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
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...
Sub-pixel mapping method based on BP neural network
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.
The Application of BP Neural Network In Oil-Field
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.
Spiking DNA Computing with applications to BP Neural Networks Classification
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.
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
Optimization Design based on BP Neural Network and GA Method
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
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
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
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...
The Software Reliability Model Using Hybrid Model of Fractals and BP Neural Network
Yong Cao; Xiaoguang Yue; Fei Xiong; Youjie Zhao
2015-01-01
The software reliability is the ability of the software to perform its required function under stated conditions for a stated period of time. In this paper, a hybrid methodology that combines both BP neural network and fractal models is proposed to take advantage of unique strength of BP neural network and fractal in modeling. Based on the experiments performed on the software reliability data obtained from literatures, it is observed that our method is effective through comparison with past...
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 Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
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.
Application of BP neural networks in non-linearity correction of optical tweezers
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
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.
Pulse frequency classification based on BP neural network
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.
Mountain ground movement prediction caused by mining based on BP-neural network
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
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.
The Software Reliability Model Using Hybrid Model of Fractals and BP Neural Network
Yong Cao
2015-12-01
Full Text Available The software reliability is the ability of the software to perform its required function under stated conditions for a stated period of time. In this paper, a hybrid methodology that combines both BP neural network and fractal models is proposed to take advantage of unique strength of BP neural network and fractal in modeling. Based on the experiments performed on the software reliability data obtained from literatures, it is observed that our method is effective through comparison with past methods and a new idea for the research of the software failure mechanism is presented.
Adaptive tracking controller using BP neural networks for a class of nonlinear systems
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.
Active fault tolerant control research for nuclear power plant based on BP neural network
In view of the sensor fault of nuclear power plant, the sensor was trained by adopting improved back propagation (BP) neural network method, and the dynamic model bank in different states was set up. The system was detected by using BP neural network in real time. When the sensor goes wrong, it will be controlled by reconstruction. Taking pressurizer as the case, a simulation experiment was performed on the nuclear power plant simulator. The results show that the proposed method is valid for the fault tolerant control of sensor faults in nuclear power plant. (authors)
Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network
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
Comparison of the BP training algorithm and LVQ neural networks for e, μ, π identification
Two different kinds of neural networks, feed-forward multi-layer mode with back-propagation training algorithm (BP) and Kohonen's learning vector quantization networks (LVQ), are adopted for the identification of e, μ, π particles in Beijing spectrometer (BES) experiment. The data samples for training and test consist of μ from cosmic ray, e and π from experimental data by strict selection. Although their momentum spectra are non-uniform, the identification efficiencies given by BP are quite uniform versus momentum, and LVQ is little worse. At least in this application BP is shown to be more powerful in pattern recognition than LVQ. (orig.)
Water quality evaluation model based on hybrid PSO-BP neural network
Xing Xu; Bingxiang Liu
2013-01-01
A hybrid neural network algorithm, aims at evaluating water quality, based on particle swarm optimization (PSO) algorithm, which has a keen ability in global search and back propagation (BP) algorithm that has a strong ability in local search. Heuristics has been proposed to optimize the number of neurons in the hidden layer. The comparison with the traditional BP NN shows the advantage of the proposed method with high precision and good correlation. The values of average absolute deviation (...
Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model
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.
Application of BP neural network to semi-solid apparent viscosity simulation
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.
Safety Prediction Analysis of the Agricultural Products Processing Based on the BP Neural Network
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.
A Prediction Model of Peasants’ Income in China Based on BP Neural Network
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
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
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.
Distinguish of Famous Jun Porcelain in Ancient and Present Age by INAA and BP Neural Network
Forty samples of Jun porcelain from an ancient Juntai kiln and 3 modern Jun kilns (Kongjia, Miaojia and Xinghang) were selected and analyzed for 25 elements by INAA.The data were trained and forecasted by BP neural network. The results indicate that the network can distinguish unknown body and glaze samples of the official Jun porcelain and the modern top-grade Jun porcelain after proper training. (authors)
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%.
