Network Traffic Prediction based on Particle Swarm BP Neural Network
Directory of Open Access Journals (Sweden)
Yan Zhu
2013-11-01
Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.
RMB Exchange Rate Forecast Approach Based on BP Neural Network
Ye, Sun
RMB exchange rate system has reformed since July, 2005. This article chose RMB exchange rate data during a period from July, 2005 to September 2010 to establish BP neural network model to forecast RMB exchange rate in the future by using MATLAB software. The result showed that BP neural network is effective to forecast RMB exchange rate and also indicated that RMB exchange rate will continue to appreciate in the future.
Sub-pixel mapping method based on BP neural network
Institute of Scientific and Technical Information of China (English)
LI Jiao; WANG Li-guo; ZHANG Ye; GU Yan-feng
2009-01-01
A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
Risk assessment of logistics outsourcing based on BP neural network
Liu, Xiaofeng; Tian, Zi-you
The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.
The Application of BP Neural Network In Oil-Field
Directory of Open Access Journals (Sweden)
Pei-Ying ZHANG
2013-09-01
Full Text Available Aiming at the situation that many techniques of production performance analysis acquire lots of data and are expensive considering the computational and human resources, and their applications are limited, this paper puts forward a new method to analyze the production performance of oil-field based on the BP neural network. It builds a dataset with some available measured data such as well logs and production history, then, builds a field-wide production model by neural network technique, a model will be used to predict. The technique is verified, which shows that the predicted results are consistent with the maximum error of rate of oil production lower than 7% and maximum error of rate of water production lower than 5%, having certain application and research value.
Research on PGNAA adaptive analysis method with BP neural network
Peng, Ke-Xin; Yang, Jian-Bo; Tuo, Xian-Guo; Du, Hua; Zhang, Rui-Xue
2016-11-01
A new approach method to dealing with the puzzle of spectral analysis in prompt gamma neutron activation analysis (PGNAA) is developed and demonstrated. It consists of utilizing BP neural network to PGNAA energy spectrum analysis which is based on Monte Carlo (MC) simulation, the main tasks which we will accomplish as follows: (1) Completing the MC simulation of PGNAA spectrum library, we respectively set mass fractions of element Si, Ca, Fe from 0.00 to 0.45 with a step of 0.05 and each sample is simulated using MCNP. (2) Establishing the BP model of adaptive quantitative analysis of PGNAA energy spectrum, we calculate peak areas of eight characteristic gamma rays that respectively correspond to eight elements in each individual of 1000 samples and that of the standard sample. (3) Verifying the viability of quantitative analysis of the adaptive algorithm where 68 samples were used successively. Results show that the precision when using neural network to calculate the content of each element is significantly higher than the MCLLS.
Seabed Classification Using BP Neural Network Based on GA
Institute of Scientific and Technical Information of China (English)
Yang Fanlin; Liu Jingnan
2003-01-01
Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of different classes of seabed materials. In this paper, seabed image is classified according to BP neural network, and Genetic Algorithm is adopted in train network in this paper. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It considers not only the spatial correlation between different pixels, but also the terrain coarseness. The texture is denoted by the statistics of the co-occurrence matrix. Double Blanket algorithm is used to calculate dimension. Because a uniform fractal may not be sufficient to describe a seafloor, two dimensions are calculated respectively by the upper blanket and the lower blanket. However, in sonar image, fractal has directivity, i. e.there are different dimensions in different direction. Dimensions are different in acrosstrack and alongtrack, so the average of four directions is used to solve this problem. Finally, the real data verify the algorithm. In this paper, one hidden layer including six nodes is adopted. The BP network is rapidly and accurately convergent through GA. Correct classification rate is 92.5% in the result.
Spiking DNA Computing with applications to BP Neural Networks Classification
Directory of Open Access Journals (Sweden)
Wenke Zang
2012-08-01
Full Text Available The study uses the idea of the extreme parallel to solve the BP neural network classification. Modification of the weights is not the traditional method which is to modify the connection weights between neurons repeatedly, but to find a group of weights in all possible weights combinations. The groups of weights are suitable for the relationship of the ideal input and the ideal output. Therefore, the model has some advantages compared with the traditional serial model in time miscellaneous. In the actual DNA computing, we also associate the coding problem with the model design. The coding problem is an important issue worthy to study in the DNA computing. There are many factors affecting the coding. The coding in this study is made when certain factors are overlooked.
Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization
Directory of Open Access Journals (Sweden)
Baoyu Xu
2015-01-01
Full Text Available The model and algorithm of BP neural network optimized by expanded multichain quantum optimization algorithm with super parallel and ultra-high speed are proposed based on the analysis of the research status quo and defects of BP neural network to overcome the defects of overfitting, the random initial weights, and the oscillation of the fitting and generalization ability along with subtle changes of the network parameters. The method optimizes the structure of the neural network effectively and can overcome a series of problems existing in the BP neural network optimized by basic genetic algorithm such as slow convergence speed, premature convergence, and bad computational stability. The performance of the BP neural network controller is further improved. The simulation experimental results show that the model is with good stability, high precision of the extracted parameters, and good real-time performance and adaptability in the actual parameter extraction.
Optimization Design based on BP Neural Network and GA Method
Directory of Open Access Journals (Sweden)
Bing Wang
2013-12-01
Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
A Worsted Yarn Virtual Production System Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
董奎勇; 于伟东
2004-01-01
Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Zhang Cuiping; Yang Qingfo
2003-01-01
According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.
Research on fault location technology based on BP neural network in DWDM optical network
Institute of Scientific and Technical Information of China (English)
LIAO Xiao-min; ZHANG Yin-fa; YANG Shi-ping; LIN Chu-shan
2008-01-01
BP neural network is introduced to the fault location field of DWDM optical network in this paper. The alarm characteris-tics of the optical network equipments are discussed, and alarm vector and fault vector diagrams are generated by analyzingsome typical instances. A 17×14×18 BP neural network structure is constructed and trained by using MATLAB. Bycomparing the training performances, the best training algorithm of fault location among the three training algorithms ischosen. Numerical simulation results indicate that the sum squared error (SSE) of fault location is less than 0.01, and theprocessing time is less than 100 ms. This method not only well deals with the missing alarms or false alarms, but alsoimproves the fault location accuracy and real-time ability.
Breakout Prediction Based on BP Neural Network in Continuous Casting Process
Directory of Open Access Journals (Sweden)
Zhang Ben-guo
2016-01-01
Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.
A BP neural network model for sea state recognition using laser altimeter
Shi, Chun-bo; Jia, Xiao-dong; Li, Sheng; Wang, Zhen
2009-07-01
A BP neural network method for the recognition of sea state in laser altimeter is presented in this paper. Sea wave is the typical stochastic disturbance factor of laser altimeter effecting on low-altitude defense penetration of the intelligent antiship missiles, the recognition of sea state is studied in order to satisfy the practical needs of flying over the ocean. The BP neural network fed with the feature vector of laser range-measurement presents the analysis of features and outputs the estimation result of sea state. The two most distinguishing features are the mean and the variance of the sea echo, which are extracted from the distance characteristics of sea echo using general theory of statistics. The use of a feedforward network trained with the back-propagation algorithm is also investigated. The BP neural network is trained using sample data set to the neural network, and then the BP neural network trained is tested to recognize the sea state waiting for the classification. The network output shows the recognition accuracy of the model can up to 88%, and the results of tests show that the BP neural network model for the recognition of sea state is feasible and effective.
An improved BP artificial neural network algorithm for urban traffic flow intelligent prediction
Institute of Scientific and Technical Information of China (English)
XIONG Shi-yong; ZHANG Yi
2009-01-01
The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Directory of Open Access Journals (Sweden)
Wei He
2013-01-01
Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.
Application of BP neural networks in non-linearity correction of optical tweezers
Institute of Scientific and Technical Information of China (English)
Ziqiang WANG; Yinmei LI; Liren LOU; Henghua WEI; Zhong WANG
2008-01-01
The back-propagation (BP) neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem. Considering the low convergence rate of the BP algo-rithm, the Levenberg-Marquardt (LM) algorithm is used to improve the BP network. The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics. The result shows that with the nonlinear correction using BP net-works, the range of force measurement of an optical tweezer system is enlarged by 30% and the precision is also improved compared with the polynomial fitting method. It is demonstrated that nonlinear correction by the neural network method effectively improves the per-formance of optical tweezers without adding or changing the measuring system.
Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
ZHANG Jun-hong; XIE An-guo; SHEN Feng-man
2007-01-01
A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.
Pulse frequency classification based on BP neural network
Institute of Scientific and Technical Information of China (English)
WANG Rui; WANG Xu; YANG Dan; FU Rong
2006-01-01
In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.
Institute of Scientific and Technical Information of China (English)
HANG Li-hong; WANG Ai-guo; TIAN Nai-yuan; ZHANG Wei-cun; FAN Qiao-li
2011-01-01
The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of technological parameters of continuous casting process directly impacts the property of slab. The relationships between continuous casting technological parameters and cooling rate of slab for spring steel were built using BP neural network model, based on which, the relevant secondary dendrite arm spacing was calculated. The simulation calculation was also carried out using the industrial data. The simulation results show that compared with that of the traditional method, the absolute error of calculation result obtained with BP neural network model reduced from 0. 015 to 0. 0005, and the relative error reduced from 6, 76 % to 0.22 %. BP neural network model had a more precise accuracy in the optimization of continuous casting technological parameters.
Research on safety assessment of gas explosion hazard in heading face based on BP neural network
Institute of Scientific and Technical Information of China (English)
TIAN Shui-cheng; ZHU Li-jun; CHEN Yong-gang; WANG Li
2005-01-01
According to hazard theory and the principle of selecting assessment index,combining the causes and mechanism of gas explosion, established assessment index system of gas explosion in heading face. Based on the method of gray clustering, principle of BP neural network and characters of gas explosion in heading face, safety assessment procedural diagram of BP neural network on gas explosion hazard in heading face is designed. Meanwhile, concrete heading face of the gas explosion hazard is assessed by safety assessment method of BP neural network and grades of comprehensive safety assessment are got. The static and dynamic safety assessment can be achieved by this method. It is practical to improve safety management and to develop safety assessment technology in coalmine.
