; SHI Zhong-kun
2006-01-01
The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.
A Study on Turbo-rotor Multi-fault Diagnosis Based on a Neural Network
Institute of Scientific and Technical Information of China (English)
SUN Shou-qun; ZHAO San-xing; ZHANG Wei; CHANG Xin-long
2003-01-01
The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine. As it has many excellent qualities, the neural network might be used to solve the problems of multi-fault diagnosis of a turbo-rotor system. First, the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing. Then, the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propagation) neural network. According to the BP neural network problems, the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated. The results indicated that the parallel BP neural network method could solve the turbo-rotor multi-fault diagnosis problems.
Directory of Open Access Journals (Sweden)
J. Reyes-Reyes
2000-01-01
Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.
Metzler, R; Kinzel, W; Kanter, I
2000-08-01
Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random. PMID:11088736
Institute of Scientific and Technical Information of China (English)
吴婉娥; 朱左明; 帅领
2011-01-01
使用粒子群算法(particle swarm optimization,PSO)来优化误差反传(back propagation,BP)神经网络的权重和阈值,建立了粒子群神经网络(PSO-BP)计算模型,利用该模型对含硼富燃料推进剂的一次燃烧性能进行了模拟计算,当端羟基聚丁二烯(HTPB,28％ ～32％)、高氯酸铵(AP,30％ ～35％,重均粒径0.06 ～0.140 mm)、卡托辛(GFP,0％ ～5％)等重要影响因素变化时,计算了相应配方的燃速和压强指数,并与测试结果进行了比较.结果显示,模拟计算的燃速和压强指数相对偏差均小于±7％.%With particle swarm optimization ( PSO) optimizing biases and weights of back-propagation (BP) neural network,a simulation model for primary combustion characteristics of boron-based fuel-rich propellant based on PSO-BP neural network was established and validated, and then was used to predict primary combustion characteristics of boron-based fuel-rich propellant.
Institute of Scientific and Technical Information of China (English)
李永亮; 张怀清; 林辉
2012-01-01
利用便携式ASD野外光谱辐射仪对杉木冠层叶片光谱进行测定,同时以分光光度法对叶片叶绿素含量进行提取.样本经均值处理、平滑处理和微分处理后,进行红边参数提取.对11个红边参数以PCA方法进行降维,将得到的前7个主成分得分作为网络输入参数,叶绿素含量作为网络输出参数,以遗传算法(GA)优化网络初始权值阈值,建立隐含层神经元数分别为4,6,8,10,12和14的6种单隐层BP神经网络模型.以R2,RMSE和相对误差作为模型精度检验标准,结果表明:6种模型预测精度均可达到92.0％以上,其中隐含层神经元数为10时,预测精度最高,可达97.372％.说明此种模型可对杉木冠层叶片叶绿素含量进行高精度估算.%High-precision estimation model of arbor canopy chlorophyll content is important to forestry and ecology. The spectral reflectance of canopy was measured by ASD FieldSpec and the chlorophyll content was measured by spectrophotometry at the same time. The sample data were pretreated by the methods of mean, smoothing and derivative, and then the red edge parameters of samples were extracted from the pretreated spectra data. The eleven red edge parameters were analyzed with principal component analysis ( PCA). The anterior 7 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer which had four, six, eight, ten, twelve or fourteen neurons, while the chlorophyll content was used as the output variables of BP-ANN, and then the three layers BP-ANN discrimination model was built. Weight value and threshold value of this model were optimized by using genetic algorithm. The fitness between the predicted value and the measured value was tested by the determination coefficient, the lowest root mean-square error and the average relative error. The results show that the precisions of six models are all above 92. 0% and the
Heiden, Uwe
1980-01-01
The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica ted throughout the text. However, they are not explored in de tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be havior of neurons or neuron pools. In this respect the essay is writt...
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Chinese word sense disambiguation based on neural networks
Institute of Scientific and Technical Information of China (English)
LIU Ting; LU Zhi-mao; LANG Jun; LI Sheng
2005-01-01
The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to ( - M, + N). The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of M and N affect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90. 31% ,and 89. 62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.
Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks
Directory of Open Access Journals (Sweden)
Reza K. Moghadas
2008-01-01
Full Text Available Efficient neural networks models are trained to predict the maximum deflection of two-way on two-way grids with variable geometrical parameters (span and height as well as cross-sectional areas of the element groups. Backpropagation (BP and Radial Basis Function (RBF neural networks are employed for the mentioned purpose. The inputs of the neural networks are the length of the spans, L, the height, h and cross-sectional areas of the all groups, A and the outputs are maximum deflections of the corresponding double layer grids, respectively. The numerical results indicate that the RBF neural network is better than BP in terms of training time and performance generality.
Artificial neural networks in NDT
International Nuclear Information System (INIS)
Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)
Institute of Scientific and Technical Information of China (English)
曾珠; 王斌; 刘冬
2013-01-01
针对客户服务项目的不确定性,基于不可分辨关系的粗糙集理论和BP神经网络算法优良的分类映射能力,提出了面向细分客户群的基于粗糙BP神经网络客户群特征与服务项目映射模型.本文将分析客户特征,运用粗糙集理论进行客户特征约简、划分等价关系、建立BP神经网络的初始拓扑结构,运用K-means算法划分客户群.通过引入粗糙集理论,改进BP神经网络算法,加快BP网络收敛的速度和逃离局部极小值点,并利用rosetta软件和Matlab编程实现面向细分客户群的客户特征与服务项目映射模型.%Aimed at the uncertainty of customer service project,this paper,which based on rough sets and BP neural network and facing on segmented customer groups,present a mapping model of the customer base characteristics and service project,using the undiscerning relation in rough set theory and the excellent classification mapping capability of BP neural network algorithm.This paper analyzes characteristics of customer,reduces the customer characteristics by using rough set theory,divides the equivalence relations,establishes the initial topological structure of the BP neural network and divides the customer groups by using K-means algorithm.Through introducing rough set theory,this paper improves BP Neural Network Algorithm to accelerate the rate of BP network convergence and escape from local minimum points,and finally,by the use of Rosetta software and Matlab programming,completes the mapping model of customer base characteristics and service project facing on segmented customer groups.
Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo
2003-01-01
In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...
Institute of Scientific and Technical Information of China (English)
林婵; 王起峰; 朱良山
2013-01-01
湿球温度是电力工程中常用的气象设计参数,而目前气象站安装的地面气象自动观测设备中无湿球温度观测工具,且已有的湿球温度计算方法存在不足.为了满足工程设计需要,分析了湿球温度与干球温度、相对湿度、大气压强及平均风速等4个气象参数的非线性关系,建立了基于LM-BP神经网络的湿球温度计算模型,并将其应用于潍坊气象站湿球温度计算中.结果表明,该模型计算精度较高,且较为合理地反映了湿球温度与干球温度等影响因子之间复杂的非线性关系.%The wet-bulb temperature is a common weather parameter in power engineering design, but at present most ground automatic weather observation equipments installed in the weather station do not include the wet-bulb temperature observation equipment. The common wet-bulb temperature calculation methods have shortcomings. In order to meet the needs in engineering design, this paper analyzes the non-linear relationship between wet-bulb temperature and four meteorological parameters, which include dry-bulb temperature, relative humidity, atmospheric pressure and wind speed. And then it established a wet-bulb temperature calculation model based on Levenberg-Marquardt BP neural network. Finally, this model is applied to calculate wet-bulb temperature in Weifang weather station. The results show that the proposed model has high precision and it can well reflect the non-linear relationship between wet-bulb temperature and dry-bulb temperature.
Institute of Scientific and Technical Information of China (English)
郑丹平; 朱名日; 刘文彬; 姚鑫; 潘凯
2014-01-01
射频消融过程非常复杂，它的疗效影响因素多且关系复杂。在冷极射频消融仪治疗肿瘤过程中，射频输出功率和循环水泵转速起着重要作用。为扩大消融范围，达到一次性灭活肿瘤细胞，在治疗前，需选择适当的治疗参数。将BP神经网络模型引入射频消融中，建立冷极射频消融凝固灶预测的模型，并对效果进行检验。结果表明：检验样本中消融凝固灶与实际值的线性相关系数为0.988。针对消融横径，其相对误差的平均值为0.01。该模型对射频消融参数设置起到一定的支持作用，具有一定的实际参考价值。%The process of radiofrequency ablation is very complicated, there are many factors to influence the ef-fect and the relation is complex. The power output by RF and circulating pump speed plays an important role in the process of treatment by cooled-tip RFA. To expand the scope of ablation, the appropriate parameters should be se-lected to achieve one-time inactivated tumor cells before treatment. The BP neural network is introduced in the radiofrequency ablation. A model of coagulation zone prediction induced by cooled-tip radiofrequency ablation is built. The results show that the test sample correlation coefficient of linear ablation lesion and actual value is 0.988. For ablation diameter, the average value of the relative error is 0.01.The model plays a supporting role on parameter setting of radiofrequency ablation, which has some practical value.
基于纹理特征与BP神经网络的运动车辆识别%Motor Vehicle Identification Based on Texture Feature and BP Neural Network
Institute of Scientific and Technical Information of China (English)
张秀林; 王浩全; 刘玉; 安然
2013-01-01
在Gabor小波滤波器组与图像卷积值作为特征向量达到很高识别率的基础上,提出了一种特征值加权的Gabor小波纹理特征的提取方法.首先Gabor小波函数与纹理图像做卷积,然后加权处理尺度各不相同和方向各不相同的的卷积值,最后将均值和方差看作它们的特征向量,该方法使特征维数有所降低,并利用BP神经网络进行训练和仿真,实现运动车辆纹理图像的自动分类,达到运动图像的识别.实验结果表明此算法有效降低了图像的识别错误,增强了稳健性,对质量差的图像能够有效识别.%On the basis of the Gabor wavelet filter group and the image convolution values as the feature vector can achieve a high recognition rate,a feature-weighted method of extracting texture is proposed.Firstly,Gabor wavelet function and texture image deconvolution.Then,the convolution values are extracted in different scales and different directions.After making the weighting process,taking its mean and variance as the characteristic vector,which greatly reduces the feature dimension.Finally,BP neural network is used to making training and simulation,in order to achieving the automatic classification of texture images of moving vehicles and the identification of moving images.The experimental results show that this algorithm can effectively reduce the recognition error of the image and enhance the robustness.To the poor quality images,it can make the effective recognition.
Medical diagnosis using neural network
Kamruzzaman, S M; Siddiquee, Abu Bakar; Mazumder, Md Ehsanul Hoque
2010-01-01
This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural networ...
Institute of Scientific and Technical Information of China (English)
张崇欣; 李克民; 肖双双
2013-01-01
The equipment running time ,explosives consumption and shovel cycle time are selected to be the quantifiable argument ,and the production capacity of the system is selected to be the dependent variable to establish a multiple linear regression equation ,based on analyzing the factors affected the self-moving crusher system's production .The equation can predict system production .The BP neural network is established to adjust residuals of the multiple linear regression model ,used with the feature of the model in nonlinear fitting .The prediction accuracy is significantly improved .The error of multiple linear regression model is 7% ,and the error of the modified model is 1 .42% .%本文在对自移式破碎机系统生产能力影响因素分析的基础上，选取设备运行时间、炸药单耗和电铲作业周期时间作为可量化自变量，以系统生产能力为因变量建立多元线性回归方程，得到系统生产能力的预测模型。对多元线性回归模型预测结果的残差建立BP神经网络模型，利BP神经网络非线性拟合能力对残差进行调整。以某露天煤矿自移式破碎机系统生产数据为样本进行计算，多元线性回归模型预测误差为7％，修正后的模型预测误差为1．42％，预测精度显著提高。
Improved Marquardt Algorithm for Training Neural Networks for Chemical Process Modeling
Institute of Scientific and Technical Information of China (English)
吴建昱; 何小荣
2002-01-01
Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.