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.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks
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.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks
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
The risk evaluation of mine coal-dust explosion based on BP neural network
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
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
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.
Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes
Factors of airspeed, flux, activity, source position, pipe length and pipe diameter affect nonlinearly source activity readout of the Long Range Alpha Detection (LRAD). In this paper, multiparameter influence experiment is carried out using variable-control method, aiming at studying relationships between the readout and each of the factors. The back propagation (BP) neural network model is established to overcome the nonlinear effects of the factors on the readout, with the readout and the multiparameters being the input, and the source activity being the output. Experiment data of 948 groups are used for BP neural network forecasting, with an average relative error of 3.4218×10-4. And in a 100-group test, an average relative error of 2.217×10-2 is obtained. It shows that with this method source radioactivity in pipes can be simulated. (authors)
Particle Swarm Optimization-based BP Neural Network for UHV DC Insulator Pollution Forecasting
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
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.
Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
Miao Suzhen
2016-01-01
Full Text Available Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO were used as inputs and ST was used as output of the model.
Water quality evaluation model based on hybrid PSO-BP neural network
Xing Xu
2013-09-01
Full Text Available A hybrid neural network algorithm, aims at evaluating water quality, based on particle swarm optimization (PSO algorithm, which has a keen ability in global search and back propagation (BP algorithm that has a strong ability in local search. Heuristics has been proposed to optimize the number of neurons in the hidden layer. The comparison with the traditional BP NN shows the advantage of the proposed method with high precision and good correlation. The values of average absolute deviation (AAD, standard deviation error (SDE and squared correlation coefficient (R2 are 0.0072, 0.0208 and 0.98845, respectively. The results show that the hybrid PSO-BP NN has a good predictal ability of evaluating water quality; it is a practical and efficacious method to evaluate water quality.
Learning algorithm and application of quantum BP neural networks based on universal quantum gates
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.
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
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.
Prediction of 2A70 aluminum alloy flow stress based on BP artificial neural network
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.
BP neural network based online prediction of steam turbine exhaust dryness
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.
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.
DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL
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.
Application of genetic BP network to discriminating earthquakes and explosions
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.
Soil infiltration based on bp neural network and grey relational analysis
Wang Juan
2013-02-01
Full Text Available Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.
The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network
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.
The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network
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.
Prediction Method of Vessel Maintenance Outlay Based on the BP Neural Network
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.
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.
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.
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…
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.
OPTIMIZATION OF INJECTION MOLDING PROCESS BASED ON NUMERICAL SIMULATIONAND BP NEURAL NETWORKS
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.
Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network
The rear axle gear is one of the key parts of transmission system for automobiles. Its healthy state directly influences the security and reliability of the automotives. However, non-stationary and nonlinear characteristics of gear vibration due to load and speed fluctuations, makes it difficult to detect and diagnosis the faults from the transmission gear. To solve this problem a fault prognosis and diagnosis method based on a combination of radial basis function(RBF) and back-propagation (BP) neural networks is proposed in this paper. Firstly, a moving average pretreatment is used to suppress the time series fluctuation of vibration characteristic parameter tie series and reduce the interference of random noise. Then, the RBF network is applied to the pretreated parameter sequences for fault prognosis. Furthermore, based on self-learning ability of neural networks, characteristic parameters for different common faults are learned by a BP network. Then the trained BP neural network is utilized for fault diagnosis of the rear axle gear. The results show that the proposed method has a good performance in prognosing and diagnosing different faults from the rear axle gear.
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
Ming Xue
2013-06-01
Full Text Available As the forefront of complex nonlinear science and artificial intelligence science, artificial neural network has began to be applied in the field of water quality control and planning step by step. According to the fuzzy feature of water quality information, this paper proposes a membership degree Back-Propagation network (MDBP for water quality assessment with combining fuzzy mathematics and artificial neural network. The proposed MDBP model combines the merits of artificial neural network method and fuzzy evaluation method, which overcomes effectively the shortcoming of other assessment methods. With improving the accuracy and reliability of the assessment method, the method has a higher flexibility than other conventional approach and its programs have a better adaptability and more convenient application. The assessment method is closer to the reality with considering the continuity of the changes of water quality environment.
Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment
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
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
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.
Yu Jingyuan; Li Qiang; Tang Ji
2011-01-01
In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template (F), centrifugal acceleration (v) and sintering temperature (T), while the only output was the ultimate compressive strength (σ). According to the registered BP model, the effects of F, v, T on σ were analyzed. The ...
Flux-measuring approach of high temperature metal liquid based on BP neural networks
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.
The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network
Haisheng Li; Long Lai; Li Chen; Cheng Lu; Qiang Cai
2015-01-01
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights a...
Application of Optimized BP Neural Network in Addressing for Garbage Power Plant
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.
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
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
HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds
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.
A Novel Method for Iris Recognition Using BP Neural Network and Parallel Computing
Hamid Reza Sahebi; Askari, S
2016-01-01
In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage of using these features is the high-speed extraction, as well as being unique to each iris. Then the back propagation neural network (BPNN) is used as a classifier. In this system, the BPNN parallel algorithms and their implementation on GPUs have been used by the aid of CUDA in order to speed up the learning process. Finall...
A Novel Method for Iris Recognition Using BP Neural Network and Parallel Computing
Hamid Reza Sahebi
2016-04-01
Full Text Available In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage of using these features is the high-speed extraction, as well as being unique to each iris. Then the back propagation neural network (BPNN is used as a classifier. In this system, the BPNN parallel algorithms and their implementation on GPUs have been used by the aid of CUDA in order to speed up the learning process. Finally, the system performance and the speeding outcomes in a way that this algorithm is done in series are presented.
Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks
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.
Prediction of time series of NPP operating parameters using dynamic model based on BP neural network
Highlights: • A dynamic prediction model for NPP operating parameters was proposed. • The structure of continuous dynamic prediction system was designed. • Multi-threading technology was used in the system. • The system can predict the fluctuating data with high accuracy. - Abstract: A dynamic model was developed using two back-propagation neural networks of the same structure, one for online training and the other for prediction, and proposed for continuous dynamic prediction of the time series of NPP operating parameters. The proposed prediction model was validated by predicting such time series of NPP operating parameters as coolant void fraction, water level in SG and pressurizer. Validation results indicated the proposed model could be used to achieve a stable prediction effect with high prediction accuracy for the prediction of fluctuating data
Yu Jingyuan
2011-08-01
Full Text Available In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template (F, centrifugal acceleration (v and sintering temperature (T, while the only output was the ultimate compressive strength (σ. According to the registered BP model, the effects of F, v, T on σ were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.
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.
Airport noise prediction model based on BP neural network%一种 BP 神经网络机场噪声预测模型
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.
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.
Li Qiang; Zhang Fengfeng; Yu Jingyuan
2013-01-01
BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The influences of the load applied on the epispastic polystyrene template (F), the centrifugal acceleration (v) and sintering temperature (T) on the porosity (P) and compressive strength (σ) of the sintered products were studied by using the registered three-layer BP model. The accuracy of the model was verified by compa...
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...
Chunhui Li
2013-08-01
Full Text Available 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.
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
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。
Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing
基于BP神经网络的CCI预测模型%Prediction Model of CCI Based on BP Neural Network
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
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。
Li Qiang
2013-07-01
Full Text Available BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The influences of the load applied on the epispastic polystyrene template (F, the centrifugal acceleration (v and sintering temperature (T on the porosity (P and compressive strength (σ of the sintered products were studied by using the registered three-layer BP model. The accuracy of the model was verified by comparing the BP model predicted results with the experimental ones. Results show that the model prediction agrees with the experimental data within a reasonable experimental error, indicating that the three-layer BP network based modeling is effective in predicting both the properties and processing parameters in designing the gradient Al2O3-ZrO2 ceramic foam filter. The prediction results show that the porosity percentage increases and compressive strength decreases with an increase in the applied load on epispastic polystyrene template. As for the influence of sintering temperature, the porosity percentage decreases monotonically with an increase in sintering temperature, yet the compressive strength first increases and then decreases slightly in a given temperature range. Furthermore, the porosity percentage changes little but the compressive strength first increases and then decreases when the centrifugal acceleration increases.