Application of New Type BP Neural Networks for Magnetic Measurement
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods to solve this kind of inverse problem has all kinds of shortcomings, BPNN (Back Propagation Neural Networks) method can be used to solve this typical inverse problem fast enough for real time measurement. In the traditional BPNN method, gradient descent search method is performed for error propagation. In this paper the authors propose a new algorithm that Newton method is performed for error propagation. For the cost function is highly nonconvex in the magnetic measurement problem, the new kind of BPNN can get convergent results quickly and precisely. A simulation result for this method is also presented.
Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
ZHOU Mei-hua; WANG Fu-dong; ZHANG Hong-hong
2006-01-01
For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning results of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.
A Prediction Model of Peasants’ Income in China Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
2011-01-01
According to the related data affecting the peasants’ income in China in the years 1978-2008,a total of 13 indices are selected,such as agricultural population,output value of primary industry,and rural employees.Based on the standardized method and BP neural network method,the peasants’ income and the artificial neural network model are established and analyzed.Results show that the simulation value agrees well with the real value;the neural network model with improved BP algorithm has high prediction accuracy,rapid convergence rate and good generalization ability.Finally,suggestions are put forward to increase the peasants’ income,such as promoting the process of urbanization,developing small and medium-sized enterprises in rural areas,encouraging intensive operation,and strengthening the rural infrastructure and agricultural science and technology input.
Circuit Design of On-Chip BP Learning Neural Network with Programmable Neuron Characteristics
Institute of Scientific and Technical Information of China (English)
卢纯; 石秉学; 陈卢
2000-01-01
A circuit system of on chip BP(Back-Propagation) learning neural network with pro grammable neurons has been designed,which comprises a feedforward network,an error backpropagation network and a weight updating circuit. It has the merits of simplicity,programmability, speedness,low power-consumption and high density. A novel neuron circuit with pro grammable parameters has been proposed. It generates not only the sigmoidal function but also its derivative. HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1.2tμm CMOS process. The results show that both functions are matched with their respec ive ideal functions very well. The non-linear partition problem is used to verify the operation of the network. The simulation result shows the superior performance of this BP neural network with on-chip learning.
Coal mine safety production forewarning based on improved BP neural network
Institute of Scientific and Technical Information of China (English)
Wang Ying; Lu Cuijie; Zuo Cuiping
2015-01-01
Firstly, the early warning index system of coal mine safety production was given from four aspects as per-sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO-BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarning management of coal mine safety production.
Institute of Scientific and Technical Information of China (English)
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%.
Institute of Scientific and Technical Information of China (English)
LONG Jiangqi; LAN Fengchong; CHEN Jiqing; YU Ping
2009-01-01
For optimal design of mechanical clinching steel-aluminum joints, the back propagation (BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm (GA) is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM(R) Tog-L-Loc system with different technologic parameters. The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network (ANN) for training. The validating samples' experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.
Yan, Li; Liu, Li
2010-07-01
An approach of using near infrared spectroscopy combined with BP neural network method was investigated for the prediction of fibre contents of textile mixture materials. The near infrared spectra of 56 textile mixture samples with different cotton and wool contents were obtained, in which 41 samples were used for the calibration set, 10 samples were used for the validation set, while 5 for the prediction set. The wavelet transform (WT) was utilized for the spectra data compression, which combined with BP neural network (BP) was specially introduced. According to the standards of absolute error (AE), mean absolute error (MAE) and root mean square error (RMSE), a calibration model of WT-ca3-BP (41-17-2) was achieved for prediction of fibre contents of textile mixture materials. The calibration set was in combination with validation set as a new calibration set, an upgraded WT-ca3-BP (51-17-2) model appeared, its mean absolute error (MAE) was less than 0.41%, root mean square error (RMSE) was less than 0.54% and a satisfying prediction precision was achieved for unknown samples. The results indicated that near infrared spectroscopy could be successfully applied for prediction of fibre contents of textile mixture materials and upgraded WT-ca3-BP model could achieve a best prediction results.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)
2006-10-15
The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
A new grey forecasting model based on BP neural network and Markov chain
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system's known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model (GNNM(1,1)) was built. In order to reduce stochastic phenomenon in GNNM(1,1), the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model(MNNGM(1,1)) was proposed by combining Markov chain with GNNM(1,1). Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM(1,1) and GNNM(1,1) with the proposed model, the results indicate that the absolute mean error of MNNGM(1,1) is about 0.4 times of GNNM(1,1) and 0.2 times of GM(1,1), and the mean square error of MNNGM(1,1) is about 0.25 times of GNNM(1,1) and 0.1 times of GM(1,1).
Directory of Open Access Journals (Sweden)
Heqing Li
2013-07-01
Full Text Available The basic Particle Swarm Optimization (PSO algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time.
The risk evaluation of mine coal-dust explosion based on BP neural network
Institute of Scientific and Technical Information of China (English)
CHEN Lian-jun; CHENG Wei-min
2007-01-01
Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and factor of men, which affect the safety and reliability of coal-dust explosion risk system and then builds up the risk factor indices of coal-dust explosion according to analysis of conditions inducing the coal-dust explosion. It fixes the risk degree of coal-dust explosion risk system by analyzing loss probability and loss scope of risk system and by means of the probabilistic hazard evaluation method and risk matrix method, etc.. According to the feature of strong capability of nonlinear approximation of BP neural network, the paper designed the structure of BP neural network for the risk evaluation of the mine coal-dust explosion with BP neural network. And the weight of the network was finally determined by training the given samples so that the risk degree of samples to be measured could be exactly evaluated and the risk of mine coal-dust explosion could be alarmed in good time.
Study of predicting breakdown voltage of stator insulation in generator based on BP neural network
Institute of Scientific and Technical Information of China (English)
Jiang Yuao; Zhang Aide; Liu Libing; Du Yu; Gao Naikui; Peng Zongren
2007-01-01
The breakdown voltage plays an important role in evaluating residual life of stator insulation in generator. In this paper, we discussed BP neural network that was used to predict the breakdown voltage of stator insulation in generator of 300 MW/18 kV. At first the neural network has been trained by the samples that include the varieties of dielectric loss factor tanδ, the partial discharge parameters and breakdown voltage. Then we tried to predict the breakdown voltage of samples and stator insulations subjected to multi-stress aging by the trained neural network. We found that it's feasible and accurate to predict the voltage. This method can be applied to predict breakdown voltage of other generators which have the same insulation structure and material.
D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process
Directory of Open Access Journals (Sweden)
Shu-zhi Gao
2014-01-01
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm
Directory of Open Access Journals (Sweden)
Jianyong Liu
2015-01-01
Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.
Institute of Scientific and Technical Information of China (English)
季学武; 王健; 赵又群; 刘亚辉; 臧利国; 李波
2015-01-01
In order to diminish the impacts of external disturbance such as parking speed fluctuation and model un-certainty existing in steering kinematics, this paper presents a parallel path tracking method for vehicle based on pre-view back propagation (BP) neural network PID controller. The forward BP neural network can adjust the parameters of PID controller in real time. The preview time is optimized by considering path curvature, change in curvature and road boundaries. A fuzzy controller considering barriers and different road conditions is built to select the starting po-sition. In addition, a kind of path planning technology satisfying the requirement of obstacle avoidance is introduced. In order to solve the problem of discontinuous curvature, cubic B spline curve is used for curve fitting. The simulation results and real vehicle tests validate the effectiveness of the proposed path planning and tracking methods.
One Prediction Model Based on BP Neural Network for Newcastle Disease
Wang, Hongbin; Gong, Duqiang; Xiao, Jianhua; Zhang, Ru; Li, Lin
The purpose of this paper is to investigate the correlation between meteorological factors and Newcastle disease incidence, and to determine the key factors that affect Newcastle disease. Having built BP neural network forecasting model by Matlab 7.0 software, we tested the performance of the model according to the coefficient of determination (R2) and absolute values of the difference between predictive value and practical incidence. The result showed that 6 kinds of meteorological factors determined, and the model's coefficient of determination is 0.760, and the performance of the model is very good. Finally, we build Newcastle disease forecasting model, and apply BP neural network theory in animal disease forecasting research firstly.
Water quality forecast through application of BP neural network at Yuqiao reservoir
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.
Effective Multifocus Image Fusion Based on HVS and BP Neural Network
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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.
Particle Swarm Optimization-based BP Neural Network for UHV DC Insulator Pollution Forecasting
Directory of Open Access Journals (Sweden)
Fangcheng Lü
2014-02-01
Full Text Available In order to realize the forecasting of the UHV DC insulator's pollution conditions, we introduced a PSOBP algorithm. A BP neural network (BPNN with leakage current, temperature, relative humidity and dew point as input neurons, and ESDD as output neuron was built to forecast the ESDD. The PSO was used to optimize the the BPNN, which had great improved the convergence rate of the BP neural network. The dew point as a brand new input unit has improved the iteration speed of the PSOBP algorithm in this study. It was the first time that the PSOBP algorithm was applied to the UHV DC insulator pollution forecasting. The experiment results showed that the method had great advantages in accuracy and speed of convergence. The research showed that this algorithm was suitable for the UHV DC insulator pollution forecasting.
Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
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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.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.
Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum
Mu, Tao-tao; Chen, Si-ying; Zhang, Yin-chao; Guo, Pan; Chen, He; Zhang, Hong-yan; Liu, Xiao-hua; Wang, Yuan; Bu, Zhi-chao
2013-09-01
In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.
Learning algorithm and application of quantum BP neural networks based on universal quantum gates
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A quantum BP neural networks model with learning algorithm is proposed.First,based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate,a quantum neuron model is constructed,which is composed of input,phase rotation,aggregation,reversal rotation and output.In this model,the input is described by qubits,and the output is given by the probability of the state in which |1＞ is observed.The phase rotation and the reversal rotation are performed by the universal quantum gates.Secondly,the quantum BP neural networks model is constructed,in which the output layer and the hide layer are quantum neurons.With the application of the gradient descent algorithm,a learning algorithm of the model is proposed,and the continuity of the model is proved.It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed,convergence rate and robustness,by two application examples of pattern recognition and function approximation.
Research on the Prediction Model of CPU Utilization Based on ARIMA-BP Neural Network
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Wang Jina
2016-01-01
Full Text Available The dynamic deployment technology of the virtual machine is one of the current cloud computing research focuses. The traditional methods mainly work after the degradation of the service performance that usually lag. To solve the problem a new prediction model based on the CPU utilization is constructed in this paper. A reference offered by the new prediction model of the CPU utilization is provided to the VM dynamic deployment process which will speed to finish the deployment process before the degradation of the service performance. By this method it not only ensure the quality of services but also improve the server performance and resource utilization. The new prediction method of the CPU utilization based on the ARIMA-BP neural network mainly include four parts: preprocess the collected data, build the predictive model of ARIMA-BP neural network, modify the nonlinear residuals of the time series by the BP prediction algorithm and obtain the prediction results by analyzing the above data comprehensively.