Rough Set Based Fuzzy Neural Network for Pattern Classification
Institute of Scientific and Technical Information of China (English)
李侃; 刘玉树
2003-01-01
A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.
A neural network method to evaluate consolidation coefficient
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Many methods to calculate the consolidation coefficient of soil depend on judgment of testing curves of consolidation,and the calculation result is influenced by artificial factors. In this work, based on the main principle of back propagation neural network, a neural network model to determine the consolidation coefficient is established. The essence of the method is to simulate a serial of compression ratio and time factor curves because the neural network is able to process the nonlinear problems. It is demonstrated that this BP model has high precision and fast convergence. Such method avoids artificial influence factor successfully and is adapted to computer processing.
FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM
Institute of Scientific and Technical Information of China (English)
Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang
2003-01-01
The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.
Institute of Scientific and Technical Information of China (English)
崔日鲜; 刘亚东; 付金东
2015-01-01
The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP)based artificial neural networks (ANN)method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass.Cano-py cover (CC)and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic).Correlation analysis was carried out to identify the relationship between CC,10 color indices and winter wheat above ground biomass.Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass.The results showed that CC,and two color indices had a significant cor-relation with above ground biomass.CC revealed the highest correlation with winter wheat above ground biomass.Stepwise mul-tiple linear regression model constituting CC and color indices of NDI and b,and BP based ANN model with four variables (CC, g,b and NDI)for input was constructed to estimate winter wheat above ground biomass.The validation results indicate that the model using BP based ANN method has a better performance with higher R 2 (0.903)and lower RMSE (61.706)and RRMSE (18.876)in comparation with the stepwise regression model.%建立基于冬小麦冠层图像分析获取的冠层覆盖度和色彩指数的地上部生物量估算模型，以促进作物冠层图像分析技术和 BP 神经网络技术在冬小麦长势无损监测中的应用。六个施氮水平的田间试验条件下，在冬小麦拔节期，分四次采集冬小麦冠层图像，同步进行破坏性取样，测定冬小麦地上部生物量；分析了通过图像分析软件（利用微软 Visual Basic
Institute of Scientific and Technical Information of China (English)
姚荣江; 杨劲松; 邹平; 刘广明
2009-01-01
Aiming at the complexity and spatial variability of the dynamic soil water and salinity in the saline region of the Lower Yellow River Delta, artificial neural network was introduced for modeling and prediction of soil water and salinity. Influence of the number of neurons in the hidden layer on training and forecasting was discussed for the three-layered network, and Back Propagation Neural Network (BPNN) models were established for modeling contents of water and salinity and their spatial distribution in the surface soil 0～20 cm in depth. Results indicate that the water and salinity in the surface soil was significantly correlated with soil bulk density and groundwater properties across the study area. For surface soil salinity, it is advisable to have the five variables, i.e. longitude and latitude of the site, soil bulk density, and depth and mineralization of groundwater cited as input vectors, while for soil moisture, the four variables, i.e. longitude, latitude, bulk density, and groundwater depth. An excessive number of neurons in the hidden layer would result in overfitting. Considering forecasting precision, the topological structure of the BP network was defined as 5∶ 8∶ 1 and 4∶ 6∶ 1 for salinity and moisture in the surface soil, respectively. Distribution maps of the observed surface soil water and salinity and their BPNN simulation displayed similarity in spatial pattern, and the BPNN effectively simulated contents of water and salinity and their spatial distribution in the surface soil with high accuracy. The findings of the study can serve as a theoretical basis for analyzing the occurrence, development and evolvement regularities of soil salinization in the Yellow River Delta, and provide a scientific basis for decision-making in regulating soil water and salt regulation and implementing scientific management of saline soils.%针对黄河下游三角洲盐渍区土壤水盐动态的复杂性和空间的变异性,将人工神经网络引
基于BP神经网络的作业场所风险预警模型研究%On a risk early-warning model for the workplace based on BP neural network
Institute of Scientific and Technical Information of China (English)
田彦清; 杨振宏; 张源勇; 番甜; 郑锐
2011-01-01
在全面分析作业场所职业危害风险影响因素的基础上,从人、物、环境、管理、安全技术、法制监管和社会经济利益等方面进行分析,建立了作业场所风险预警指标体系,建立了作业场所风险预警的BP神经网络模型.应用该模型分析多个企业作业场所职业危害的样本,对作业场所风险预警进行实证研究.结果表明,该模型精度较高,具有实用性和可行性,可用于作业场所职业危害风险预警.%In this paper, we have brought about a new risk early-warning model of workplace by using artificial neural network characteristic of highly nonlinear and complicated systems. The demand for such a kind of new model comes from the urgency to take necessary measures to guarantee the safety production activities and reduce occupational and professional diseases. As is well-known, invention or renovation of such a kind of early-warning system of occupational hazards at the workplace is actually affected by innumerous factors, though mainly the factors involving the control and management of human resources, machinery and facilities, environmental and managerial defects, safety measures, legal regulations and social economic benefits, etc. Based on the analysis of the above mentioned factors, we have developed a new workplace hazards early-warning model, including the analysis of the index system of workplace hazards early warning practice with the simulated data concerned and SPSS principal components. Furthermore, we have worked out the training and testing of this BP model to be checked by using the MATLAB software . And, next, we have established the hazards early-warning model of the workplace to make the early warning as accurately as possible. And, afterwards, when we trial-used this model to some sample enterprises, we have given the primary training and forecasting practice to the personnel involved. Actually, eight times of necessary training would be enough to reduce
Institute of Scientific and Technical Information of China (English)
荆涛; 李霖; 于文柱; 王玉娟; 郑永杰; 田景芝
2015-01-01
According to the date of PM2�5 mass concentration and the corresponding hourly meteorological factors, gas pollutant concentration from March to May in 2014 at Qiqihar University monitoring site, BP neural network model optimized by t⁃distribution controlled genetic algorithm ( BPM⁃TCG) was established. BPM⁃TCG was applied to simulate and predict PM2�5 mass concentration,and a comparative analysis was made between BPM⁃TCG and BP neural network model,BP neural network optimized by genetic algorithm ( BP⁃GA) . The experimental results showed that BPM⁃TCG possessed the highest precision and the best generalization ability. The accurate prediction of BPM⁃TCG model on PM2�5 mass concentration provides valuable reference for the prevention and control of PM2�5 pollution.%根据齐齐哈尔大学监测点2014年3—5月PM2�5质量浓度及其对应的每小时的气象因素、气体污染物浓度，建立基于t分布受控遗传算法的BP神经网络模型（ BPM⁃TCG），对PM2�5质量浓度进行模拟预测。并将其与BP神经网络模型、遗传算法优化BP神经网络模型（ BP⁃GA）进行对比分析。3种模型预测结果表明：BPM⁃TCG模型预测精度最高，泛化能力最好。 BPM⁃TCG模型对PM2�5质量浓度的准确预测为预防和控制PM2�5提供依据。
Neural networks and statistical learning
Du, Ke-Lin
2014-01-01
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...
Neural Networks Of VLSI Components
Eberhardt, Silvio P.
1991-01-01
Concept for design of electronic neural network calls for assembly of very-large-scale integrated (VLSI) circuits of few standard types. Each VLSI chip, which contains both analog and digital circuitry, used in modular or "building-block" fashion by interconnecting it in any of variety of ways with other chips. Feedforward neural network in typical situation operates under control of host computer and receives inputs from, and sends outputs to, other equipment.
What are artificial neural networks?
DEFF Research Database (Denmark)
Krogh, Anders
2008-01-01
Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...
Neural Networks for Fingerprint Recognition
Baldi, Pierre; Chauvin, Yves
1993-01-01
After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to...
Neural Networks and Photometric Redshifts
Tagliaferri, Roberto; Longo, Giuseppe; Andreon, Stefano; Capozziello, Salvatore; Donalek, Ciro; Giordano, Gerardo
2002-01-01
We present a neural network based approach to the determination of photometric redshift. The method was tested on the Sloan Digital Sky Survey Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some cases, better than SED template fitting techniques. Different neural networks architecture have been tested and the combination of a Multi Layer Perceptron with 1 hidden layer (22 neurons) operated in a Bayesian framework, with a Self Organizing Map used to estimate the accuracy...
Correlational Neural Networks.
Chandar, Sarath; Khapra, Mitesh M; Larochelle, Hugo; Ravindran, Balaraman
2016-02-01
Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches. PMID:26654210
Robotic velocity generation using neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The fast-paced nature of robotic soccer necessitates real-time sensing coupled with quick decision making and behaving. The robot must have high response-rate, exact motion ability, and must robust enough to confront interfere during drastic match. But during the match, we find that the robot usually do not act exactly as the commands from host computer. In this paper, we analyze the reason and present a method that uses BP neural network to output robotic velocity directly instead of conventional path-plan strategy, to reduce the error between actual motion and ideal plan.
Institute of Scientific and Technical Information of China (English)
刘红胜; 卢慧清
2011-01-01
The paper establishes a risk evaluation index system for lean supply chain collaboration and evaluates the risks exposed to manufacturing enterprises in their lean supply chain collaboration using BP neural network.%建立了精益供应链协同风险评价指标体系,并运用BP神经网络方法进行协同风险评价,为制造企业精益供应链协同风险的评价提供理论指导.
Institute of Scientific and Technical Information of China (English)
刘明军; 李恒堂; 姜在炳
2011-01-01
Based on the analysis of features of Genetic Algorithm (GA) and Back-Propagation Algorithm (BP), it is concluded that the disadvantage of BP algorithm includes large identification specimen in inversion, so a methodology to optimize BP network structure and link weights with GA is proposed and the lithology identification model based on GA optimized BP algorithm is established. Using the basic data from Binchang mining area, the lithology identification function is tested, and the result indicates that the GA-BP neural network model has good identification speed and accuracy.%为提高测井岩性识别的自动化程度和地质解释精度,在分析遗传算法(Genetic Algorithm,简称GA)与误差反向传播算法(Back- Propagation,简称BP)各自特性的基础上,针对BP算法在反演中测井数据识别样本大以及BP算法本身存在的缺陷,提出了利用GA算法来同时优化BP神经网络的结构和连接权值的解决方案,建立了基于GA优化BP神经网络的测井数据岩性识别模型.该模型通过彬长矿区实际数据的检验,获得了较高的识别速度和准确率.
Institute of Scientific and Technical Information of China (English)
周祥; 何小荣; 陈丙珍
2004-01-01
Because of the powerful mapping ability, back propagation neural network (BP-NN) has been employed in computer-aided product design (CAPD) to establish the property prediction model. The backward problem in CAPD is to search for the appropriate structure or composition of the product with desired property, which is an optimization problem. In this paper, a global optimization method of using the α BB algorithm to solve the backward problem is presented. In particular, a convex lower bounding function is constructed for the objective function formulated with BP-NN model, and the calculation of the key parameter α is implemented by recurring to the interval Hessian matrix of the objective function. Two case studies involving the design of dopamine β-hydroxylase (DβH) inhibitors and linear low density polyethylene (LLDPE) nano composites are investigated using the proposed method.