BP Neural Network Based on Artificial Bee Colony Algorithm%基于人工蜂群的BP神经网络算法
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.
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
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。
Realization of Chinese text classification by using BP neural network%用BP神经网络实现中文文本分类
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
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BP神经网络的数字识别研究%Study of Digital Recognition Based on BP Neural Network
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.
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
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
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.
Vajda, Igor; Grim, Jiří
Oxford : Eolss Publishers-UNESCO, 2008 - (Parra-Luna, F.), s. 224-248 ISBN 978-1-84826-654-4. - (Encyclopedia of Life Support Systems. Volume III) R&D Projects: GA ČR GA102/07/1594 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * probabilistic approach Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2008/SI/vajda-systems science and cybernetics .pdf
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。
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.
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
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.
Research of Image Compression Based on Quantum BP Network
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.
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。
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
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.
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.
基于BP神经网络的水果分级研究%Classification of fruit based on the BP neural network
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
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。
Target Recognition Algorithm Based on BP Networks and Invariant Moments
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.
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.
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
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.
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
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.
BP Network Based Users' Interest Model in Mining WWW Cache
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.
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。
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.
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.
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.
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。
Chaotic diagonal recurrent neural network
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.
Chaotic diagonal recurrent neural network
We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning 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. (interdisciplinary physics and related areas of science and technology)
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.
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。
Recognition of Continuous Digits by Quantum Neural Networks
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.
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。
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—
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.
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
Uršič, Aleš
2012-01-01
The goal of this work is construction of an artificial life model and simulation of organisms in an environment with food. Organisms survive if they find food successfuly. With evolution and learning organisms develop a neural network which enables that. First neural networks and their history are introduced with the basic concepts like a neuron model, a network, transfer functions, topologies and learning. I describe the backpropagation learning on multilayer feed forward network and dem...
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
Remote Sensing Image Segmentation with Probabilistic Neural Networks
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.
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.
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
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.
Layered learning of soccer robot based on artificial neural network
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.
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.
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.
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
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...
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.
Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks
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.
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.
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....
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…
Term Structure of Interest Rates Based on Artificial Neural Network
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.
Recurrent Neural Network Regularization
Zaremba, Wojciech; Sutskever, Ilya; Vinyals, Oriol
2014-01-01
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.
Deep Sequential Neural Network
Denoyer, Ludovic; Gallinari, Patrick
2014-01-01
Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process. The resulting model is structured according to a DAG like architecture, so that a path from the root to a leaf node defines a sequence of transformations. Instead of c...
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。
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.
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
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.
Decoupling Control Method Based on Neural Network for Missiles
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
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
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。
A Modified Algorithm for Feedforward Neural Networks
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.
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。
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.
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
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.
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.
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
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
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.
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
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.
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.
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
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
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.
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.
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.
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.
Hyperbolic Hopfield neural networks.
Kobayashi, M
2013-02-01
In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states. PMID:24808287
Rule Extraction:Using Neural Networks or for Neural Networks?
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.
Applications of Neural Networks in Spinning Prediction
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.
Introduction to Artificial Neural Networks
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....
Neural network fault diagnosis method optimization with rough set and genetic algorithms
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.
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 Networks and Micromechanics
Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.
The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
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。
Inversion of surface parameters using fast learning neural networks
Dawson, M. S.; Olvera, J.; Fung, A. K.; Manry, M. T.
1992-01-01
A neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.
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
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.
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.