Institute of Scientific and Technical Information of China (English)
LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi
2005-01-01
An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.
BP-Neural-Network-Based Tool Wear Monitoring by Using Wavelet Decomposition of the Power Spectrum
Institute of Scientific and Technical Information of China (English)
ZHENG Jian-ming; XI Chang-qing; LI Yan; XIAO Ji-ming
2004-01-01
In a drilling process, the power spectrum of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the power spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet transform and extraction of the low frequency decomposition coefficient us the envelope information of the power spectrum. Intelligent identification of the tool wear status is achieved in the drilling process through fusing the wavelet decomposition coefficient of the power spectrum by using a BP ( Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural networks show high identification precision and the ability of extension.
Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
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Jianjin Wang
2017-01-01
Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.
BP Neural Network Model-based Physical Exercises and Dietary Habits Relationships Exploration.
Guo, Xingwei; Zhang, Xuesheng; Sun, Yi
2015-01-01
With the continuous progress of society, increment of social pressure, people have paid little and little attentions to physical exercises and dietary necessity. Take Beijing, Shanghai, Guangzhou, Shenzhen, Shijiazhuang and Baotou university students as research objects, targeted at physical exercises time and dietary habits, it starts investigation. Make principal component analysis of investigation results, results indicates that cereal intake is principal component in dietary habits; strenuous exercise time and general physical exercise time are the principal components in physical exercise. Utilize BP neural network model, analyze these seven cities' physical exercises and dietary habits conditions, the result indicates that except for Shenzhen, all the other six cities haven't reached the standard.
Directory of Open Access Journals (Sweden)
Cheng Yugui
2013-07-01
Full Text Available A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.
An Evaluating Model for Enterprise's Innovation Capability Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
HU Wei-qiang; WANG Li-xin
2007-01-01
To meet the challenge of knowledge-based economy in the 21st century, scientifically evaluating the innovation capability is important to strengthen the international competence and acquire long-term competitive advantage for Chinese enterprises. In the article, based on the description of concept and structure of enterprise's innovation capability, the evaluation index system of innovation capability is established according to Analytic Hierarchy Process (AHP). In succession, evaluation model based on Back Propagation (BP) neural network is put forward, which provides some theoretic guidance to scientifically evaluating the innovation capability of Chinese enterprises.
NEURAL NETWORK BP MODEL APPROXIMATION AND PREDICTION OF COMPLICATED WEATHER SYSTEMS
Institute of Scientific and Technical Information of China (English)
张韧; 余志豪; 蒋全荣
2001-01-01
An artificial neural network BP model and its revised algorithm are used to approximate quite successfully a Lorenz chaotic dynamic system and the mapping relation is established between the indices of Southern Oscillation and equatorial zonal wind and lagged equatorial eastern Pacific sea surface temperature (SST) in the context of NCEP/NCAR data, and thereby a model is prepared.The constructed net model shows fairly high fit precision and feasible prediction accuracy, thus making itself of some usefulness to the prognosis of intricate weather systems.
Temperature prediction and analysis based on BP and Elman neural network for cement rotary kiln
Yang, Baosheng; Ma, Xiushui
2011-05-01
In order to reduce energy consumption and improve the stability of cement burning system production, it is necessary to conduct in-depth analysis of the cement burning system, control the operation state and law of the system. In view of the rotary kiln consumes most of the fuel, we establish the simulation model of the cement kiln used to find effective control methods. It is difficult to construct mathematical model for the rotary cement kiln as the complex parameters, so we expressed directly using neural network method to establish the simulation model for the kiln. Choosing reasonable state and control variables and collecting actual operation data to train neural network weights. We first in-depth analyze mechanism and working parameters correlation to determine factors of the yield and quality as the model input variables; then constructed cement kiln model based on BP and Elman network, both achieved good fitting results. Elman network model has a faster convergence speed, high precision and good generalization ability. So the Elman network based model can be used as simulation model of the cement rotary kiln for exploring new control method.
BP neural network based online prediction of steam turbine exhaust dryness
Institute of Scientific and Technical Information of China (English)
XIE Danmei; CHEN Chang; XIONG Yangheng; GAO Shang; WANG Chun
2014-01-01
In large scale condensing turbine unit,the exhaust status always lies in wet steam area.Due to the lack of effective measuring method,the exhaust dryness of the steam turbine is difficult to obtain di-rectly,which has been the difficult problem in online economic analysis for thermal power units.By taking an N1000-25/600/600 ultra-supercritical steam turbine as an example,the nonlinear mapping ability of BP neural network was used to establish a model which can reflect the relationship between exhaust dryness and unit load and exhaust pressure.After learning and training under some typical conditions,this model was used for exhaust dryness online calculation under full condition.The results show the final error of the training samples and verifying samples were controlled within -0.006 1 and -0.001 0,which satisfies the accuracy requirement for engineering calculation,indicating the established BP neural network can be used in exhaust dryness prediction.
Directory of Open Access Journals (Sweden)
Reza Azad
2014-06-01
Full Text Available In recent years, face detection has been thoroughly studied due to its wide potential applications, including face recognition, human-computer interaction, video surveillance, etc.In this paper, a new and illumination invariant face detection method, based on features inspired by the human's visual cortexand applying BP neural network on the extracted featureset is proposed.A feature set is extracted from face and non-face images, by means of a feed-forward model, which contains a view and illumination invariant C2 features from all images in the dataset. Then, these C2 feature vector which derived from a cortex-like mechanism passed to a BP neural network. In the result part, the proposed approach is applied on FEI and Wild face detection databases and high accuracy rate is achieved. In addition, experimental results have demonstrated our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the first best result on all tested challenging face detection databases.
Application of genetic BP network to discriminating earthquakes and explosions
Institute of Scientific and Technical Information of China (English)
边银菊
2002-01-01
In this paper, we develop GA-BP algorithm by combining genetic algorithm (GA) with back propagation (BP) algorithm and establish genetic BP neural network. We also applied BP neural network based on BP algorithm and genetic BP neural network based on GA-BP algorithm to discriminate earthquakes and explosions. The obtained result shows that the discriminating performance of genetic BP network is slightly better than that of BP network.
Institute of Scientific and Technical Information of China (English)
ZHANG Wei; LIANG Cheng-hao
2005-01-01
Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.
The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
LI Shao-kun; SUO Xing-mei; BAI Zhong-ying; QI Zhi-li; Liu Xiao-hong; GAO Shi-ju; ZHAO Shuang-ning
2002-01-01
Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixei labeling was used to segment image and extract feature. (3)A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.
Prediction Method of Vessel Maintenance Outlay Based on the BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭冰冰; 黎放; 王威
2002-01-01
With the development of technology, the performance of vessel equipment is improved, the structure is more complicated, the automation level is enhanced, the source needed by maintenance is increased and the outlay is rising day by day. For these questions, this paper analyzes the factors that affect the outlay of equipment maintenance, and describes the computational principle of the BP (back propagation) artificial neural network and its applications in the maintenance of naval ship and craft. Finally, a dynamic investment prediction model of outlay for the military equipment maintenance is designed. It is important for decreasing the entire ilfe period outlay and drawing up the maintenance plan and programming to analyze the position and action of maintenance outlay in entire life period outlay.
Li, Zhaokun; Zhao, Xiaohui
2017-02-01
The sensor-less adaptive optics (AO) is one of the most promising methods to compensate strong wave front disturbance in free space optics communication (FSO). The back propagation (BP) artificial neural network is applied for the sensor-less AO system to design a distortion correction scheme in this study. This method only needs one or a few online measurements to correct the wave front distortion compared with other model-based approaches, by which the real-time capacity of the system is enhanced and the Strehl Ratio (SR) is largely improved. Necessary comparisons in numerical simulation with other model-based and model-free correction methods proposed in Refs. [6,8,9,10] are given to show the validity and advantage of the proposed method.
Antenna Recognition Based on BP Neural Network%基于BP神经网络的天线识别
Institute of Scientific and Technical Information of China (English)
赵春燕; 石丹; 高攸纲; 陈亚洲
2014-01-01
本文比较研究了BP神经网络中的几种常用算法，针对这些不同算法下的BP神经网络进行训练，并得出了各自网络的性能。在此基础上，针对经典BP算法和LM算法进行对比研究，找到LM算法的改进之处。此外，在实际的应用中表明，不仅不同的BP算法对网络的运算速度、泛化能力等有较大的影响，而且BP神经网络对隐含层神经元数目也很敏感。我们希望在BP神经网络的基础上，构建一种合适的天线模型，来应用于天线的分类识别，这将具有很大的现实意义。%This paper studies several commonly used algorithms in the BP neural network. The BP neural network under these different algorithms is trained to can see the various performance of their networks. The study of both classical BP algorithm and LM algorithm will find the improvements of LM algorithm. In addition, practical applications show the following things. For one hand, different BP algorithm influences the speed of the network. For the other hand, the number of hidden layer neurons is also a sensitive factor to the performance of BP neural network. On the basis of the BP neural network, we want to build a suitable antenna model and use it in the identification of the antenna, so it has great practical significance.
Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu
2013-01-01
As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…
Directory of Open Access Journals (Sweden)
Zeng Jie
2016-01-01
Full Text Available Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.
OPTIMIZATION OF INJECTION MOLDING PROCESS BASED ON NUMERICAL SIMULATIONAND BP NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
王玉; 邢渊; 阮雪榆
2001-01-01
Plastic injection molding is a very complex process and its process planning has a direct influence on product quality and production efficiency. This paper studied the optimization of injection molding process by combining the numerical simulation with back-propagation(BP) networks. The BP networks are trained by the results of numerical simulation. The trained BP networks may:(1) shorten time for process planning;(2) optimize process parameters;(3) be employed in on-line quality control;(4) be integrated with knowledge-based system(KBS) and case-based reasoning(CBR) to make intelligent process planning of injection molding.
BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species
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Limin Jiang
2016-01-01
Full Text Available MicroRNAs (miRNAs are a set of short (21–24 nt noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.