Applying Neural Network in Evaporative Cooler Performance Prediction
Institute of Scientific and Technical Information of China (English)
QIANG Tian-wei; SHEN Heng-gen; HUANG Xiang; XUAN Yong-mei
2007-01-01
The back-propagation (BP) neural network is created to predict the performance of a direct evaporative cooling (DEC) air conditioner with GLASdek pads. The experiment data about the performance of the DEC air conditioner are obtained. Some experiment data are used to train the network until these data can approximate a function, then, simulate the network with the remanent data. The predicted result shows satisfying effects.
Institute of Scientific and Technical Information of China (English)
李伟; 何鹏举; 杨恒; 陈明
2012-01-01
针对BP神经网络结构由于特征维数增多变得复杂,以及网络易陷入局部极值点,提出了粗糙集和改进遗传算法结合共同优化神经网络的方法.首先利用粗糙集对样本空间进行属性约简,降低特征维数,进而简化BP神经网络的结构;然后训练过程中先用改进的遗传算法全局搜索网络的权值和阀值,再使用BP算法局部搜索细化,避免网络过早收敛.试验分析证明优化后BP神经网络比传统BP网络的预测精度得到了极大提高,泛化能力得到了增强,说明了该方法的可行性、有效性.%Considering that the BP neural network became complex due to the increase of the sample dimension and it fell easily into local maximums or minimums, we combined genetic algorithm and rough set to optimize the BP neural network. Sections 1 through 3 explain our backpropagation algorithm mentioned in the title, which we believe is effective and whose core consists of; (1) rough set was applied to simplify the network by reducing the attribute dimension; (2) modified genetic algorithm was used to globally search the weights and bios and, further, the BP algorithm was to locally optimize them to avoid the network falling into the local extremes. Simulation results, presented in Fig. 1 and Table 2 in subsection 3. 4, and their analysis indicated preliminarily that prediction accuracy was increased greatly over that of the traditional BP neural network and that generalization was enhanced, thus showing that our backpropagation algorithm is indeed effective.
Complex-Valued Neural Networks
Hirose, Akira
2012-01-01
This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...
Institute of Scientific and Technical Information of China (English)
赵璇; 张强
2013-01-01
The construction industry is the important material production sectors. It plays a crucial role on the development of national economy and improvement of people's lives. The development of Chinese building industry has made great contributions to the growth of the national economy. Trends of the construction industry will directly influence on the GNP and the capital from the international market. The construction industry which is labor intensive provides a lot of employment opportunities, from this per-spective, the well-developed construction industry is very significant to social stability. Therefore, it is necessary and valuable to conduct a research on the development of the construction industry. In this paper we will use the Cobb-Douglas production func-tion and BP neural network to predict the development of Chinese building industry, besides, Matlab will also play an important role in modeling and function fitting. Based on the analysis, research and forecasting data of Chinese building industry in 2005-2011, we find out the value of Chinese building industry in 2012. This paper provides the basis and foundation for the simulation prediction of Chinese building industry.% 建筑业是我国国民经济的重要物质生产部门，它与发展国民经济、改善人民生活的质量有着密切的关系。我国建筑业的发展为国民经济高速增长做出了重要贡献，建筑业的发展趋势将直接影响到国民生产总值及国际市场对我国建筑业的资本投放。建筑业是劳动密集型行业，提供了大量的就业机会，从这个角度讲，建筑行业发展良好与否对社会稳定有十分重要的意义。因此，对建筑行业的发展进行研究是非常有必要，有价值的。本文将利用柯布-道格拉斯生产函数和BP神经网络对我国建筑业的发展趋势做预测研究，借助Matlab实现建模和函数拟合。通过对2005-2011年我国建筑行业的数据进行分析
Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model
Institute of Scientific and Technical Information of China (English)
2011-01-01
Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.
Institute of Scientific and Technical Information of China (English)
LI Zuoyong; PENG Lihong
2004-01-01
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.
Phase Transitions of Neural Networks
Kinzel, Wolfgang
1997-01-01
The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: Perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayesian estimate, on-line training, noise estimation and time series generation.
Institute of Scientific and Technical Information of China (English)
师帅; 庞金波; 王刚毅
2014-01-01
农业温室气体排放量逐渐增多，日益受到各国重视。低碳经济是倡导低能耗、低污染、低排放的经济模式，农业作为国民经济的基础产业，兼具碳源和碳汇功能，平衡农业生产碳源排放和碳汇量可以促进农业协调发展和可持续发展。基于低碳经济下区域农业协调发展的运行机理，运用 BP 神经网络方法构建低碳经济下区域农业协调发展评价模型，并采用我国区域农业面板数据为例进行实证研究。研究表明，低碳经济视角下区域农业经济发展水平与资源环境协调水平呈反向变化，我国经济发展较好的区域，资源环境协调度较差，而资源环境禀赋优良区域的经济发展较慢，社会协调度间差异较小。从创新农业技术减少碳源，合理布局农业生产增加碳汇，推动农业碳排放权交易增加农民收入等方面可提高低碳经济下区域农业协调发展的程度。%There is a growing world wide concern on the increasing agriculturalgreenhouse gas emission.In response, low-carbon economy is promoted as the mode of lowenergy consumption, low pollution and low emission. As the foundation ofnationaleconomy,agriculture both emits and absorbs carbon.To balance their volume is importantfor a coordinated and sustainable development in agriculture. This paper presents the mechanismof regional agricultural coordination in a framework of low-carbon economy.We develop an evaluation model based on BP neural network method and conductempiricalstudy with paneldata in China.Studies showa negative relationship between the levelof agriculturaldevelopmentand resource and environmental coordination.The better the regionaleconomy develops,the poorerresource and environment coordination is.In comparison,the better resource and environmental coordination is, the slowerregionaleconomy develops.To improve regionalagriculture coordination in the framework ofa low-carbon economy, we need
Multigradient for Neural Networks for Equalizers
Directory of Open Access Journals (Sweden)
Chulhee Lee
2003-06-01
Full Text Available Recently, a new training algorithm, multigradient, has been published for neural networks and it is reported that the multigradient outperforms the backpropagation when neural networks are used as a classifier. When neural networks are used as an equalizer in communications, they can be viewed as a classifier. In this paper, we apply the multigradient algorithm to train the neural networks that are used as equalizers. Experiments show that the neural networks trained using the multigradient noticeably outperforms the neural networks trained by the backpropagation.
On the identification of quark and gluon jets using artificial neural network method
International Nuclear Information System (INIS)
The identification of quark and gluon jets produced in e+e- collisions using the artificial neural network method is addressed. The structure and the learning algorithm of the BP (Back Propagation) neural network model is studied. Three characteristic parameters--the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are inputted to the BP network for repeated training. The learning process is ended when the output error of the neural network is less than a preset precision (σ=0.005). The same training routine is repeated in each of the 8 energy bins ranging from 2.5-22.5 GeV, respectively. The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples,getting from the non-symmetric three-jet events produced by the Monte Carlo generator JETSET 7.4. Then the pattern recognition of the mixed sample getting from the combination of the quark and gluon jet samples is carried out through applying the trained BP neural network. It turns out that the purities of the identified quark and gluon jets are around 75%-85%, showing that the artificial neural network is effective and practical in jet analysis. It is hopeful to use the further improved BP neural network to study the experimental data of high energy e+e- collisions. (author)
Video Compression Using Neural Network
Directory of Open Access Journals (Sweden)
Sangeeta Mishra
2012-08-01
Full Text Available Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding. Successful applications of neural networks to basic propagation algorithm have now become well established and other aspects of neural network involvement in this technology. In this paper different algorithms were implemented like gradient descent back propagation, gradient descent with momentum back propagation, gradient descent with adaptive learning back propagation, gradient descent with momentum and adaptive learning back propagation and Levenberg-Marquardt algorithm. The size of original video clip is 25MB and after compression it becomes 21.3MB giving the compression ratio as 85.2% and compression factor of 1.174. It was observed that the size remains same after compression but the difference is in the clarity.
Relations Between Wavelet Network and Feedforward Neural Network
Institute of Scientific and Technical Information of China (English)
刘志刚; 何正友; 钱清泉
2002-01-01
A comparison of construction forms and base functions is made between feedforward neural network and wavelet network. The relations between them are studied from the constructions of wavelet functions or dilation functions in wavelet network by different activation functions in feedforward neural network. It is concluded that some wavelet function is equal to the linear combination of several neurons in feedforward neural network.
Facial expression recognition using constructive neural networks
Ma, Liying; Khorasani, Khashayar
2001-08-01
The computer-based recognition of facial expressions has been an active area of research for quite a long time. The ultimate goal is to realize intelligent and transparent communications between human beings and machines. The neural network (NN) based recognition methods have been found to be particularly promising, since NN is capable of implementing mapping from the feature space of face images to the facial expression space. However, finding a proper network size has always been a frustrating and time consuming experience for NN developers. In this paper, we propose to use the constructive one-hidden-layer feed forward neural networks (OHL-FNNs) to overcome this problem. The constructive OHL-FNN will obtain in a systematic way a proper network size which is required by the complexity of the problem being considered. Furthermore, the computational cost involved in network training can be considerably reduced when compared to standard back- propagation (BP) based FNNs. In our proposed technique, the 2-dimensional discrete cosine transform (2-D DCT) is applied over the entire difference face image for extracting relevant features for recognition purpose. The lower- frequency 2-D DCT coefficients obtained are then used to train a constructive OHL-FNN. An input-side pruning technique previously proposed by the authors is also incorporated into the constructive OHL-FNN. An input-side pruning technique previously proposed by the authors is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having 5 facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images are used for generalization and
Research on Feasibilityof Top-Coal Caving Based on Neural Network Technique
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on the neural network technique, this paper proposes a BP neural network model which integratesgeological factors which affect top-coal caving in a comprehensive index. The index of top-coal caving may be usedto forecast the mining cost of working faces, which shows the model's potential prospect of applications.
Application of Artificial Neural Network in Active Vibration Control of Diesel Engine
Institute of Scientific and Technical Information of China (English)
SUN Cheng-shun; ZHANG Jian-wu
2005-01-01
Artificial Neural Network (ANN) is applied to diesel twostage vibration isolating system and an AVC (Active Vibration Control) system is developed. Both identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.
Ocean wave forecasting using recurrent neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Neural Networks for Flight Control
Jorgensen, Charles C.
1996-01-01
Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential.However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and ...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile; Beale, R
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and...
Building a Chaotic Proved Neural Network
Bahi, Jacques M; Salomon, Michel
2011-01-01
Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to make these iterations behave chaotically, as defined by Devaney, and thus we obtain the first neural networks proven chaotic. Several neural networks with different architectures are trained to exhibit a chaotical behavior.
Grinding Parameter Intelligent Prediction Model Based on BP Neural Network%基于神经网络的磨削工艺参数智能预测模型
Institute of Scientific and Technical Information of China (English)
刘伟强; 杨建国
2013-01-01
磨削参数的合理选择对于磨削加工过程有着重要的影响,将人工智能运用到磨削工艺参数的选择过程中是现代发展的一个新趋势.在分析现有的智能算法后,提出了一种利用BP神经网络模型来确定磨削参数的方法.在该方法中综合考虑影响磨削加工的因素,把它们列为神经网络系统的输入参数,并对输入参数进行编码；同时也对输出参数(砂轮速度、工件速度、磨削深度、磨削进给速度)进行了归一化处理以适应神经网络的学习.采用循环算法比较得出隐层的最优神经元个数,从而最终建立了磨削参数智能预测模型,并利用Matlab进行仿真预测,仿真结果表明该预测模型准确率很高,能为磨削参数的选择提供可靠数据.%The reasonable selection of grinding parameters plays an important role in grinding process.Combine artificial intelligence with the selection of grinding process parameters is a new trend in the modern development.After analyzing the existing intelligent algorithm,put forward a new method that using artificial neural network model to determine the grinding parameters.Considerating the influence factors of grinding comprehensively,and listing them as neural network input parameters which are encoded.At the same time make the output parameters (wheel speed,workpiece speed,grinding depth,grinding feed rate) on the normalized in order to adapt to the neural network learning.Using cyclic algorithm for optimal number of neurons in the hidden layers,and eventually established the grinding parameters intelligent prediction model Using matlab to simulate it,the simulation results show that the prediction model has high accuracy,and can provide reliable data for the selection of grinding parameters.