Sea ice classification using fast learning neural networks
Dawson, M. S.; Fung, A. K.; Manry, M. T.
1992-01-01
A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.
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.
Implementing Neural Networks Efficiently
Collobert, Ronan; Kavukcuoglu, Koray; Farabet, Clément; Montavon, Grégoire; Orr, Geneviève; Müller, K.-R.
2012-01-01
Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framework called Torch7, that is especially suited to achieve both of these competing goals. Torch7 is a versatile numeric computing framework and machine learning library that extends a very lightweight and powerful programming language Lua. Its ...
Neural networks for triggering
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.
Neural networks for triggering
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
Dynamic recurrent neural networks
Pearlmutter, Barak A
1990-01-01
We survey learning algorithms for recurrent neural networks with hidden units and attempt to put the various techniques into a common framework. We discuss fixpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non-fixpoint algorithms, namely backpropagation through time, Elman's history cutoff nets, and Jordan's output feedback architecture. Forward propagation, an online technique that uses adjoint equations, is also discussed. In many cases...
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。
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
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.
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
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。
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
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。
MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
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.
Using Artificial Neural Networks for ECG Signals Denoising
Zoltán Germán-Salló; Katalin György
2010-01-01
The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG) signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1)th sample from n previous samples To train and adjust the network weights, the backpropagation (BP) algorithm was used. In this paper, prediction of ECG signals (as...
Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network
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
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.
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.
Neural logic networks a new class of neural networks
Heng, Teh Hoon
1995-01-01
This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks wh
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
Parameter incremental learning algorithm for neural networks.
Wan, Sheng; Banta, Larry E
2006-11-01
In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable. PMID:17131658
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
Application of particle swarm optimization to identify gamma spectrum with neural network
In applying neural network to identification of gamma spectra back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of globe optimal searching. In this paper, we propose a new algorithm for neural network training, i.e. combined BP and PSO optimization, or PSO-BP algorithm. Practical example shows that the new algorithm can overcome shortcomings of BP algorithm and the neural network trained by it has a high ability of generalization with identification result of 100% correctness. It can be used effectively and reliably to identify gamma spectra. (authors)
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...
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
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.
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.
A Neural Network Based Collision Detection Engine for Multi-Arm Robotic Systems
Rana, A. S.; Zalzala, A.M.S.
1996-01-01
A neural ntwork is proposed for collision detection among multiple robotic arms sharing a common workspace. The structure of the neural network is a hybrid between Guassian Radial Basis Function (RBF) neural networks and Multi-layer perceptron back-propagation (BP) neural networks. This network is used to generate potential fields in the configuration space of the robotic arms. A path planning algorithm based on heuristics is presented. It is shown that this algorithm works better than the c...
Neural network applications in telecommunications
Alspector, Joshua
1994-01-01
Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.
Neural networks at the Tevatron
This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF
Neural Networks for Optimal Control
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....
Neural Networks for the Beginner.
Snyder, Robin M.
Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…
Chinese word sense disambiguation based on neural networks
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.
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.
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.
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％.
Artificial neural networks in NDT
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)
Food Safety Evaluation System Construction Based on Artificial Neural Network
Jian Wang; Zhenmin Tang; Xianli Jin
2015-01-01
This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.
Food Safety Evaluation System Construction Based on Artificial Neural Network
Jian Wang
2015-05-01
Full Text Available This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.
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.
Trends in neural network technology. Neural network gijutsu no doko
Nishimura, K. (Toshiba Corp., Tokyo (Japan))
1991-12-01
The present and future of neural network technologies were reviewed. Neural networks simulate the neurons and synapses of human brain, thus permitting the utilization of heuristic knowledge difficult to describe in a logical manner. Such networks can therefore solve optimization problems, difficult to solve by conventional computers, more rapidly while sacrificing a permissible degree of rigor. In light of these advantages, many attempts have been made to apply neural networks to a variety of engineering fields including character recognition, phonetic recognition diagnosis, operation and so on. Now that these attempts have demonstrated the great potential of neural network technology, its application to practical problems will receive increasing attention. The necessity for fundamental studies on learning algorithms, modularization techniques, hardware technologies and so on will grow in conjunction with the above trends in application. 20 refs., 11 figs., 1 tab.