Research on the head form design of service robots based on Kansei engineering and BP neural network
Zhu, Yan; Chen, Gang
2017-01-01
It is always a difficult problem to demonstrate the users' perceptual demand in the form design of home service robots. In this paper, the relationship between the design elements of the head form of home service robots and the perceptual evaluation of users is analyzed quantitatively by Kansei engineering and BP neural network. Finally, the aided design system of home service robots' head form is constructed by using VB language with the trained BP network and 3D modeling software. Furthermore, it's considered that the results should be applied to the overall form design of home service robots and the impacts of different design constraints should also be incorporated as the input layer of BP network. Thus the more comprehensive aided design system of home service robots could be established.
Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network
Institute of Scientific and Technical Information of China (English)
彭源; 莫玉龙
2003-01-01
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery.Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between theimpedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.
Autogenous shrinkage prediction on high-performance concrete of fly ash based on BP neural network
Wang, Baomin; Zhang, Wenping; Wang, Lijiu
2006-11-01
The article adopts test data of neural network for autogenous shrinkage to train and predict on the data which doesn't join training. The article's prediction is on the basis of common medium sand, 5-31.5mm limestone rubble, second class fly-ash, P.O42.5 silicate cement, considering factors include five ones such as ratio of water and cement, sand rate, content of cement, content of fly ash, etc.By adjusting various parameters of neural network structure, it obtains three optimized results of neural network simulation. The error between concrete autogtenous shrinkage value of neural network prediction and trial value is within 3%, which can meet requirement of the concrete engineering.
The Application of LM-BP Neural Network in the Prediction of Total Output Value of Agriculture
Institute of Scientific and Technical Information of China (English)
Zimin; ZHANG; Yanying; FAN; Guanping; CHEN
2015-01-01
Gross agricultural product is an important indication to measure the agricultural development level of a region. It would be affected by many factors,having the characteristics of non- linearity. For this reason,LM- BP neural network was put forward as the model and method for predicting gross agricultural product. Taking the indications of the sown area of crop,the output of grain,sugarcane,cassava,tea,meat,aquatic products,turpentine and camellia seed,etc. as inputs,during 2000 to 2012 in Guangxi,the gross agricultural product data from the analysis of simulation experiment show that the prediction of LM- BP neural network fits well with actual results.
Liu, Pudong; Zhou, Jiayuan; Shi, Runhe; Zhang, Chao; Liu, Chaoshun; Sun, Zhibin; Gao, Wei
2016-09-01
The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm 711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.
Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona
2016-10-01
The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.
Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment
Institute of Scientific and Technical Information of China (English)
杨卫平; 廖宁放; 柴冰华; 胡中平; 白力; 栗兆剑
2003-01-01
A CRT characterization method based on color appearance matching is presented. A matching between Munsell color chips and CRT charts was obtained in vision perceiver in typical office environment and viewing condition by recommending. And neural networks were utilized to accomplish the color space conversion from CIE standard color space to CRT device color space. The neural networks related the color space conversion and color reproduction of soft/hard-copy directly to the influence of the illuminance and viewing condition in vision perceiver. The average color difference of training samples is 3.06 and that of testing samples is 5.17. The experiment results indicated that the neural networks can satisfy the requirements for the color appearance of hard-copy reproduction in CRT.
Energy Technology Data Exchange (ETDEWEB)
Sung, W.; Yoo, I.; Ra, S. [Hanyang Univ., Seoul (Korea, Republic of). Mineral and Petroleum Engineering Dept.; Park, H.
1996-08-01
The back propagation (BP) neural network approach has been the subject of recent focus because it can identify models for incomplete or distorted data without performing data preparation procedures. However, this approach uses only partial sets of data to reduce computing time and memory, and it may miss the points representing characteristics of the curve shape. Therefore, the resulted model may not be correct, forcing one to use sequential neural nets to find the correct model. The authors present the Hough Transform (HT) method combined with the BP neural network to improve this problem. With the aid of an HT, one can extract one simple pattern, including noisy and extraneous points, from the full-set data. A number of exercises also have been conducted for the published well-test data with the artificial intelligence neutral network identification system (ANNIS) they developed. The results show that ANNIS is quite reliable, especially for the incomplete or distorted data. They also demonstrate that the modified Levenberg-Marquart interpretation model, also developed in this work, successfully estimates reservoir parameters.
The Signal Extraction of Fetal Heart Rate Based on Wavelet Transform and BP Neural Network
Institute of Scientific and Technical Information of China (English)
YANG Xiao-hong; ZHANG Bang-cheng; FU Hu-dai
2005-01-01
This paper briefly introduces the collection and recognition of biomedical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI, the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women' s signals of FM were collected successfully by using the sensor. The correct of BP network recognition is about 83.3% by using the above measure.
Flux-measuring approach of high temperature metal liquid based on BP neural networks
Institute of Scientific and Technical Information of China (English)
胡燕瑜; 桂卫华; 李勇刚
2003-01-01
A soft-measuring approach is presented to measure the flux of liquid zinc with high temperature andcausticity. By constructing mathematical model based on neural networks, weighing the mass of liquid zinc, the fluxof liquid zinc is acquired indirectly, the measuring on line and flux control are realized. Simulation results and indus-trial practice demonstrate that the relative error between the estimated flux value and practical measured flux value islower than 1.5%, meeting the need of industrial process.
Application of Optimized BP Neural Network in Addressing for Garbage Power Plant
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects.One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.
HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds
Institute of Scientific and Technical Information of China (English)
CHI Yong; WEN Jun-ming; ZHANG Dong-ping; YAN Jian-hua; NI Ming-jiang; CEN Ke-fa
2005-01-01
The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a ф150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The Hclemission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit.When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased,if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14 x 6 × 1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal cofired fluidized bed incinerator.
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 phi 150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission 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 x 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 co-fired fluidized bed incinerator.
Directory of Open Access Journals (Sweden)
Fuqiang Zhou
2014-10-01
Full Text Available This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory.
Institute of Scientific and Technical Information of China (English)
LIU Man-lan; ZHU Chun-bo; WANG Tie-cheng
2005-01-01
In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.
Directory of Open Access Journals (Sweden)
Jie-sheng Wang
2014-01-01
Full Text Available For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
A Novel Method for Iris Recognition Using BP Neural Network and Parallel Computing
Directory of Open Access Journals (Sweden)
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.
The prediction in computer color matching of dentistry based on GA+BP neural network.
Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang
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 and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.
Directory of Open Access Journals (Sweden)
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.
Chen, Ying; Liu, Teng; Wang, Wenyue; Zhu, Qiguang; Bi, Weihong
2015-04-01
According to the band gap and photon localization characteristics, the single-arm notching and the double-arm notching Mach-Zehnder interferometer (MZI) structures based on 2D triangular lattice air hole-typed photonic crystal waveguide are proposed. The back-propagation (BP) neural network is introduced to optimize the structural parameters of the photonic crystal MZI structure, which results in the normalized transmission peak increasing from 85.3% to 97.1%. The sensitivity performances of the two structures are compared and analyzed using the Salmonella solution samples with different concentrations in the numerical simulation. The results show that the sensitivity of the double-arm notching structure is 4583 nm/RIU, which is about 6.4 times of the single-arm notching structure, which can provide some references for the optimization of the photonic devices and the design of high-sensitivity biosensors.
A BP Neural Network Programmed by LabVIEW%基于LabVIEW实现的BP神经网络
Institute of Scientific and Technical Information of China (English)
刘姝敏; 刘海龙
2015-01-01
图形化编程环境LabVIEW编写图形化语言程序可以有效提高设计者的编程效率。人工智能可以利用计算机模拟人类大脑的思维。基于LabVIEW编写的BP神经网络，可以方便灵活的应用于人类的各种生产经营活动。%The programming efficiency can be improved by using LabVIEW graphical language to program. Artificial intelligence can simulate human brain’s thinking using computer. The BP neural network programmed by LabVIEW can be widely and easily used in human’s various activities.
Tracking Control of Mobile Robot Based on BP Neural Network%基于 BP 神经网络的移动机器人循迹控制
Institute of Scientific and Technical Information of China (English)
雷双江; 肖世德; 熊鹰; 查峰
2013-01-01
研制自动控制移动机器人循迹控制系统，通过感测外界黑色指导线的变化来控制电机的实时变化。考虑了运动过程中会遇到的各种情况，通过训练BP神经网络使微控制器能够根据不同的环境做出快速、正确的反映。采用微控制技术对电机进行控制，使自动和无线遥控兼容。实验结果表明：移动机器人能根据室内黑色指导线的变化情况快速做出反映，有效抑制了移动机器人在运动过程中的出轨和静止现象，证明了提出的基于B P神经网络的循迹控制系统可靠性较高。%An intelligent tracking control system based on micro-control unit (MCU)was developed to real-time control the mo-tors by sensing the change of the black guide lines. After training the BP neural network,the MCU was able to make quick and accu-rate decisions for various situations encountered during the robot moving. Using MCU technology to control the motors,the system was compatible for both manual and automatic control. The experiment results show that the mobile robot can follow the change of black guide lines accurately and quickly,and stillness and out-of-orbit phenomena are effectively inhibited during moving. The proposed tracking control system based on BP neural network has been verified to be high reliability.
Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
龚声蓉; 王朝晖
2004-01-01
In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.
Application of BP neural network in DNBR prediction%BP神经网络在DNBR计算中的应用
Institute of Scientific and Technical Information of China (English)
黄禹; 刘俊强; 刘乐
2015-01-01
在压水堆事故分析中，通常采用系统分析程序、热流密度计算程序和子通道分析程序分步计算堆芯偏离泡核沸腾比(Departure from Nucleate Boiling Ratio, DNBR)。利用该方法计算的堆芯DNBR结果准确性较好，但是计算过程繁琐、费时。对于系统分析程序自带的堆芯DNBR简化计算模型，由于其根据堆芯限制线偏微分近似得到，适用范围较窄，准确性也难以保证。利用神经网络中的误差反向传播(Back Propagation, BP)算法，基于堆芯核功率、入口温度、流量和压力4个变量对应的一系列DNBR值，选取部分数据进行训练并建立模型，以达到快速和准确地预测堆芯DNBR的目的。根据误差分析，建立的计算模型具有较好的准确性，而且在部分失流事故和汽机停机事故下可较好地预测堆芯DNBR。%Background: In safety analysis of pressurized water reactor (PWR), departure from nucleate boiling ratio (DNBR) is usually calculated by three codes: a system transient analysis code, a heat flux calculation code and a subchannel analysis code, or by simplified model through a partial derivative approximation of the core DNB limit lines, but either procedure has problems of cumbersome or low accuracy.Purpose: The aim of this study is to gain a simple DNBR calculation method with high accuracy.Methods: A 3-layers back propagation (BP) neural network was proposed with a training data set to quickly predict DNBR using four variables of reactor coolant system (nuclear power, core inlet temperature, mass flow rate and pressure).Results: The error of the developed BP network is very small, and has similar results compared with the subchannel code calculations in two typical events.Conclusion: The trained BP network is accurate enough to be used in predicting DNBR, even in transient conditions.