Institute of Scientific and Technical Information of China (English)
张飞; 耿红琴
2014-01-01
清洁机器人的移动定位是个复杂的非线性定位的问题，精密机械结构与路径规划无法补偿定位不精确造成的移动误差，提出一种基于异质 RBF神经网络信息融合的清洁机器人定位技术，设计了智能机器人的控制系统、移动系统和感知系统，设计多个位姿传感器后，实时采集位置信息，在主控芯片中使用粒子群优化神经网络技术对多传感器的信息进行融合，计算清洁机器人的位置信息，解决了位置因素非线性强，定位误差大的问题，并且有效提高了神经网络的局部收敛能力；使用机器人多传感器的实验平台测试证明，这种方法下清洁机器人的移动中定位准确率较传统方法提高13％，具有很强的可靠性与实用性。%The orientation of the movement of the Clean robot is a complex nonlinear problem,structure and path planning can not com-pensate positioning precision machinery movement error caused by inaccurate,put forward a kind of cleaning robot localization based on het-erogeneous RBF neural network information fusion technology,intelligent robot control system is designed and perception system,moving system,design more bits after posture sensor,real-time collecting location information,in the main control chip,using particle swarm opti-mization neural network technology of multi-sensor information fusion,the calculation of the cleaning robot position information,to solve the nonlinear strong location factors,the problem of large positioning error,and effectively improve the local convergence ability of neural network.Using robot multi-sensor experimental platform to test proved that under this kind of method the clean mobile robot positioning accuracy increased by 13%than the traditional methods,have very strong reliability and practicability.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Institute of Scientific and Technical Information of China (English)
蔡定葆; 张群莉; Mykola Anyakin; Ruslan Zhuk; 任博; 姚建华
2013-01-01
为了进一步提高Ti-6Al-4V的性能,以满足其在工程中更广泛的运用,研究了在Ti-6Al-4V激光NiAl-VC合金化的工艺.以改变激光功率、激光扫描速度和粉末质量含量比例进行了工艺实验,采用BP神经网络(BP-NN)算法,建立了合金化层性能与工艺参数之间的关系模型,并通过验证实验表明预测效果良好,具有可行性.采用BP-NN算法进行了模拟实验,分析了不同工艺参数条件对合金化层深度、宽度、平均硬度、最高硬度的影响规律.本研究对Ti-6Al-4V激光NiAl-VC合金化的实践应用具有指导意义和参考价值.%In order to further improve the performance of Ti-6A1-4V, this paper makes it meet the more widely used in engineering, then trying to study the laser alloying process on Ti-6A1-4V surface using NiAl-VC powder. Experiments with the change of laser power and scanning speed and the mass ratio of powder content were preceded. The relation model between alloyed layer performances and process parameters was established by using BP neural network (BP-NN) algorithm. Experimental verification shows that, the prediction effect is nice and relatively feasible. The influence of different process parameters on the properties, such as depth, width, average hardness and maximum hardness of the alloyed layer, was analyzed by the simulation based on BP neural network algorithm. This research embodies guiding significance and reference value to the application of the laser alloying process on Ti-6A1-4V surface in the industrial manufacturing.
Neural Network based Consumption Forecasting
DEFF Research Database (Denmark)
Madsen, Per Printz
2016-01-01
This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...
Artificial neural networks in medicine
Energy Technology Data Exchange (ETDEWEB)
Keller, P.E.
1994-07-01
This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.
Medical Imaging with Neural Networks
International Nuclear Information System (INIS)
The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan; Berks, G.;
2000-01-01
A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...
Model Of Neural Network With Creative Dynamics
Zak, Michail; Barhen, Jacob
1993-01-01
Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Simplified LQG Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...
Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions
International Nuclear Information System (INIS)
Highlights: • Four hybrid algorithms are proposed for the wind speed decomposition. • Adaboost algorithm is adopted to provide a hybrid training framework. • MLP neural networks are built to do the forecasting computation. • Four important network training algorithms are included in the MLP networks. • All the proposed hybrid algorithms are suitable for the wind speed predictions. - Abstract: The technology of wind speed prediction is important to guarantee the safety of wind power utilization. In this paper, four different hybrid methods are proposed for the high-precision multi-step wind speed predictions based on the Adaboost (Adaptive Boosting) algorithm and the MLP (Multilayer Perceptron) neural networks. In the hybrid Adaboost–MLP forecasting architecture, four important algorithms are adopted for the training and modeling of the MLP neural networks, including GD-ALR-BP algorithm, GDM-ALR-BP algorithm, CG-BP-FR algorithm and BFGS algorithm. The aim of the study is to investigate the promoted forecasting percentages of the MLP neural networks by the Adaboost algorithm’ optimization under various training algorithms. The hybrid models in the performance comparison include Adaboost–GD-ALR-BP–MLP, Adaboost–GDM-ALR-BP–MLP, Adaboost–CG-BP-FR–MLP, Adaboost–BFGS–MLP, GD-ALR-BP–MLP, GDM-ALR-BP–MLP, CG-BP-FR–MLP and BFGS–MLP. Two experimental results show that: (1) the proposed hybrid Adaboost–MLP forecasting architecture is effective for the wind speed predictions; (2) the Adaboost algorithm has promoted the forecasting performance of the MLP neural networks considerably; (3) among the proposed Adaboost–MLP forecasting models, the Adaboost–CG-BP-FR–MLP model has the best performance; and (4) the improved percentages of the MLP neural networks by the Adaboost algorithm decrease step by step with the following sequence of training algorithms as: GD-ALR-BP, GDM-ALR-BP, CG-BP-FR and BFGS
Institute of Scientific and Technical Information of China (English)
刘勇; 杨莉; 彭振仁; 黄开勇
2013-01-01
目的 构建广西道路交通事故BP人工神经网络预测模型,为研究广西道路交通事故提供新方法.方法 在分析道路交通事故与人、车、路等因素关系的基础上,选取人口数、客运周转量、民用车辆拥有量和公里里程数作为输入变量,交通事故发生数作为输出变量,应用BP人工神经网络技术,对2010年广西道路交通发生数进行预测.结果 2010年广西交通事故预测数为4 562次,实际发现4 351次,预测值与实际值误差为4.85%,建立的模型拟合效果较好.结论 BP人工神经网络模型适用于广西交通事故数的预测,为交通部门进行交通事故预测研究提供新方法.%Objective To construct the prediction model of road traffic accidents in Guangxi by BP neural network, and to provide a new method for studying road traffic accidents. Methods Based on the analysis of the relation between road traffic accidents and factors,including human,vehicle and road,the predicting model of road traffic accidents, which used population,passenger turnover,number of civilian vehicles and Km mileage as the input neurons and the road traffic accidents as the output neuron, was established by BP neural network to predict the road traffic accidents of Guangxi in 2010. Results The predicted value and actual one in 2010 for the road traffic accidents were 4 562 and 4 351,respectively, and the percentage of error was 4. 85%. The fitting of the model established was more effective. Conclusion The predicting model established by BP neural network is suited for predicting road traffic accidents in Guangxi, and it has provided a new method for traffic department.
Institute of Scientific and Technical Information of China (English)
潘刚; 梁玉英; 贾占强; 张国龙
2012-01-01
针对基于仿真加速寿命试验仿真规模较大的问题,采用定时等间隔监测的方法对定时截尾失效时间进行控制,同时运用最小二乘支持向量机(LS-SVM)理论和基于遗传算法的BP神经网络理论(GA-BP神经网络)用于目标函数拟合,建立了基于LS-SVM和GA-BP神经网络拟合的加速寿命仿真试验优化方案设计的数学模型;最后通过算例进行验证且与文献[2]相比,在保证试验数据统计精度的前提下,得到了最大截尾时间范围为1000～1500小时的最优化结果,仿真规模降低了2/3,提高了试验优化效率.%Aim at the greater problem based on the emulation acceleration scale of life test . we take the method of time interval test, to control the biggest cut-off time of time censored test and take use of the least square support vector machine (LS-SVM) theory and the BP neural network theory based on genetic algorithm (GA-BP neural network) for target function fitting . and establish the mathematics model based on the LS-SVM and GA-BP neural network fitting about the acceleration life emulation test optimization scheme design . Finally, validate through calculating example , and compared with the literature [2], while ensuring data to test the statistical precision , got the optimization result with biggest censoring time with 1000~1500 hours, and the emulation scale reduced 2/3, improve the efficiency of the test.
A Robust Digital Watermark Extracting Method Based on Neural Network
Institute of Scientific and Technical Information of China (English)
GUOLihua; YANGShutang; LIJianhua
2003-01-01
Since watermark removal software, such as StirMark, has succeeded in washing watermarks away for most of the known watermarking systems, it is necessary to improve the robustness of watermarking systems. A watermark extracting method based on the error Back propagation (BP) neural network is presented in this paper, which can efficiently improve the robustness of watermarking systems. Experiments show that even if the watermarking systems are attacked by the StirMark software, the extracting method based on neural network can still efficiently extract the whole watermark information.
Neural Networks and Photometric Redshifts
Tagliaferri, R; Andreon, S; Capozziello, S; Donalek, C; Giordano, G; Tagliaferri, Roberto; Longo, Giuseppe; Andreon, Stefano; Capozziello, Salvatore; Donalek, Ciro; Giordano, Gerardo
2002-01-01
We present a neural network based approach to the determination of photometric redshift. The method was tested on the Sloan Digital Sky Survey Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some cases, better than SED template fitting techniques. Different neural networks architecture have been tested and the combination of a Multi Layer Perceptron with 1 hidden layer (22 neurons) operated in a Bayesian framework, with a Self Organizing Map used to estimate the accuracy of the results, turned out to be the most effective. In the best experiment, the implemented network reached an accuracy of 0.020 (interquartile error) in the range 0
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Institute of Scientific and Technical Information of China (English)
黄春晖; 温永杰
2014-01-01
在分析宽频带CMMB直放站高功率功放(HPA）特性的基础上，提出了一种可分离处理功放记忆效应和非线性的延时神经网络（ FIR-NLNNN ）模型。该模型以实数延时神经网络（ RVTDNN ）为基础，用Levenberg-Marquardt（LM）优化算法确定神经网络系数，在模型中新增参数 w0，给出了 LM 算法的修改公式。接着在预失真神经网络系统中引入Bayesian机理消除LM算法的过拟合现象，构建CMMB数字直放站的间接学习预失真器，拟合HPA的非线性和记忆效应。结果表明：RVTDNN和FIR-NLNNN 2种预失真器均能显著提高系统性能，降低邻信道功率比30 dB左右。在保持均方误差（MSE）小于10-6的情况下，FIR-NLNNN结构的网络参数比RVTDNN结构减少了近50%，迭代过程中的乘法和加法次数约降低75%。%Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations, a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier. The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network. Due to the new parameters w0 in the novel NN model, the modified formulas of LM algorithm were provided. Next,in order to eliminate the over-fitting of LM algorithm, the Bayesian regularization algorithm was applied to the predistortion system. Additionally, the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA. Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB. Moreover, with the mean square error less than 10-6, the coefficient of network for FIR-NLNNN is about half of that for RVTDNN. Similarly, the times of
DEM interpolation based on artificial neural networks
Jiao, Limin; Liu, Yaolin
2005-10-01
This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.