Neural Networks in Control Applications
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...
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...
Improved Marquardt Algorithm for Training Neural Networks for Chemical Process Modeling
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.
A neural network method to evaluate consolidation coefficient
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.
Application of artificial neural network for NHR fault diagnosis
The author makes researches on 200 MW nuclear heating reactor (NHR) fault diagnosis system using artificial neural network, and use the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis comparing to the single network system
Principles of artificial neural networks
Graupe, Daniel
2013-01-01
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies
Modular, Hierarchical Learning By Artificial Neural Networks
Baldi, Pierre F.; Toomarian, Nikzad
1996-01-01
Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.
Intercurrent fault diagnosis of nuclear power plants based on hybrid artificial neural network
Based on the analysis of the structure of ART-2 and parallel BP neural network, a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants. Firstly the ART-2 net is used to identify the single fault, then the parallel BP net is used to distinguish intercurrent faults from new fault. The simulation shows that, the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault, and can diagnose the intercurrent fault and new fault efficiently. (authors)
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...
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.
Robotic velocity generation using neural network
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.
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.
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...
What are artificial neural networks?
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...
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
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。
Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model
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.
Neural networks: genuine artifical intelligence. Neurale netwerken: echte kunstmatige intelligentie
Jongepier, A.G. (KEMA NV, Arnhem (Netherlands))
Artificial neural networks are a new form of artificial intelligence. At this moment KEMA NV is examining the possibilities of applying artificial neural networks to processes that are related to power systems. A number of applications already gives hopeful results. Artificial neural networks are suited to pattern recognition. If a problem can be formulated in terms of pattern recognition, an artificial neural network may give a valuable contribution to the solution of this problem. 8 figs., 15 refs.
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...
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。
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.
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.
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
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
COCOMO Estimates Using Neural Networks
Anupama Kaushik
2012-08-01
Full Text Available Software cost estimation is an important phase in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most critical tasks and an accurate estimate provides a strong base to the development procedure. In this paper, the most widely used software cost estimation model, the Constructive Cost Model (COCOMO is discussed. The model is implemented with the help of artificial neural networks and trained using the perceptron learning algorithm. The COCOMO dataset is used to train and to test the network. The test results from the trained neural network are compared with that of the COCOMO model. The aim of our research is to enhance the estimation accuracy of the COCOMO model by introducing the artificial neural networks to it.
Neural Networks and Database Systems
Schikuta, Erich
2008-01-01
Object-oriented database systems proved very valuable at handling and administrating complex objects. In the following guidelines for embedding neural networks into such systems are presented. It is our goal to treat networks as normal data in the database system. From the logical point of view, a neural network is a complex data value and can be stored as a normal data object. It is generally accepted that rule-based reasoning will play an important role in future database applications. The knowledge base consists of facts and rules, which are both stored and handled by the underlying database system. Neural networks can be seen as representation of intensional knowledge of intelligent database systems. So they are part of a rule based knowledge pool and can be used like conventional rules. The user has a unified view about his knowledge base regardless of the origin of the unique rules.
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.
Automatic Generation of Neural Networks
A. Fiszelew; P. Britos; G. Perichisky; R. García-Martínez
2003-01-01
This work deals with methods for finding optimal neural network architectures to learn particular problems. A genetic algorithm is used to discover suitable domain specific architectures; this evolutionary algorithm applies direct codification and uses the error from the trained network as a performance measure to guide the evolution. The network training is accomplished by the back-propagation algorithm; techniques such as training repetition, early stopping and complex regulation are employ...
On the identification of quark and gluon jets using artificial neural network method
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
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.
Neural networks in signal processing
Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)
Relations Between Wavelet Network and Feedforward Neural Network
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.