Institute of Scientific and Technical Information of China (English)
张本国; 李强; 王葛; 张水仙
2012-01-01
LM algorithm was introduced to the training process of a BP neural network and a LM--BP neural network model was established aiming at the defects of slow convergence in the train- ing process of the traditional BP neural network. The LM--BP neural network model was applied to the breakout prediction in the continuous casting processes, and it was tested with the historical data collected from a steel mill. The feasibility and the validity of the model are verified by the results with the accuracy rate of 96.15% and the prediction rate of 100%%针对传统BP神经网络在训练过程中存在收敛速度慢的缺陷，将LM（levenberg marquardt）算法引入到BP神经网络的训练过程，建立了LM—BP神经网络模型，并将其应用于连铸过程中的漏钢预报系统。结合某钢厂连铸现场历史数据对系统进行了测试，测试结果以96．15％的预报率及100％的报出率，验证了基于LM算法的BP神经网络连铸漏钢预报方案的可行性和有效性。
Accident Database Earlv Warning Based on BP Neural Network%基于BP神经网络的车祸库预警技术
Institute of Scientific and Technical Information of China (English)
冯继妙; 胡立芳
2011-01-01
In order to achieve the purpose of accident early warning, this paper presents a new method: establish the vehicle accident databases, and combine it with BP neural network technology. First, construct a suitable BP neural network. Second, use the accident feature information to train the BP neural network, then the trained BP neural network can determine the possibility of this specific car accident At last, send the vehicle information into the trained BP neural network, and it can predict the possibility of this specific car accident In this paper, the author simulates this method by Matlab7.0.1. Simulation results show that the method is feasible and effective.%针对如何有效预测车祸发生的可能性,从而达到车祸预警的目的,提出了一种新的车祸预警方法:通过建立车辆的车祸库,并结合BP神经网络技术达到车祸库预警目的.先构建合适的BP神经网络,再用车祸特征信息训练BP神经网络,训练好的BP神经网络就具有判断发生该类型车祸可能性的能力,最后把车辆行驶信息输入到已训练好的BP神经网络,就可以预测发生该类型车祸的可能性.用Matlab7.0.1进行了该方法的仿真实验,仿真结果表明该方法具有一定的可行性和有效性.
Airport noise prediction model based on BP neural network%一种 BP 神经网络机场噪声预测模型
Institute of Scientific and Technical Information of China (English)
杜继涛; 张育平; 徐涛
2013-01-01
机场噪声预测对机场噪声控制、航班计划制定和机场规划设计具有十分重要的作用.现有的机场噪声预测模型都是以飞机的噪声距离曲线(NPD 曲线)为核心，用相应的数学模型将其修正至与具体机场的特定环境条件相关的噪声传播模型，存在预测成本高和误差大的缺点.针对这种情况，提出一种使用 BP 神经网络利用机场噪声历史监测数据进行NPD 曲线修正计算方法，从而建立适用于特定机场环境条件的机场噪声预测模型.实验表明，在特定机场的特定环境条件下，允许误差为0.5 dB 时，该模型预测准确率高达91.5%以上，具有预测成本小、准确度高的特点.%Airport noise prediction plays an important role in airport noise controlling, flight planning and airport designing. The airport noise prediction models are usually built based on aircraft noise distance curve(NPD), and the NPD curves are little by little revised to the noise propagation model under the specific airport environmental conditions by using a variety of mathematical models. In this way, there are shortcomings of the high cost and great prediction error. This paper presents an airport noise pre-diction model for particular airport environmental conditions. The proposed model applies BP neural network and history data of the airport noise monitoring to modifying the NPD curves. Experiment results show that in particular specific airport environ-mental conditions, the accuracy rate of noise prediction is more than 91.5% in the case of ±0.5 dB error. The proposed model has the features of lower cost and high accuracy.
基于算法的BP神经网络的应用研究%Research on Application of Algorithm-based BP Neural Network
Institute of Scientific and Technical Information of China (English)
栾玖阳
2012-01-01
BP神经网络的智能化特征与能力使其应用领域日益扩大,潜力日趋明显,许多传统信息处理方法无法解决的问题采用BP神经网络后取得了良好的效果,神经网络已被广泛应用于自动化、工程、经济及医学等各个领域.本文重点研究了BP神经网络的原理、算法及其应用.%Application fields of BP neural network with its intelligent features and ability are expanding, and its potential is increasingly evident. Problems can not be resolved using many traditional information processing methods, but using BP neural network can achieved good results, neural network has been widely used in fields, such as automation, engineering, economics, and medicine. This paper focuses on the principle, algorithm of BP neural network and its application.
Institute of Scientific and Technical Information of China (English)
孙晨; 李阳; 李晓戈; 于娇艳
2016-01-01
针对当前智能算法对股票市场预测精度不高的问题，提出使用布谷鸟算法优化神经网络（CS-BP）的方法，对股票市场进行预测。并与粒子群算法优化神经网络模型（PSO-BP）和遗传算法优化神经网络模型（GA-BP）的测试结果进行比较。通过对SZ300091（金通灵）日线的收盘价数据回测分析看出，布谷鸟算法优化神经网络模型明显优于这两种算法，能有效对股票市场进行预测，对于30天的预测精度约为98．633％。%This paper puts forward the method of predicting the stock market by using the cuckoo search algorithm to optimise BP-neural network(CS-BP)aimed at the problem of current intelligent algorithms in poor prediction accuracy on the market.Besides,it compares its test result with the results of PSO-BP model (optimising BP-neural network with particle swarm optimisation)and GA-BP model (optimising BP-neural network with genetic algorithm).After analysing the data backtesting result of the closing price of daily candlesticks of SZ300091 (JTL),we can conclude that the CS-BP model is obviously superior to these two algorithms,it can effectively predict the stock market with about 98.633% of accuracy for thirty days prediction.
Institute of Scientific and Technical Information of China (English)
何勇; 李妍琰
2014-01-01
该文提出改进的PSO‐BP算法在洪水预测应用中建立预测模型。以BP神经网络为基础，提取观测站往年平均径流量作为洪水属性。采用改进的PSO‐BP算法对神经网络的各个参数进行优化，最后建立模型应用于流域观测站的洪水预报模型，叙述了PSO粒子群算法和BP神经网络算法，详细阐述粒子群算法优化BP神经网络的权值和阈值，得出最优的BP神经网络预测适应度值。通过实验仿真对比，结果表明此方法预测结果比BP神经网络算法和混沌径向基神经网络模型算法精度更高，提高了预测的效率。%The flood prediction model base on PSO‐BP algorithm has been proposed in this paper .Extrac‐tion of observation station in average runoff as flood has been conducted on the bases of BP neural net‐work .Using the improved PSO‐BP algorithm parameters of the neural network has been optimized ,the flood forecasting model for watershed observing station with the model .This paper introduces the particle swarm optimization algorithm and BP neural network algorithm ,a detailed explanation of PSO algorithm to optimize BP neural network weights and threshold .Through the simulation results ,this method fore‐casting result is higher than the BP neural network algorithm and chaos RBF neural network model accura‐cy ,is an effective method of prediction and reliable flood .
Directory of Open Access Journals (Sweden)
Hankun Ye
2014-05-01
Full Text Available Evaluating supply chain performance of fresh agricultural products is one of the key techniques and a research hotspot in supply chain management and in fields related. The paper designs a new evaluation indicator system and presents a new model for evaluating supply chain performance of fresh agriculture product companies. First, based on analyzing the specific characteristics of the supply chain performance evaluation of fresh agriculture products, the paper designs a new evaluation indicator system including external and internal performance. Second, some improvements, such as adjusting dynamic strategy and the value of momentum factor, are taken to speed up calculation convergence and simplify the structure and to improve evaluating accuracy of the original BP evaluation model. Finally the model is realized with the data from certain supply chains of three fresh agriculture product companies and the experimental results show that the algorithm can improve calculation efficiency and evaluation accuracy when used for supply chain performance evaluation of fresh agriculture product companies practically.
基于BP网络的PID控制器参数整定法研究%PID Parameters Tuning Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
高峰
2012-01-01
将神经网络和PID参数的整定相结合，提出了基于误差反传神经网络的PID参数整定方法，通过神经网络的自学习和权值调整寻找最优的PID参数．该方法适用于非线性系统和时变系统，实现了PID参数的在线整定．%Neural network and PID parameters tuning are integrated, and tuning method based on BP neural network, which searches for optimum PID parameters by the self-learning and weights adjusting of neural network is presented, which is applicable to nonlinear and time-varying system, and realizes the on-line PID parameters adjusting.
Evaluation of Value Chain Risks Based on BP Artificial Neural Network%基于BP人工神经网络的价值链风险的评价
Institute of Scientific and Technical Information of China (English)
郭秋霞; 邓样明; 欧阳江
2011-01-01
通过对价值链管理深人研究,利用BP人工神经网络对价值链风险管理进行评价,采用专家评分法,获得实际的数据,对模型进行仿真和测试,证实价值链风险管理的指标体系的实用性和BP人工神经网络模型的价值.%Through in-depth analysis of value chain management, the paper employs BP artificial neural network to evaluate the risk management of value chains. Expert scoring is used on practical data collected and simulation and testing are conducted to verify the practicality of the index system of value chain risk management and the value of BP artificial neural network.
Nonlinear prediction of gold prices based on BP neural network%基于 BP神经网络的黄金价格非线性预测
Institute of Scientific and Technical Information of China (English)
张延利
2013-01-01
针对黄金价格的非线性特征和神经网络的自身特点，利用BP神经网络建立了黄金价格的非线性预测模型。实证研究结果表明，BP神经网络模型具有较好的预测精度，可以为黄金投资和宏观经济决策提供一定的参考依据。%According to the neural network nonlinear characteristics of gold price and its own characteristics ,using BP neural network nonlinear prediction model was set up for the price of gold .The results show that the BP prediction has good accuracy and is available to provide references for the gold investment and macroeconomic decisions .