Photon spectrometry utilizing neural networks
International Nuclear Information System (INIS)
Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)
Directory of Open Access Journals (Sweden)
CHEN, Z.
2013-11-01
Full Text Available A microphone clustering and back propagation (BP neural network based acoustic source localization method using distributed microphone arrays in an intelligent meeting room is proposed. In the proposed method, a novel clustering algorithm is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to select clusters which are small and close to the source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each selected cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization approaches confirm the validity of the proposed method.
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Institute of Scientific and Technical Information of China (English)
甄新武; 乔均俭
2013-01-01
将BP人工神经网络(Artificiai neural network)技术与传统的正交试验方法相结合,提出一种新的试验分析和处理方法,利用神经网络特有的自学能力,对主要影响因素进行仿真优化,获得Bacillus velezensis Z-27菌株培养基组分,即玉米粉2.0％、豆饼粉1.5％、MnSO4· H2O 0.07％,起始pH为7.0、接种量为2.0％,应用优化得到的培养基组分进行验证试验,取得了较好的效果.%In this article,a new method of test analysis and data treatment which combined BP artificial neural network and traditional orthogonal experiment was proposed to obtain optimized medium composition of Bacillus velezensis Z-27 strains.By this method the main factors could be simulated and optimized with the help of specific learning capability of neural network.The results showed that the medium components of B.velezensis Z-27 strains was corn flour 2.0％,soybean powder 1.5％,MnSO4·H2O 0.07％ at initial pH 7.0,with inoculation quantity 2.0％.The optimized culture medium was then verified,and showed satisfactory effect.
Forecasting Zakat collection using artificial neural network
Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina
2013-04-01
'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.
Institute of Scientific and Technical Information of China (English)
杨国良; 钟雯; 黄晓韵; 梁思敏; 何慧慧; 陈家驹
2015-01-01
Based on layered elastic theory,the elastic modulus of asphalt course in asphalt pavement was predicted using BP artificial neural network.According to the types of pavement structure in common use,the database of surface deflections with their corresponding structural parameters of asphalt course based on layered elastic theory was established.The elastic modulus backcalculation model of asphalt course in asphalt pavement was developed using BP artificial neural network to predict.The predictive results of asphalt course elastic modulus backcalculation using theoretical deflection basin and measured deflection basin indicate that the elastic modulus backcalculation model of asphalt course in asphalt pavement is of good predictive accuracy and reliability.It would provide the references with the elastic modulus backcalculation model of asphalt course to accurately and quickly estimate the conditions of asphalt course in asphalt pavement.%基于层状弹性体系理论,建立BP人工神经网络反演沥青路面沥青面层弹性模量预测模型,利用BP人工神经网络预测沥青路面沥青面层弹性模量.理论弯沉盆和实测弯沉盆反演沥青面层弹性模量的结果表明,建立的BP人工神经网络反演沥青路面沥青面层弹性模量模型具有良好的预测精度和可靠性,为评价沥青路面的沥青面层性能状况提供了参考.
Institute of Scientific and Technical Information of China (English)
李进才; 尹超; 刘飞
2011-01-01
针对通用机械产品售后质量损失影响因素多、质量损失评估及预警困难等问题,提出了一种基于模糊综合评判和BP神经网络的通用机械产品售后质量损失评估及预警方法。建立了通用机械产品售后质量损失的评价指标体系,并对售后质量损失的综合模糊评价、三层BP神经网络质量损失预警等关键技术进行了研究。最后,将该方法应用于重庆某通用机械制造企业售后质量损失的评估预警管理,取得了良好应用效果。%In view of the after-sale quality loss of universal engineering products is affected by multiple factors and it has difficulties in evaluation and early warning. An after-sale quality loss evaluation and early warning method for universal engineering products was put forward,which was based on fuzzy comprehensive evaluation and BP neural networks.Meanwhile,the evaluation index system for after-sale quality loss was founded.Subsequently,some of the key technologies were studied,including fuzzy comprehensive evaluation,three layer BP neural networks forecasting for the after-sale quality loss.Finally,the method was successfully applied in after-sale quality loss evaluation and early warning in a universal engineering products manufacturer of Chongqing, and good results were obtained.
Neural Networks Methodology and Applications
Dreyfus, Gérard
2005-01-01
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...
Institute of Scientific and Technical Information of China (English)
郭华锋; 李菊丽; 孙涛
2014-01-01
In order to study effects of process parameters on kerf quality of fiber laser cutting , the relationship between process parameters and kerf quality was analyzed based on the test of laser cutting T 4003 stainless steel .The prediction model between the main process parameters , such as laser power , cutting speed , assistant gas pressure and kerf roughness was established based on error back propagation artificial neural network .The samples collected by the cutting test was network trained and the training model was inspected by the test samples .The results show that , kerf roughness increases while laser power increases and kerf roughness decreases while cutting speed and assist gas pressure increase .The neural network prediction model has high precision and the network training has good effect .The maximum relative error between the predictive values and the test sample value is 2.4%.After training, the prediction model has high inspection precision, the maximum relative error of the test sample is only 6.23%.The model can predict the laser cutting kerf roughness effectively and can provide the experiment basis for selecting and optimizing process parameters and improving laser cutting quality .%为了研究工艺参量对光纤激光切割切口质量的影响，进行了切割T4003不锈钢试验，分析了工艺参量与切口质量之间的关系。采用基于误差反向传播算法的人工神经网络，建立了激光功率、切割速率、辅助气体压力等工艺参量与切口粗糙度之间的预测模型。对切割试验采集的训练样本进行了网络训练，并利用测试样本对训练模型进行验证。结果表明，随着激光功率增加，切口粗糙度增大；随着切割速率和辅助气体压力增加，切口粗糙度减小。神经网络预测模型精度较高，网络训练效果良好，预测值与试验样本值间的最大相对误差为2．4％。训练后检验精度较高，检验样本最大
Learning with heterogeneous neural networks
Belanche Muñoz, Luis Antonio
2011-01-01
This chapter studies a class of neuron models that computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the quasi-linear mean of the partial input-weight similarities. The neuron model is capable of dealing directly with mixtures of continuous as well as discrete quantities, among other data types and there is provision for missing values. An artificial neural network using these n...
Process Neural Networks Theory and Applications
He, Xingui
2010-01-01
"Process Neural Networks - Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks, and enhancing the expression capability for practical problems, with broad applicability to solving problems relating to process in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are strictly proved. The application methods, network construction principles, and optimization alg
The LILARTI neural network system
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
1992-10-01
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
Institute of Scientific and Technical Information of China (English)
宋博
2012-01-01
With the increasingly heated market competition,catering enterprises must construct and cultivate their core competence to develop permanently.In order to analyze and evaluate the core competence of catering enterprises,this paper constructed the comprehensive evaluation index system by using the fault-tolerance characteristics of neural network theory,then achieved fuzzy comprehensive evaluation for the core competence of catering enterprises by using proper action function,data structure and processing various of non-numerical index.%本文构造了餐饮企业核心竞争力的综合评价指标体系,利用神经网络理论的容错特征,通过选取适当的作用函数和数据结构,处理各种非数值性指标,实现对餐饮企业核心竞争力的模糊综合评价。
E-nose based rapid prediction of early mouldy grain using probabilistic neural networks
Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun
2015-01-01
In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poo...
Institute of Scientific and Technical Information of China (English)
宋丹丹; 任国臣; 张梦松; 苏人奇
2016-01-01
The photovoltaic power station has the characteristics that generating capacity is intermittent and easily impact large power networks. This paper analyzes the main factors influencing PV station, including illumination intensity weather patterns and temperature. And it is important to establish the forecasting model of generating capacity. The model uses Self-Organizing Feature Maps to classify the input samples, then the classification of sample uses LM learning algorithm training. By comparison between the predicted value and the real value, the model gets over shortcomings which are easy to fall into local minima. The accuracy and application of models are high.%针对并网型光伏电站发电量具有间歇性、易对大电网造成冲击等特点，分析影响光伏电站发电量的主要气象因素，包括辐照度、天气类型、温度，建立了一种基于 SOFM-LM-BP 神经网络发电量预测模型。该模型采用SOFM神经网络对输入样本进行分类，再将分类后的样本采用LM学习算法训练，从而得到光伏发电量的预测系统。通过预测值与真实值对比可知，该预测模型的预测精度较高，克服陷入局部极小值等缺点。
Institute of Scientific and Technical Information of China (English)
朱爱胜; 俞林; 许敏; 张天华
2015-01-01
Firstly, the paper collects the first hand data by the questionnaire survey method, and then studies the relationship between university students' entrepreneurial intentions and entrepreneurial behaviors by artificial neural network based on the preliminary research results. The results show that among the 11 dimensions which are influenced the contemporary university students' Entrepreneurial intentions and entrepreneurial behavior, the impact of entrepreneurship education, entrepreneurship, entrepreneurial intentions, institutional environment, endowments are largest, the impact of the expected benefits, market opportunities and behavi-oral attitudes are smaller than the above five dimensions, the impact of cognitive, subjective norm, perceived behavioral control are smallest. The predicted result of improved BP Neural Network algorithm through genetic algorithm optimization are compared with the traditional BP neural network, it is found that optimization algorithm has better prediction accuracy which is improved nearly 10 percen-tage points.%通过设计调研问卷进行实地调研, 收集第一手资料数据, 在前期成果的基础上, 通过遗传算法优化的人工神经网络技术计量研究高校学生的创业意愿与创业行为间的关系. 结果表明影响当代大学生创业意愿与创业行为的11个维度中创业教育、 创业能力、 创业意愿、 制度环境、 禀赋5个维度对其影响度较大, 预期收益、 市场机会和行为态度等维度对当代大学生创业意愿与创业行为的影响次之, 认知、 主观规范、 行为知觉控制3个维度对当代大学生创业意愿与创业行为的影响最小. 并在此基础上, 将遗传算法优化的神经网络的预测结果与传统神经网络进行比较, 发现遗传算法优化的神经网络的预测效果更佳, 预测精确度提升了近10个百分点.
Practical neural network recipies in C++
Masters
2014-01-01
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum
Neural Network-Based Active Control for Offshore Platforms
Institute of Scientific and Technical Information of China (English)
周亚军; 赵德有
2003-01-01
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
Data Process of Diagnose Expert System based on Neural Network
Directory of Open Access Journals (Sweden)
Shupeng Zhao
2013-12-01
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model
Puspanjali Mohapatra; Alok Raj; Tapas Kumar Patra
2012-01-01
This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network(FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices or one day, one week, two weeks and one month in advance.The Indian stock prices i.e. BSE (Bombay Stock Exchange), NSE,INFY etc. with few technical indicators are considered as input for the experime...
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Hanxin Chen; Yanjun Lu; Ling Tu
2013-01-01
A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, ...
Speed-Sensorless Control Using Elman Neural Network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed,with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.
Activated sludge process based on artificial neural network
Institute of Scientific and Technical Information of China (English)
张文艺; 蔡建安
2002-01-01
Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.
Applications of neural networks in reactor diagnosis and monitoring
International Nuclear Information System (INIS)
The Sodium temperature estimation in intermediate Heat Exchanger is very significant for nuclear power generation in fast breeder test reactor (FBTR). Hence accurate evaluation of sodium temperature is a major concern both in case of offline and online operation of nuclear power plant (NPP). This section addresses the training of artificial neural network model to precisely estimate the sodium temperature of Sodium-Sodium (Na-Na) Intermediate Heat exchanger and studying its behavior at transient conditions. Severely unbalanced flow conditions in addition to steady state condition are investigated to generate sufficient number of dataset. Based on the in house data gathered from Quadratic Upstream Interpolation for Convective Kinetics code (QUICK), a three layer neural network model is developed for training and subsequent validation. The back propagation (BP) algorithm is used for training the network. Further a model based on Radial Basis Function (RBF) neural network is developed and trained and the results are compared with standard back propagation algorithm. From the comparison studies of earlier models, it is found that the network trained with RBF converges faster than BP network. Training and testing results of some work related to this issues show the successful modeling of plant dynamics of the reactor with improved accuracy. ANN can be an alternative to the conventional model as it predicts the physical parameters without much complex calculations as used in conventional model
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
A Novel Training Algorithm of Genetic Neural Networks and Its Application to Classification
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.
Neural networks and MIMD-multiprocessors
Vanhala, Jukka; Kaski, Kimmo
1990-01-01
Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.
Neural-Network Computer Transforms Coordinates
Josin, Gary M.
1990-01-01
Numerical simulation demonstrated ability of conceptual neural-network computer to generalize what it has "learned" from few examples. Ability to generalize achieved with even simple neural network (relatively few neurons) and after exposure of network to only few "training" examples. Ability to obtain fairly accurate mappings after only few training examples used to provide solutions to otherwise intractable mapping problems.
Salience-Affected Neural Networks
Remmelzwaal, Leendert A; Ellis, George F R
2010-01-01
We present a simple neural network model which combines a locally-connected feedforward structure, as is traditionally used to model inter-neuron connectivity, with a layer of undifferentiated connections which model the diffuse projections from the human limbic system to the cortex. This new layer makes it possible to model global effects such as salience, at the same time as the local network processes task-specific or local information. This simple combination network displays interactions between salience and regular processing which correspond to known effects in the developing brain, such as enhanced learning as a result of heightened affect. The cortex biases neuronal responses to affect both learning and memory, through the use of diffuse projections from the limbic system to the cortex. Standard ANNs do not model this non-local flow of information represented by the ascending systems, which are a significant feature of the structure of the brain, and although they do allow associational learning with...
Hanbing Liu; Gang Song; Yubo Jiao; Peng Zhang; Xianqiang Wang
2014-01-01
An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its...
Fast Algorithms for Convolutional Neural Networks
Lavin, Andrew; Gray, Scott
2015-01-01
Deep convolutional neural networks take GPU days of compute time to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3x3 filters. We ...
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Information Theory for Analyzing Neural Networks
Sørngård, Bård
2014-01-01
The goal of this thesis was to investigate how information theory could be used to analyze artificial neural networks. For this purpose, two problems, a classification problem and a controller problem were considered. The classification problem was solved with a feedforward neural network trained with backpropagation, the controller problem was solved with a continuous-time recurrent neural network optimized with evolution.Results from the classification problem shows that mutual information ...
Sequential optimizing investing strategy with neural networks
Ryo Adachi; Akimichi Takemura
2010-01-01
In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the current round. We compare performance of our proposed strategy with various strategies including a strategy based on supervised neural network models and show that our procedure is co...
Artificial neural networks in nuclear medicine
International Nuclear Information System (INIS)
An analysis of the accessible literature on the diagnostic applicability of artificial neural networks in coronary artery disease and pulmonary embolism appears to be comparative to the diagnosis of experienced doctors dealing with nuclear medicine. Differences in the employed models of artificial neural networks indicate a constant search for the most optimal parameters, which could guarantee the ultimate accuracy in neural network activity. The diagnostic potential within systems containing artificial neural networks proves this calculation tool to be an independent or/and an additional device for supporting a doctor's diagnosis of artery disease and pulmonary embolism. (author)
Fuzzy neural network theory and application
Liu, Puyin
2004-01-01
This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he
Application of neural networks in coastal engineering
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
neural networks, J of computer aided civil and infrastructural engineering, (UK), 13, 113-120. Deo, MC and Naidu, CS (1999) Real time wave forecasting using neural networks, Ocean Engineering, 26, 191-203. Deo, MC, Gondane, DS and Kumar, VS (2002...) An application of artificial neural networks in tide-forecasting. Ocean Engineering, 29, pp 1003-1022 MandaI,S; Subba Rao and Chackraborty, l\\TV (2002) Hindcasting cyclonic waves using neural network. International Conference SHOT 2002, lIT Kharagpur, 18...
Neural networks for nuclear spectroscopy
Energy Technology Data Exchange (ETDEWEB)
Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States)] [and others
1995-12-31
In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.
Cheng, Xinmin; Zhang, Xiaodan; Zhao, Li; Deng, Aideng; Bao, Yongqiang; Liu, Yong; Jiang, Yunliang
2014-04-01
When using acoustic emission to locate the friction fault source of rotating machinery, the effects of strong noise and waveform distortion make accurate locating difficult. Applying neural network for acoustic emission source location could be helpful. In the BP Wavelet Neural Network, BP is a local search algorithm, which falls into local minimum easily. The probability of successful search is low. We used Shuffled Frog Leaping Algorithm (SFLA) to optimize the parameters of the Wavelet Neural Network, and the optimized Wavelet Neural Network to locate the source. After having performed the experiments of friction acoustic emission's source location on the rotor friction test machine, the results show that the calculation of SFLA is simple and effective, and that locating is accurate with proper structure of the network and input parameters.
Institute of Scientific and Technical Information of China (English)
WANG Hong-bing; XU An-jun; AI Li-xiang; TIAN Nai-yuan
2012-01-01
The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
Institute of Scientific and Technical Information of China (English)
王成勇; 朱汝城; 王婉璐; 李亨; 刘全坤; 周慧杰
2011-01-01
According to the feature of high pressure die casting of A356 coffee machine dome, the die casting process of coffee machine dome was simulated by finite element simulate software.The L16 (45 )-orthogonal experiments and six complementary experiments were chosen as the trained samples of Back Propagation Neural Network.The major processing parameters of die casting were pouring temperature, mould pre-heated temperature, injection pressure and injection speed.The non-linear mapping between these processing parameters and thermal stress of die casting mould were built up.In order to get the minimum heat stress of die casting mould, the die casting processing parameters were optimized by GA algorithm.The best combination processing parameters of pouring temperature, mould pre-heated temperature, injection pressure, injection speed were found.Under these process parameters, the experimental index (σ)max became low, the trend of mold fatigue was reduced and the quality of casting was improved.The experiment results validate the feasibility of this optimization on reducing the thermal fatigue of mould and provide guidance on producing similar die casting parts.%依据A356咖啡机顶盖高压铸造特点,采用FEM仿真软件对铸件成型工艺进行数值模拟,以L16(45)正交试验和6个补充试验作为BP神经网络的训练样本,建立模具热应力与浇注温度、模具预热温度、压射比压、压铸速度4个压铸工艺参数的非线性映射关系;以模具热应力θmax的最小值为优化目标,运用遗传算法进行工艺参数优化.最终得出浇注温度、模具预热温度、压射比压、压铸速度等4个参数最佳的一组组合,使试验指标θmax最小,模具的热疲劳趋势最低,零件的成型质量最佳.试验结果证明,该减少模具热疲劳趋势的优化方案具有可行性,同时对相近结构压铸件的生产也具有一定的指导意义.
Neural Network Controlled Visual Saccades
Johnson, Jeffrey D.; Grogan, Timothy A.
1989-03-01
The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.
Institute of Scientific and Technical Information of China (English)
LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai
2008-01-01
Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.
Neural networks with discontinuous/impact activations
Akhmet, Marat
2014-01-01
This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...
Video Traffic Prediction Using Neural Networks
Directory of Open Access Journals (Sweden)
Miloš Oravec
2008-10-01
Full Text Available In this paper, we consider video stream prediction for application in services likevideo-on-demand, videoconferencing, video broadcasting, etc. The aim is to predict thevideo stream for an efficient bandwidth allocation of the video signal. Efficient predictionof traffic generated by multimedia sources is an important part of traffic and congestioncontrol procedures at the network edges. As a tool for the prediction, we use neuralnetworks – multilayer perceptron (MLP, radial basis function networks (RBF networksand backpropagation through time (BPTT neural networks. At first, we briefly introducetheoretical background of neural networks, the prediction methods and the differencebetween them. We propose also video time-series processing using moving averages.Simulation results for each type of neural network together with final comparisons arepresented. For comparison purposes, also conventional (non-neural prediction isincluded. The purpose of our work is to construct suitable neural networks for variable bitrate video prediction and evaluate them. We use video traces from [1].
Neural Networks for Emotion Classification
Sun, Yafei
2011-01-01
It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of the neural network, i.e. the learning rate, activation function parameter, momentum number, the number of nodes in one layer, etc. we developed a strategy that can automatically select comparatively better combination of these parameters. We also introduce another way to perform back propagation. Instead of using the partial differential of the error function, we use optimal algorithm; namely Powell's direction set to minimize the error function. We were also interested in construction an authentic emotion databases. This...
Artificial neural networks in neurosurgery.
Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali
2015-03-01
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.
The Laplacian spectrum of neural networks
Directory of Open Access Journals (Sweden)
Siemon ede Lange
2014-01-01
Full Text Available The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these ‘conventional’ graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network’s structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.
Optimising the topology of complex neural networks
Jiang, Fei; Schoenauer, Marc
2007-01-01
In this paper, we study instances of complex neural networks, i.e. neural netwo rks with complex topologies. We use Self-Organizing Map neural networks whose n eighbourhood relationships are defined by a complex network, to classify handwr itten digits. We show that topology has a small impact on performance and robus tness to neuron failures, at least at long learning times. Performance may howe ver be increased (by almost 10%) by artificial evolution of the network topo logy. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution.
A novel compensation-based recurrent fuzzy neural network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
WU Bo; WU Ke; LU JianHong
2009-01-01
Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-computing is a practical and advanced tool for solving large-scale underground rock engineering problems.
A new formulation for feedforward neural networks.
Razavi, Saman; Tolson, Bryan A
2011-10-01
Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry. Based on this geometrical interpretation, a new set of variables describing neural networks is proposed as a more effective and geometrically interpretable alternative to the traditional set of network weights and biases. Then, this paper develops a new formulation for neural networks with respect to the newly defined variables; this reformulated neural network (ReNN) is equivalent to the common feedforward neural network but has a less complex error response surface. To demonstrate the learning ability of ReNN, in this paper, two training methods involving a derivative-based (a variation of backpropagation) and a derivative-free optimization algorithms are employed. Moreover, a new measure of regularization on the basis of the developed geometrical interpretation is proposed to evaluate and improve the generalization ability of neural networks. The value of the proposed geometrical interpretation, the ReNN approach, and the new regularization measure are demonstrated across multiple test problems. Results show that ReNN can be trained more effectively and efficiently compared to the common neural networks and the proposed regularization measure is an effective indicator of how a network would perform in terms of generalization.
Drift chamber tracking with neural networks
International Nuclear Information System (INIS)
We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed
Drift chamber tracking with neural networks
Energy Technology Data Exchange (ETDEWEB)
Lindsey, C.S.; Denby, B.; Haggerty, H.
1992-10-01
We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed.
Coherence resonance in bursting neural networks
Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J.
2015-10-01
Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal—a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.