Application of LM-BP neural network in predicting dam deformation.%LM-BP神经网络在大坝变形预测中的应用
Institute of Scientific and Technical Information of China (English)
缪新颖; 褚金奎; 杜小文
2011-01-01
为了对大坝进行切实有效的监控,需要建立一个良好的大坝预测模型.针对传统BP (Back-Propagation)神经网络存在的收敛速度慢和泛化能力弱等缺陷,利用LM-BP(Levenberg Marquardt Back Propagation)算法对大坝变形进行预测,并根据丹江口大坝1996和1997两年的变形观测数据,对大坝挠度预测结果进行分析.结果表明,所建立的LM-BP神经网络的预测精度和收数速度明显提高.%It is significant to establish an effective and practical dam safety monitoring model. The shortcomings of the traditional BP neural network lie in the slowness in the convergence rate and the weakness in the generalization ability. Based on the above, LM-BP neural network is adopted for predicting the dam deformation. With the measured data of Danjiangkou dam deformations in the year of 1996 and 1997 as examples,the deflection of dam is predicted using LM-BP. The results show that the proposed method can obviously enhance the forecasting precision and convergence rate.
基于 BP 神经网络的调剖效果预测模型分析%Prediction model of conformance control effect based on BP neural network
Institute of Scientific and Technical Information of China (English)
刘宁; 刘士梦; 李明
2014-01-01
产油量预测是调剖方案实施以后效果预测或评价的关键，基于BP神经网络理论，通过分析影响调剖效果的因素，利用Matlab神经网络工具箱函数，建立了调剖神经网络预测模型，经过模型预测效果分析及实际运用，认为利用BP神经网络预测产油量与实际值较为吻合，误差相对较小，可靠性高，可运用此模型预测调剖产油量。%The oil production prediction is a key to prediction or evaluation of conformance control effect after the scheme implementation .Based on the theory of BP neural network ,by analyzing the influencing factors of conformance con-trol effect ,the neural network prediction model for conformance control was established by employing the toolbox functions in the Matlab neural network .Through the analysis of the model forecast effect and practical application ,it was considered that the oil production predicted by the BP neural network was consistent with the actual one ,which had smaller relative error and high reliable .The model can be used to predict the conformance control production .
Li, Chunhui; Yu, Chuanhua
2013-08-15
To provide a reference for evaluating public non-profit hospitals in the new environment of medical reform, we established a performance evaluation system for public non-profit hospitals. The new "input-output" performance model for public non-profit hospitals is based on four primary indexes (input, process, output and effect) that include 11 sub-indexes and 41 items. The indicator weights were determined using the analytic hierarchy process (AHP) and entropy weight method. The BP neural network was applied to evaluate the performance of 14 level-3 public non-profit hospitals located in Hubei Province. The most stable BP neural network was produced by comparing different numbers of neurons in the hidden layer and using the "Leave-one-out" Cross Validation method. The performance evaluation system we established for public non-profit hospitals could reflect the basic goal of the new medical health system reform in China. Compared with PLSR, the result indicated that the BP neural network could be used effectively for evaluating the performance public non-profit hospitals.
一种低温锆弱凝胶调剖剂的研制%Modeling of rock drillability with BP neural network optimized by SDCQGA
Institute of Scientific and Technical Information of China (English)
李谦定; 李彦闯; 李彦庆
2013-01-01
In the control process of intelligent drilling,there are some difficulties in the rock drillability modeling,such as poor real-time, low accuracy of rock drillability extraction,etc. For this reason,a modeling method for the rock drillability extraction is put for-ward, which is based on the BP neural network optimized by SDCQGA ( Self-Adaptive Double Chain Quantum Genetic Algorithm). A fast self-adaptive double chain quantum genetic algorithm is established according to the variation rate of objective function at search point, and then the structure of BP neural network is optimized using this algorithm in order to overcome the shortcomings of easily being influenced by initial weights and poor generalization ability of BP neural network. The model for rock driilability extraction was established according to statistical analysis and preprocessing and analysis shortage of over-fitting, random and of back-propagation neural network which can affect the generalization ability with the subtle changes of the parameters of the network, this paper presents rock driilability extraction modeling methods using an optimization the BP neural network structure which is based on the adaptive double chain quantum genetic algorithm. Finally,the rock driilability extraction model is constructed by using a large number of measurement while drilling data in different drilling areas. The model can be effectively solved difficult extraction of rock driilability in the complex formation. The tests of extraction rock driilability of different lithology prove that this modeling approach not only improves the accuracy of parameter extraction and generalization ability,but also has a good real-time and suitability in the actual rock driilability extraction.%针对温度低于50 ℃的高含水油藏,研制出了一种聚合物/有机锆弱凝胶调剖剂.该调剖剂以部分水解聚丙烯酰胺(HPAM)为主剂,以自制有机锆YJ-1为交联剂,在35～50℃温度下能形成稳定的
Method of Deep Web entities identification based on BP neural network%基于BP神经网络的Deep Web实体识别方法
Institute of Scientific and Technical Information of China (English)
徐红艳; 党晓婉; 冯勇; 李军平
2013-01-01
针对现有实体识别方法自动化水平不高、适应性差等不足,提出一种基于反向传播(BP)神经网络的Deep Web实体识别方法.该方法将实体分块后利用反向传播神经网络的自主学习特性,将语义块相似度值作为反向传播神经网络的输入,通过训练得到正确的实体识别模型,从而实现对异构数据源的自动化实体识别.实验结果表明,所提方法的应用不仅能够减少实体识别中的人工干预,而且能够提高实体识别的效率和准确率.%To solve the problems such as low level automation and poor adaptability of current entity recognition methods, a Deep Web entity recognition method based on Back Propagation ( BP) neural network was proposed in this paper. The method divided the entities into blocks first, then used the similarity of semantic blocks as the input of BP neural network, lastly obtained a correct entity recognition model by training which was based on the autonomic learning ability of BP neural network. It can achieve entity recognition automation in heterogeneous data sources. The experimental results show that the application of the method can not only reduce manual interventions, but also improve the efficiency and the accuracy rate of entity recognition.
Discussionm on Implementing the Basic Model of BP Neural Networks with C Language%浅析BP神经网络基本模型的C语言实现
Institute of Scientific and Technical Information of China (English)
赵朝凤; 令晓明
2013-01-01
BP 神经网络已经成为应用最为广泛的神经网络模型之一。而人工神经网络是对人脑真正神经工作的简化生物模型。为了加深对神经网络的理解，利用推导公式来详细分析其最后的输出值和误差。这里旨在阐述用C语言实现BP神经网络基本模型，在BP神经网络的初始化函数中采用了归一化处理的方法，另外就是对神经元的权重初始化；而 BP 神经网络训练函数是整个 BP 神经网络形成的引擎，驱动着样本训练过程的执行。%Nowadays BP Neural Network becomes one of the most widely used neural network models, while the artificial neural network is the simplified biological model of human-brain’s real work. For deepening the understanding of neural network, the last output value and error of the neural network is analyzed with the deduction formula. This paper describes basic model of BP neural networks implemented with C language. The normalized method is adopted in the initial function of BP neural network, and the training function of BP neural network is the forming engine of the whole network, and thus it could drive the successful execution of sample training process.
Optimized BP Neural Networks for EMG Finger Movement Recognition%改进BP神经网络的EMG手指运动识别
Institute of Scientific and Technical Information of China (English)
方一新
2014-01-01
In the pattern recognition of Finger movement based on electromyography (EMG), the Stability and Recognition rate are both the problem. The paper proposes a new method of pattern recognition of EMG signal. The method combination of the algorithm using BP neural network AR model and the improvement of modern signal pro-cessing in the theory of the algorithm, can effectively solve the problem of BP network into local extremum recogni-tion. To make the classification of the eigenvalues of the EMG, these eigenvalues have been inputted to the MAT-LAB to build up a improved multilayer BP neural networks. For the recognition of three different kinds of finger mo-tion's EMG signals, the experiments show that the improved BP algorithm, to obtain higher recognition accuracy than the traditional BP algorithm, to around 94%.%在基于肌电信号（EMG）手指运动的模式识别中，稳定性和识别率是两个主要问题，为此提出了一种新的EMG模式识别算法。该算法采用现代信号处理理论中的AR模型和改进的BP神经网络相结合的算法，有效的解决了BP网络识别中落入局部极值问题。进行试验，将提取到的特征值输入MATLAB建立一个改进多层BP神经网络，识别三个不同类型的手指运动。实验表明，改进BP算法较传统BP算法获得了更高的识别精度，达到94%左右。
Handwritten digit recognition based on AP and BP neural network algorithm%基于 AP 和 BP 神经网络算法的手写数字识别
Institute of Scientific and Technical Information of China (English)
朱婷婷; 魏海坤; 张侃健
2014-01-01
Given the problem that current methods of handwritten digit recognition are not ideal for large-scale application,a new method of handwritten digit recognition has been proposed,combining affinity propagation with error back-propagation neural network algorithm.Firstly,pretreatment of samples was carried out.Then the AP algorithm was used to cluster samples to e-liminate redundant and re-construct the sample space.Finally,the BP neural network was utilized to learn and recognize each class from AP clustering.Experiments were conducted with the data from UCI machine learning database,and the correct identi-fication rate of the method reaches 96.10%,which is better than that of BP neural network algorithm (94.88%),and the pro-cessing time of the method is only one over ten of BP neural network algorithm.Thus,the proposed method can be used to effi-ciently and effectively identify handwritten digits with high practical value.%针对现有的手写数字识别技术不适合大规模应用的问题，提出了一种基于 AP 和 BP 神经网络的快速手写数字识别算法。首先对预处理后的样本通过 AP 算法（affinity propagation）聚类消除冗余，重新构造样本空间；然后构造 BP（误差反向传播）神经网络模型，学习测试集合样本。采用 UCI 机器学习数据库中的数据进行实验，结果表明，算法的识别正确率可达96．10％，高于 BP 神经网络算法的识别正确率94．88％，且执行时间约为后者的10％，具有较高的实用价值。
Institute of Scientific and Technical Information of China (English)
姚仲敏; 潘飞; 沈玉会; 吴金秋; 于晓红
2015-01-01
当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题.基于本地5 kW小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用 BP 以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型.实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度.其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强.%In the current PV output short-term forecast, BP or optimization BP neural network algorithm is used commonly, which has problems of single optimization algorithm, the lack of a variety of optimization algorithms for comparison and selection, and big forecast error. Therefore, based on local 5 kW small-scale distributed PV power station, considering the related factors that influence PV output such as solar radiation intensity, environmental temperature, wind speed and historical generation data of photovoltaic power station, this paper uses BP, GA-BP and POS-BP neural network algorithm respectively to construct short-term prediction model of PV output in sunny, cloudy and rainy weather conditions. Test results show that three kinds of neural network prediction models all reach certain prediction accuracy under three different weather conditions, among which GA-BP and POS-BP prediction models reduce the prediction errors compared to the traditional BP model, and POS algorithm has a better optimization effect on BP neural network prediction model and a stronger applicability compared to GA algorithm, and
基于BP神经网络混凝土抗压强度预测%PREDICTION OF CONCRETE COMPRESSIVE STRENGTH BASED ON BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
皮文山; 周红标; 胡金平
2011-01-01
Aimed at the main facts of concrete compressive strength, a multi-factor 3-layer BP network model was set up using BP artificial neural network for the prediction of concrete compressive strength, with cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age as the model input parameters, and concrete compressive strength as the model output parameter. The results show that the maximum predicted error of BP neural network model is less than 20 , the average error is 5.99 ,and the concrete compressive strength artificial neural network model has higher prediction accuracy.%在阐述BP人工神经网络原理的基础上,针对影响强度的主要因素,建立了多因子混凝土抗压强度3层BP网络模型,以每立方混凝土中水泥、高炉矿渣粉、粉煤灰、水、减水剂、粗集料和细集料含量及置放天数作为模型输入参数,混凝土抗压强度值作为模型的输出,对混凝土抗压强度进行了预测.实验结果表明:所建BP神经网络混凝土抗压强度预测模型最大误差绝对值都小于20%,平均误差为7.33%,模型具有较高预测精度.
整区拟合似大地水准面的 BP 神经网络方法%Study of whole fitting quasi-geoid using BP neural network method
Institute of Scientific and Technical Information of China (English)
宋雷; 胡伍生
2013-01-01
Due to the model error and smooth joint problem of fitting quasi-geoid using polynomial fitting method, a new method of whole fitting quasi-geoid using BP neural network was proposed in this study. Using the gravimetric quasi-geoid and GPS/leveling data in an area, the BP neural network method was compared with the whole and divisional polynomial surface fitting method. The results show that the new method could reduce the model error in the case of a bigger area and the anomalous difference between two kinds of quasi-geoids. The new method can obviously improve the inner and outer precision of fitting two kinds of quasi-geoids than the whole and divisional polynomial surface fitting method. The BP neural network is a feasible fitting method for finishing local high precise and high resolution quasi-geoid.% 针对组合法似大地水准面精化过程中,传统的分区曲面拟合法存在模型代表性误差和及分区间的平滑连接问题,提出整区拟合似大地水准面的 BP 神经网络方法。利用某区域的重力似大地水准面模型和 GPS/水准数据,将 BP 神经网络似大地水准面整区拟合法与整区曲面拟合法和分区曲面拟合法进行比较。研究结果表明：在较大区域和两类似大地水准面差别呈不规则的情况下,BP 神经网络方法有效地减小了拟合模型的代表性误差,整区 BP神经网络拟合法明显地较整区曲面拟合法和分区曲面拟合法提高了拟合结果的内、外符合精度。为区域高精度、高分辨率似大地水准面精化过程中似大地水准面整区拟合提供了有效的方法。
Directory of Open Access Journals (Sweden)
Yuyang Gao
2016-09-01
Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.
Circuit design of a LSI neural network using BP-GA algorithm%采用BP-GA算法的一种LSI神经网络的电路设计
Institute of Scientific and Technical Information of China (English)
卢纯; 石秉学
2001-01-01
A new algorithm is proposed to combine the Back-Propagation algorithm (BP) and the Genetic Algorithm (GA). The combined algorithm is used to design a Large Scale Integrated circuit (LSI) for a two-layer feedforward Artificial Neural Network (ANN). A novel neuron is proposed as the key element of the neural network. The neuron's activation function fit the sigmoid well and the bias weight and the gain factor of the neuron can be modulated. Further more, the saturation levels of the sigmoid remain constant for different gain values. HSPICE simulations were done using the neural network using transistor models for a standard 1.2μm CMOS process. Results using the exclusive or (XOR) benchmark demonstra te its effectiveness.%将误差反传（BP）算法和遗传算法（GA）有机地结合在一起，提出了一种新的算法BP-GA。采用BP-GA算法，设计了一个两层前向LSI神经网络。作为神经网络的关键部件，提出的新型神经元性能优越。它的激活函数与理想sigmoid函数拟合很好； 可实现对阈值及增益因子的编程并且不同增益因子下饱和输出电压值相同。采用标准1.2μm CMOS工艺的模型参数，对该两层前向神经网络电路进行的HSPICE模拟证明了它有解决异或(XOR)问 题的能力。
Directory of Open Access Journals (Sweden)
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.
Realization of Chinese text classification by using BP neural network%用BP神经网络实现中文文本分类
Institute of Scientific and Technical Information of China (English)
火善栋
2015-01-01
文本分类是文本挖掘的一个重要内容，在很多领域都有广泛的应用。为了实现中文文本分类问题，先采用分词技术和TF-IDF算法得到每一篇中文文档的特征向量，然后采用PB神经网络构造一个中文文本分类器。实验证明，采用BP神经网络进行中文文本分类时，虽然存在学习周期长，收敛速度慢等问题，但其分类速度和分类的正确率还是很高的。因此，采用BP神经网络进行中文分类是一个比较好的方法。%Text classification is an important part of text mining, and it has been widely used in many fields. In order to realize the Chinese text classification, the feature vector of each document is obtained by using the word segmentation technique and TF-IDF algorithm, and then a Chinese text classifier is constructed by BP neural network. Experiment results show that using BP neural network to Chinese text categorization, although there are problems such as a long learning period, slow convergence and so on, the classification speed and classification accuracy rate is quite high. Therefore, using BP neural network to classify Chinese is a good way.
ON SLOPE DISPLACEMENT PREDICTION MODEL BASED ON LMD-BP NEURAL NETWORK%边坡位移 LMD-BP神经网络模型研究
Institute of Scientific and Technical Information of China (English)
于伟; 蔡璟珞; 安凤平
2013-01-01
结合局部均值分解LMD（ Local mean decomposition ）算法和BP神经网络算法，提出一种全新的局部均值分解---BP神经网络位移时序预测模型。通过把实际监测的位移值作为训练样本，利用局部均值分解算法对其进行高度的自适应分解，得到多个生产函数PF（ Product function ）分量；而后通过BP神经网络模型对每一个PF分量进行预测，再把各个PF分量预测值进行重构累加，即可得到位移的预测值。通过BP神经网络对相关参数进行优化，达到了对于预测精度的改善。将该模型应用到永久船闸高边坡的三个监测点上进行位移时序预测中，结果表明，预测精度较高，具有一定的科学依据，在边坡体位移时序预测领域中具有极大的潜在价值。%We present a novel LMD-BP neural network displacement time series prediction model in combination with the algorithms of local mean decomposition ( LMD ) and BP neural network .By selecting actual monitoring displacement data as the training sample and conducting highly adaptive decomposition on it using LMD algorithm , several product function (PF) components are obtained.After that, every PF component is predicted through BP neural network model , and then each prediction value is reconstructed and accumulated , the prediction value of displacement can be derived .BP neural network is used to optimise the correlated parameters , thus the improvement in prediction accuracy is reached .The model is put into application in displacement time series prediction carried out on three monitoring points at the high slope of permanent lock , result shows that the prediction accuracy is high , scientifically valid and has great potential value in the field of slope body displacement time series prediction .
基于LADT-BP算法的心电图快速分析%A NEW ALGORITHM FOR ECG ANALYSIS BASED ON LADT-BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
李刚; 叶天宇; 何峰
2001-01-01
本文提出了一种应用LADT(Linear Approximation Distance Thresholding)压缩算法进行预处理的BP(Backpropagation)网络算法(我们称为LADT-BP算法)。实验证明该算法与现有的算法相比，在运算速度及正确识别率等方面，均有大幅度的提高。%A new algorithm for ECG analysis was proposed, with combination of LADT compression technique and BP neural network method. The Basic principles of the algorithm and its applications were also discussed. The experiment result showed that the new algorithm was faster in convergence and more accurate in recognition than that of the others.
Research and Application of Improving BP Neural Network%改进BP神经网络的研究及应用
Institute of Scientific and Technical Information of China (English)
周凌翱; 车金庆
2012-01-01
The artificial neural network has a strong nonlinear mapping ability, has been applied to various fields such as pattern recognition, intelligent control, image processing and time series etc., in this paper, the heuristic improvement of BP algorithm was proposed aimed at the deficiencies of BP algorithms, and a common type of improvement was introduced aimed at the main drawback of the genetic algorithm through analysis and research on genetic neural network model and its algorithm.%人工神经网络具有强大的非线性映射能力,已经被应用于模式识别、智能控制、图像处理以及时间序列分析等各种领域.本文针对BP算法的不足,提出了BP算法的启发式改进,通过对遗传神经网络模型及其算法进行分析和研究,针对遗传算法的主要缺陷介绍了一种常用的改进类型.