Coherence resonance in bursting neural networks.
Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J
2015-10-01
Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.
Institute of Scientific and Technical Information of China (English)
苗静; 曹玉珍; 杨仁杰; 刘蓉; 孙惠丽; 徐可欣
2013-01-01
Discriminant models of adulterated milk and pure milk were established using BP neural network combined with two-dimensional (2D) correlation near-infrared spectra parameterization .Forty pure milk samples ,40 adulterated milk samples with urea (1~20 g · L -1 ) and 40 adulterated milk samples with melamine (0.01~3 g · L -1 ) were prepared respectively .Based on the characteristics of 2D correlation near-infrared spectra of pure milk and adulterated milk ,5 apparent statistic parameters were calculated based on the parameterization theory .Using 5 characteristic parameters ,discriminant models of urea adulterated milk , melamine adulterated milk and two types of adulterated milk were built by BP neural network .The prediction rate of unknown samples were 95% ,100% and 96.7% ,respectively .The results show that this method can extract effectively feature informa-tion of adulterant ,reduce the input dimensions of BP neural network ,and better realize qualitative analysis of adulterant in milk .%将二维相关近红外谱参数化方法与BP神经网络结合，建立掺杂牛奶与纯牛奶的判别模型。分别配制含有尿素牛奶（1～20g·L -1）和三聚氰胺牛奶（0.01～3g·L -1）样品各40个。研究了纯牛奶、掺杂牛奶的二维相关近红外谱特性，在此基础上，分别提取了各样品二维相关同步谱的5个特征参数。将这5个特征参数作为BP神经网络的输入，分别建立掺杂尿素、掺杂三聚氰胺、两种掺杂牛奶与纯牛奶的判别模型，采用这些模型对未知样品进行预测，其预测正确率分别为95％，100％和96.7％。研究结果表明：该方法有效地提取了牛奶中掺杂目标物的特征光谱信息，同时又减少了BP神经网络输入变量的维数，实现了掺杂牛奶与纯牛奶的鉴别。
Radiation Behavior of Analog Neural Network Chip
Langenbacher, H.; Zee, F.; Daud, T.; Thakoor, A.
1996-01-01
A neural network experiment conducted for the Space Technology Research Vehicle (STRV-1) 1-b launched in June 1994. Identical sets of analog feed-forward neural network chips was used to study and compare the effects of space and ground radiation on the chips. Three failure mechanisms are noted.
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Self-organization of neural networks
Energy Technology Data Exchange (ETDEWEB)
Clark, J.W.; Winston, J.V.; Rafelski, J.
1984-05-14
The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (brainwashing) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conducive to the simulation of memory and learning phenomena. 18 references, 2 figures.
Self-organization of neural networks
Clark, John W.; Winston, Jeffrey V.; Rafelski, Johann
1984-05-01
The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (“brainwashing”) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conductive to the simulation of memory and learning phenomena.
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Institute of Scientific and Technical Information of China (English)
高晓旭; 董丁稳; 杨日丽
2011-01-01
Coal mines are man-machine-environment systems with complex structure. Based on the theory of man-machine-environment-management, relevant elements of coal mines are studied which leading to feasible principles for application of the Intrinsic safety concept to coal mines. Based on the theory of human behavior, a Man Assessment Index Model of Intrinsic safety is founded; Through ergonomics analysis and reliability analysis, Machine Assessment Index Model is founded; The environment of coal mines, being classified as working environment and geological environment, the Environment Assessment Index Model was founded; Based on the principles of occupation health and safety management and the System of National Industrial Safety Management, The Management Assessment Index Model is founded. As the system of man-machine-environment-management being thoroughly studied, an overall Evaluation Index System of coal mines is put forward. With the theory of Back Propagation Neural Networks, the Assessment Model is established. The research on the sustainable safety in the certain Coal Mine of Shaanxi Province leads to the establishment of the Intrinsic Safety Management System, and the evaluation model verification. The concept of Intrinsic Safety in coal mines safety management which will promote the improvement of safety management and safety control standard in coal mines to build a safer and more productive industry.%煤矿井下是一个复杂多变的人-机-环境-管理系统.运用人-机-环境-管理系统理论,以煤矿人、机、环境、管理4个单要素和系统整体为研究对象,对煤矿本质安全的评价和持续改进开展了系统分析和研究.在人的行为理论分析基础上,建立人因素本质安全评价模型；对设备进行了人机工程学分析,利用可靠性原理建立了设备本质安全评价的指标体系；在将煤矿井下环境归类为作业环境和地质环境的基础上,建立了环境本质安全评价的指标
Secure Key Exchange using Neural Network
Vineeta Soni
2014-01-01
Any cryptographic system is used to exchange confidential information securely over the public channel without any leakage of information to the unauthorized users. Neural networks can be used to generate a common secret key because the processes involve in Cryptographic system requires large computational power and very complex. Moreover Diffi hellman key exchange is suffered from man-in –the middle attack. For overcome this problem neural networks can be used.Two neural netwo...
Introduction to Concepts in Artificial Neural Networks
Niebur, Dagmar
1995-01-01
This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.
A Neural Network Appraoch to Fault Diagnosis in Analog Circuits
Institute of Scientific and Technical Information of China (English)
尉乃红; 杨士元; 等
1996-01-01
Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.
Rule Extraction using Artificial Neural Networks
Kamruzzaman, S M
2010-01-01
Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The ...
International Conference on Artificial Neural Networks (ICANN)
Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics
2015-01-01
The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...
Institute of Scientific and Technical Information of China (English)
周振民; 刘俊秀; 范秀; 郭威
2015-01-01
介绍了LM －BP神经网络模型的原理及算法和模型的优点。针对实际水质评价问题，利用随机内插方法在地表水环境质量分级标准阈值间生成训练样本和检验样本，建立了新乡市卫河地面水环境质量综合评价的LM －BP神经网络模型，将模型应用于卫河2011年3月份、9月份的水质评价，并与单因子评价法、模糊综合评价法进行了比较分析。实验结果表明该模型设计合理，泛化能力强，收敛速度快，算法稳定，推导严谨，有较充分的理论依据，应用于水质评价具有其合理性、实用性和有效性，适用于作深入的水环境质量分析。%The working principle ,algorithm and advantages of LM-BP Neural Network is introduced .According to the actual prob‐lem of water quality assessment ,the random interpolation method is used to generate training and testing samples at the surface wa‐ter environmental quality grading standard threshold and the surface water environmental quality comprehensive assessment of the BP neural network model is established for the Weihe River of Xinxiang City .The model was applied to water quality assessment in March 2011 and in September 2011 of Weihe River .Then the results are compared with single factor assessment method ,fuzzy math‐ematical assessment method .The experimental results show that the model design is reasonable ,the generalization ability is strong , the convergence is fast ,derivation is rigorous ,algorithm is stable ,and theoretical basis is sufficient .The model applied in water quality evaluation has its rationality ,practicability and validity ,which is available to a thorough analysis of the water environmental quality .
Institute of Scientific and Technical Information of China (English)
王书涛; 陈东营; 王兴龙; 魏蒙; 王志芳
2015-01-01
研究了山梨酸钾在水溶液和橙汁中的荧光特性，结果表明在两种溶液中山梨酸钾的荧光特性虽然有很大的区别，但是它们的荧光特征峰都存在于λex／λem ＝375／490 nm。从二维荧光光谱可以看出，橙汁中山梨酸钾的浓度和相对荧光强度关系错综复杂，两者不再满足线性关系。为了准确测定橙汁中山梨酸钾的浓度，提出了一种微粒群（PSO）算法优化的误差逆向传播（BP）神经网络的新方法。两组预测浓度的相对误差分别为1.83％和1.53％，预测结果表明该方法具有可行性。在浓度范围为0.1～2.0 g・L -1内，PSO-BP神经网络能够完成橙汁中梨酸钾浓度的准确测定。%In this paper ,fluorescence spectra properties of potassium sorbate in aqueous solution and orange juice are studied ,and the result shows that in two solution there are many difference in fluorescence spectra of potassium sorbate ,but the fluorescence characteristic peak exists in λex/λem =375/490 nm .It can be seen from the two dimensional fluorescence spectra that the relationship between the fluorescence intensity and the con-centration of potassium sorbate is very complex ,so there is no linear relationship between them .To determine the concentration of potassium sorbate in orange juice ,a new method combining Particle Swarm Optimization (PSO) algorithm with Back Propagation (BP) neural network is proposed .The relative error of two predicted concentrations is 1.83% and 1.53% respectively ,which indicate that the method is feasible .The PSO-BP neural network can accurately measure the concentration of potassium sorbate in orange juice in the range of 0.1~2.0 g・L -1 .
CONVERGENCE OF GRADIENT METHOD WITH MOMENTUM FOR BACK-PROPAGATION NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
Wei Wu; Naimin Zhang; Zhengxue Li; Long Li; Yan Liu
2008-01-01
In this work,a gradient method with momentum for BP neural networks is considered.The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the network weights.Corresponding convergence results are proved.
Institute of Scientific and Technical Information of China (English)
张晓兔; 刘祖源; 张乐文
1999-01-01
随着神经网络研究热潮的兴起,已经提出了许多学习算法,并迅速在各领域得到应用;本文试图采用改进的BP(Back-Propagation)算法来求解平面势流问题,并且与用有限差分法FDM(Finite Difference Method)计算的结果进行了比较,发现吻合良好.
Wavelet Neural Networks for Adaptive Equalization
Institute of Scientific and Technical Information of China (English)
JIANGMinghu; DENGBeixing; GIELENGeorges; ZHANGBo
2003-01-01
A structure based on the Wavelet neural networks (WNNs) is proposed for nonlinear channel equalization in a digital communication system. The construction algorithm of the Minimum error probability (MEP) is presented and applied as a performance criterion to update the parameter matrix of wavelet networks. Our experimental results show that performance of the proposed wavelet networks based on equalizer can significantly improve the neural modeling accuracy, perform quite well in compensating the nonlinear distortion introduced by the channel, and outperform the conventional neural networks in signal to noise ratio and channel non-llnearity.
FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
ZHANG Dailin; CHEN Youping; AI Wu; ZHOU Zude; KONG Ching Tom
2008-01-01
Various force disturbances influence the thrust force of linear motors when a linear motor (LM) is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation(BP) neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.
Neural network predicts sequence of TP53 gene based on DNA chip
DEFF Research Database (Denmark)
Spicker, J.S.; Wikman, F.; Lu, M.L.;
2002-01-01
We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero...... and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence....
Prediction of the Performance of the Fabrics in Garment Manufacturing by Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
LIU Kan; ZHANG Wei-yuan
2004-01-01
An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the different ANN's construct on the convergence speed and the prediction accuracy are investigated. The result indicates that the BP neural network is an efficiency technique and has a wide prospect in the application to garment processing.
Institute of Scientific and Technical Information of China (English)
张晔; 邓楚雄; 谢炳庚; 刘利科; 雷国强
2014-01-01
按照“美丽中国”建设的时代内涵要求，从生态、经济、政治、文化、社会等5个维度入手，尝试性地构建包含32个单项指标的“美丽湖南”建设水平定量综合评价指标体系及评判标准；以市（州）为评价单元，探索性地采用 BP 人工神经网络方法建模评价“美丽湖南”建设现状。结果表明：1）分维度看，2011年湖南省各市（州）政治子系统评价均值为0.6514，评价结果为Ⅱ级，处于“美丽”状态；生态、经济和社会子系统评价均值分别为0.5018、0.4502和0.4730，评价结果为Ⅲ级，处于“较美丽”状态；文化子系统评价均值为0.4197，评价结果为Ⅳ级，处于“欠美丽”状态。2）整体而言，2011年湖南省各市（州）系统综合评价均值为0.4927，综合评价结果为Ⅲ级，总体上湖南省处于“较美丽”状态。3）运用 BP 人工神经网络建模评价，简便高效，结果客观可靠，在区域建设水平综合评价中适用、有效。%According to the times connotation requirement of Beautiful China construction, this paper built a set of evaluation index system including 32 indexes and its judgment standard of Beautiful Hunan construction from ecological subsystem, economic subsystem, political subsystem, cultural subsystem and social subsystem, made a quantitative and comprehensive evaluation of Beautiful Hunan construction by BP artificial neural network model at city level. The results are as follows: 1) In 2011, the mean evaluation value of political subsystem was 0.651 4, which was of grade Ⅱ, that means the political subsystem in Hunan Province was in “beautiful” status; the mean evaluation values of ecological, economic and social subsystems were 0.501 8, 0.450 2, and 0.473 0, respectively, the results were at grade Ⅲ, that means those subsystems were in “less beautiful” status; the mean evaluation value of cultural subsystem was 0.419 7, which
Sunspot prediction using neural networks
Villarreal, James; Baffes, Paul
1990-01-01
The earliest systematic observance of sunspot activity is known to have been discovered by the Chinese in 1382 during the Ming Dynasty (1368 to 1644) when spots on the sun were noticed by looking at the sun through thick, forest fire smoke. Not until after the 18th century did sunspot levels become more than a source of wonderment and curiosity. Since 1834 reliable sunspot data has been collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Naval Observatory. Recently, considerable effort has been placed upon the study of the effects of sunspots on the ecosystem and the space environment. The efforts of the Artificial Intelligence Section of the Mission Planning and Analysis Division of the Johnson Space Center involving the prediction of sunspot activity using neural network technologies are described.
Subspace learning of neural networks
Cheng Lv, Jian; Zhou, Jiliu
2010-01-01
PrefaceChapter 1. Introduction1.1 Introduction1.1.1 Linear Neural Networks1.1.2 Subspace Learning1.2 Subspace Learning Algorithms1.2.1 PCA Learning Algorithms1.2.2 MCA Learning Algorithms1.2.3 ICA Learning Algorithms1.3 Methods for Convergence Analysis1.3.1 SDT Method1.3.2 DCT Method1.3.3 DDT Method1.4 Block Algorithms1.5 Simulation Data Set and Notation1.6 ConclusionsChapter 2. PCA Learning Algorithms with Constants Learning Rates2.1 Oja's PCA Learning Algorithms2.1.1 The Algorithms2.1.2 Convergence Issue2.2 Invariant Sets2.2.1 Properties of Invariant Sets2.2.2 Conditions for Invariant Sets2.
Introduction to artificial neural networks.
Grossi, Enzo; Buscema, Massimo
2007-12-01
The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy. PMID:17998827
Drift chamber tracking with neural networks
International Nuclear Information System (INIS)
With the very high event rates projected for experiments at the SSC and LHC, it is important to investigate new approaches to on line pattern recognition. The use of neural networks for pattern recognition. The use of neural networks for pattern recognition in high energy physics detectors has been an area of very active research. The authors discuss drift chamber tracking with a commercial analog VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed
Energy Technology Data Exchange (ETDEWEB)
Modarres, Hamid Reza; Kor, Mohammad; Abkhoshk, Emad; Alfi, Alireza; Lower, James C.
2009-06-15
In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks - particle swarm optimization method gave the most accurate HGI prediction.
Exponential Stability for Delayed Cellular Neural Networks
Institute of Scientific and Technical Information of China (English)
YANG Jin-xiang; ZHONG Shou-ming; YAN Ke-yu
2005-01-01
The exponential stability of the delayed cellular neural networks (DCNN's) is investigated. By dividing the network state variables into some parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Liapunov function. It is shown that the conditions differ from previous ones. The new conditions, which are associated with some initial value, are represented by some blocks of the interconnection matrix.
Learning Processes of Layered Neural Networks
Fujiki, Sumiyoshi; Fujiki, Nahomi M.
1995-01-01
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.
ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD
Institute of Scientific and Technical Information of China (English)
ZHURong-gang; JIANGChangsheng; FENGBin
2004-01-01
A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.
Research of The Deeper Neural Networks
Directory of Open Access Journals (Sweden)
Xiao You Rong
2016-01-01
Full Text Available Neural networks (NNs have powerful computational abilities and could be used in a variety of applications; however, training these networks is still a difficult problem. With different network structures, many neural models have been constructed. In this report, a deeper neural networks (DNNs architecture is proposed. The training algorithm of deeper neural network insides searching the global optimal point in the actual error surface. Before the training algorithm is designed, the error surface of the deeper neural network is analyzed from simple to complicated, and the features of the error surface is obtained. Based on these characters, the initialization method and training algorithm of DNNs is designed. For the initialization, a block-uniform design method is proposed which separates the error surface into some blocks and finds the optimal block using the uniform design method. For the training algorithm, the improved gradient-descent method is proposed which adds a penalty term into the cost function of the old gradient descent method. This algorithm makes the network have a great approximating ability and keeps the network state stable. All of these improve the practicality of the neural network.
Directory of Open Access Journals (Sweden)
Hanbing Liu
2014-01-01
Full Text Available An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its superiority. The results indicate that curvature of flexibility changes can identify damages with both single and multiple locations. The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.
IUKF neural network modeling for FOG temperature drift
Institute of Scientific and Technical Information of China (English)
Feng Zha; Jiangning Xu; Jingshu Li; Hongyang He
2013-01-01
A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and com-pensate for the fiber optic gyro (FOG) bias drift caused by tempe-rature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter (UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure-ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea-surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise (-20-20◦C) and drop (70-20◦C) tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respec-tively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back-propagation (BP) and UKF network models.
Coronary Artery Diagnosis Aided by Neural Network
Stefko, Kamil
2007-01-01
Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.
The application of neural networks to comprehensive prediction by seismology prediction method
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
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 W0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W0-value usually appeared obviously around the future epicenter 1～3 years before earthquake. It is effective to mid-term prediction.
A fast learning algorithm of neural network with tunable activation function
Institute of Scientific and Technical Information of China (English)
SHEN Yanjun; WANG Bingwen
2004-01-01
This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.
Neural network regulation driven by autonomous neural firings
Cho, Myoung Won
2016-07-01
Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.
Using fuzzy neural networks for RMB/USD real exchange rate forecasting
Institute of Scientific and Technical Information of China (English)
HUI Xiao-feng; LI Zhe; WEI Qing-quan
2005-01-01
In order to aim at improving the forecasting performance of the RMB/USD exchange rate, this paper proposes a new architecture of fuzzy neural networks based on fuzzy logic, and the method of point differential,which guarantees not only the direction of weight correction, but also the needed precision for the BP algorithm.In applying genetic algorithms for optimal performance, this approach, in the forecasting of the RMB/USD real exchange rate from 1994 to 2000, obviously outperforms typical BP Neural Networks and exhibits a higher capacity in regard to nonlinear, time-variablility, and illegibility of the exchange rate.
A Study of Maneuvering Control for an Air Cushion Vehicle Based on Back Propagation Neural Network
Institute of Scientific and Technical Information of China (English)
LU Jun; HUANG Guo-liang; LI Shu-zhi
2009-01-01
A back propagation (BP) neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle (ACV). The calculation was based on four-freedom-degree model experiments of hydrodynamics and aerodynamics. It is necessary for the ACV to control the velocity and the yaw rate as well as the velocity angle at the same time. The yaw rate and the velocity angle must be controlled correspondingly because of the whipping, which is a special characteristic for the ACV. The calculation results show that it is an efficient way for the ACV's maneuvering control by using a BP neural network to adjust PID parameters online.
Active Diverse Learning Neural Network Ensemble Approach for Power Transformer Fault Diagnosis
Directory of Open Access Journals (Sweden)
Yu Xu
2010-10-01
Full Text Available An ensemble learning algorithm was proposed in this paper by analyzing the error function of neural network ensembles, by which, individual neural networks were actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms were included in the learning criterion function of individual networks. And all the individual networks in the ensemble were leaded to learn diversity through cooperative training. The method was applied in Dissolved Gas Analysis based fault diagnosis of power transformer. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. In addition, the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting.
Institute of Scientific and Technical Information of China (English)
鲍蓉
2016-01-01
针对印刷图像压缩重建问题，构建BP神经网络模型，采用LM算法提高了运算速度。在印刷图像压缩重建训练过程中，对隐含层神经元个数设定进行了类比分析，峰值信噪比和压缩比率均达到预期效果。对印刷图像压缩重建后出现块效应效果进行了分析，提出了修正模型方法。%With respect to compression reconstruction of printing images,this paper establishes a BP neural network model and adopts LM algorithm to raise the computing speed.In the training process of printing image compression reconstruction,analogy analysis is made on the setting of number of neurons of the hidden layer,and both the peak signal-to-noise ratio and compression ratio achieve the expected effect.The blocking effect appearing after printing image compression reconstruction is analyzed,and model correction methods are given.
Institute of Scientific and Technical Information of China (English)
杨俊生; 薛勇军
2014-01-01
With the rapid development of the ASEAN free trade area,its demand for talent will increase greatly.Therefore,it has important theoretical and practical significance to forecast the trend of talent demand of the ASEAN free trade area.This pa-per selects several variables that affect the number of ASEAN free trade area talent demand,and predicts its talent demand trend by using BP artificial neural network model.At last,based on the prediction results,several countermeasures for Yunnan prov-ince are proposed.%随着东盟自由贸易区的快速发展，东盟自由贸易区对人才需求量也将大大增加，因此对东盟自由贸易区人才需求趋势预测就具有重要的理论和现实意义。本文选取影响东盟自由贸易区人才需求数量的几个变量，运用BP人工神经网络模型对东盟自由贸易区人才需求趋势进行预测，并在人才需求预测结果的基础上提出云南省应对措施。
Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks
Kaaniche, Heni
2010-01-01
Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.
Neural network for sonogram gap filling
DEFF Research Database (Denmark)
Klebæk, Henrik; Jensen, Jørgen Arendt; Hansen, Lars Kai
1995-01-01
a neural network for predicting mean frequency of the velocity signal and its variance. The neural network then predicts the evolution of the mean and variance in the gaps, and the sonogram and audio signal are reconstructed from these. The technique is applied on in-vivo data from the carotid artery....... The neural network is trained on part of the data and the network is pruned by the optimal brain damage procedure in order to reduce the number of parameters in the network, and thereby reduce the risk of overfitting. The neural predictor is compared to using a linear filter for the mean and variance time......In duplex imaging both an anatomical B-mode image and a sonogram are acquired, and the time for data acquisition is divided between the two images. This gives problems when rapid B-mode image display is needed, since there is not time for measuring the velocity data. Gaps then appear...
Convolutional Neural Network for Image Recognition
Seifnashri, Sahand
2015-01-01
The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.
Multispectral-image fusion using neural networks
Kagel, Joseph H.; Platt, C. A.; Donaven, T. W.; Samstad, Eric A.
1990-08-01
A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard a circuit card assembly and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations results and a description of the prototype system are presented. 1.
Multispectral image fusion using neural networks
Kagel, J. H.; Platt, C. A.; Donaven, T. W.; Samstad, E. A.
1990-01-01
A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard, a circuit card assembly, and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations, results, and a description of the prototype system are presented.