Application of BP neural network in weapon cardinal estimation%BP神经网络在武器基数评估预测上的应用
Institute of Scientific and Technical Information of China (English)
杨侃; 巩青歌; 王文俊
2014-01-01
BP neural network which is a applicative intelligent algorithm, was ofen used in approximation of function and analysis, estimate of data. This paper that mainly base on some of the illethal weapon's function and quota of CAPF ( Such as ammunition mass, minimum smoke stimulant concentration, smoke density in the air, smoke diffusing radiu, weapon cardinal) , apply BP neural network to estimate the minimum weapon cardinal during the time dealing with the accidents and cases, depending on different scale.%主要结合武警部队的非致命性武器(催泪弹)的一些性能指标(如：弹重、烟雾最小刺激浓度、刺激剂的空气密度、刺激扩散半径，武器基数)，针对不同事件规模，使用BP神经网络分析评估预测其在处突防恐中的武器使用基数的最小量。
Que, Ruiyi; Zhu, Rong
2012-01-01
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed. PMID:23112638
一种基于惩罚系数的 BP 神经网络预测能力%Predictive ability of BP neural network based on penalty coefficient
Institute of Scientific and Technical Information of China (English)
廖卫强; 迟岩; 王国玲
2013-01-01
为提高 BP 神经网络训练的预测能力,采用有助于提高 BP 神经网络逼近精度的 Metropolis 准则来克服BP 神经网络训练学习过程中容易陷入局部极小值的问题；考虑到两类误分的代价不同,利用两个惩罚系数 C1和 C2,对两类误分给予不同程度的惩罚；采用轮换法的策略来避免因样本不均衡分布带来的负面影响。研究结果表明：所构建的神经网络模型效果令人满意,是行之有效的做法。%In order to improve the predictive ability of BP neural network, this study uses Metropolis criterion to overcome the shortcoming of opting to fall into local minimum in BP algorithm. This study utilizes the penalty coefficients (C1, C2) in training to give sample misclassification with different penalties. This is because the cost of different misclassification is different. In terms of the cost of variant misclassification, the study uses a method of selecting training samples properly to avoid negative impact from imbalanced distributed samples. The study results show that the model is effective and rational.
Directory of Open Access Journals (Sweden)
Schwindling Jerome
2010-04-01
Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
Institute of Scientific and Technical Information of China (English)
毕凤荣; 刘以萍
2016-01-01
Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural net-work(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is pre-sented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square(RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level de-composition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance condi-tions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.
基于 BP神经网络的录音地点识别方法%An Approach to Identifying Rceording Locations Based on BP Neural Networks
Institute of Scientific and Technical Information of China (English)
王学强; 吉建梅; 包永强
2014-01-01
The existing digital audio forensics technology has difficulty in identifying the location,where the recordings are made,making it hard for judicial organs to assess the effectiveness of the audio evidence.To address such an issue,this paper devises a method for identifying such locations using a grid ENF based on BP neural network. In the identification process ,grid ENF is used as a training sample for purpose of training BP neural network.Next,the grid frequency data are extracted from audio files as input samples,which are then identified by using the trained BP neural network.Finally,to identify the location of the recording,optimal recognition results are obtained from the recognition results by adopting a simulated annealing algorithm.The experimental results show that recognition rate of this approach is at least 90.6 %,and the approach is reasonably reliable.%现有的数字音频取证技术很难做到录音地点的识别，因此司法机关就不易对音频证据的有效性做出判断．针对现状，本文设计了一种基于BP神经网络的录音地点识别方法．该方法是将电网频率（ ENF）作为识别根据．进行地点识别操作时，首先将电网ENF作为训练样本训练BP神经网络，然后从待取证的音频文件中提取电网频率数据并作为输入样本，用训练好的BP神经网络对输入样本进行识别，最后用模拟退火算法从识别结果中搜索出最佳识别结果，从而识别出录音的地点．实验结果表明，该方法的识别准确率最低达到90．6％，可靠性满足一定的要求．
Interpolation of Missing Precipitation Data Base on BP Neural Network%基于BP神经网络的缺测降水数据插补
Institute of Scientific and Technical Information of China (English)
田琳; 王龙; 余航; 杨蕊
2012-01-01
缺测降水数据的插补可以有效改善数据系列的完整性,以元江境内的元江、洼垤、因远、街子河、阿支、磨房河等水文和雨量站点逐月及年降水数据为基础,研究缺测降水数据的插补.站点之间月降水数据相关分析表明:各站点之间相关性较差,相关分析难以满足本研究流域内部分月降水数据插补精度,故尝试采用BP神经网络模型对研究流域降水数据进行插补.研究表明:基于本流域降水数据建立的神经网络模型检测样本合格率达到89.6％,具有较好的插补精度,说明神经网络可以用于本研究流域的缺测降水数据插补,为降水数据缺测的插补提供了新的途径.%The interpolation of missing precipitation data can improve the integrity of data series effectively. We did some research on interpolation of missing precipitation data base on hydrological and rainfall station's month and annual precipitation data in Yuanjiang, Wadi, Yinyuan, Jiezihe, Azhi and Mofanghe which are in Yuanjiang area. Correlation analysis among all stations showed that: correlation among all stations in the study area was weak; correlation analysis could hardly meet the interpolation precision in some months. So we tried to use the BP neural network model to interpolate the precipitation data in study area. The research showed that; the acceptable quality level of BP neural network sample test reached 89. 6% , which showed that the BP neural network could be used to interpolate the missing precipitation data in the area which we had studied and it provided a new way to interpolate the missing precipitation data.
Institute of Scientific and Technical Information of China (English)
Xu; Quan-xi
2001-01-01
Based on the basic principles of BP artificial neural network model an d the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serv ing as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute t he runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Co mparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.
Institute of Scientific and Technical Information of China (English)
叶斌; 刘知贵
2009-01-01
Based on the research of BP neural network and CELTS-22,this paper constitutes the guide line system refer to the main evaluation standard in CELTS-22.Applying the three layers BP neural network structure, it designs the computer assistant evaluation model that can simulate the expert, remedies the facticious misplay in the process of evaluating.%基于对人工神经网络和CELTS-22的研究,建立了以CELTS-22中主要评价规范为参照的指标体系.该系统应用三层BP神经网络结构,设计出能模拟专家进行评价的计算机辅助评价模型,可以弥补评价过程中的人为失误.
Institute of Scientific and Technical Information of China (English)
蒋莉; 黄华东; 王先义; 陈桦深
2016-01-01
The BP neural network model,with charging amount of blasting cut,blasting center distance and blasting velocity as main factors,is established based on Leyenberg-Marquardt (LM)calculation method;and the blasting vibration velocity is predicted and analyzed.The charging amount of blasting cut is calculated by means of critical blasting vibration velocity in related criteria.The calculation results show that LM-BP neural network method is superior to traditional method in terms of prediction of blasting vibration velocity;the blasting cut charging amount calculated by means of critical blasting vibration velocity inverse calculation method is rational and effective.%为了对隧道爆破振动灾害的危险状态进行有效地预测，实验采用基于 Levenberg-Marquardt（LM）算法改进的 BP 算法，建立以实测隧道爆破掏槽眼装药量、爆心距和爆破振速为主要爆破影响因素的神经网络模型，对振速进行预测分析，预测结果与实测数据吻合良好；继而引用 GB 6722—2014《爆破安全规程》所规定的临界安全振速反向预测掏槽装药量，通过反向预测计算得出满足安全振速要求的临界掏槽装药量。预测结果表明：LM-BP 算法相比传统的经验模型在振速预测上表现更好，通过反向的预测运算，能有效预知临界装药参数，对爆破振动安全预测及控制有积极的意义。
Institute of Scientific and Technical Information of China (English)
葛建坤; 李小平; 罗金耀
2016-01-01
通过田间试验,对温室膜下滴灌茄子冠层叶片蒸腾速率的变化规律进行了深入研究。通过分析温室内地面温度、相对湿度、植株冠层温度、气压、水面蒸发、太阳辐射等6个环境参数与茄子蒸腾速率的综合影响关系,确定了网络拓扑结构为6-9-1。并应用 MATLAB 软件,选择 Levenberg-Marquardt (L-M)优化算法,建立了基于 Back Propagation(BP)神经网络的温室膜下滴灌茄子蒸腾速率预测模型。经模型验证得出,BP 神经网络模型预测值与蒸腾速率实测值间拟合效果较好,平均相对误差为0.0298,达到预测精度要求。该研究成果对温室膜下滴灌作物需水规律及需水量研究具有较好的参考价值。%In order to reveal the law of crop transpiration in greenhouse,a field experiment on transpiration rate of greenhouse egg-plant with drip irrigation under mulch was taken in a Venlo type greenhouse in North China University of Water Resources and Elec-tric Power.Through the analysis on the combined influence between eggplant transpiration rate and 6 indoor environmental factors (greenhouse ground temperature,relative humidity,plant canopy temperature,air pressure,evaporation and solar radiation),topol-ogical structure of the model was discussed and determined (6-9-1).And a prediction model of greenhouse eggplant transpiration rate was established based on BP Neural network of L-M optimizing algorithm,by using MATLAB.After the model validation,the re-sults indicated that,the BP neural network prediction model has a high precision,the predicted value fits the measured value well, and average relative error is only 0.0298,which meets the precision requirement.The research result has a certain reference value to the study on crop water requirement in greenhouse with drip irrigation under mulch.
Institute of Scientific and Technical Information of China (English)
黄惠峰; 张献州; 张拯; 刘龙; 喻巧; 乐亚南
2015-01-01
The long monitoring period , plentiful contents and complex influencing factors of tunnel deformation monitoring have made the monitoring methods being improved continuously and will require a real‐time health evaluation to the tunnel .It states the construction of BP neural network and tunnel safety evaluation model ,studies the tunnel safety evaluation model base on BP neural netw ork in the category of deformation monitoring , proposes a modern internet of things mode of tunnel deformation monitoring system joints satellite positioning technology , measurement robot , sensor technology and network technology of wireless mobile communication together ,analyzes the deformation characteristics of multi‐source data ,combines with the experiences and knowledge of evaluation experts ,realizes tunnel safety evaluation under BP neural netw ork and deformation monitoring data ,and provides a novel and effective method to tunnel deformation monitoring and safety evaluation .%隧道变形监测周期长、内容多且影响因素复杂，因此需要对隧道监测方法进行不断的改进，对隧道的健康状态进行实时评估。研究变形监测范畴内基于BP神经网络的隧道安全状态评估模型，组建集卫星定位技术、测量机器人、传感器技术、移动网络通信等为一体的现代化物联网模式下的隧道变形监测系统，分析多源数据的变形特征，结合专家经验知识，实现基于BP神经网络与变形监测成果下的隧道安全状态评估，为隧道变形监测及安全状态评估提供一种新颖而有效的方法。
基于改进的BP神经网络方法的数据挖掘%Data Mining Based on Improved BP Neural Network Method
Institute of Scientific and Technical Information of China (English)
王磊; 王汝凉
2016-01-01
数据挖掘技术是从庞大数据中挖掘出隐藏信息的有效工具，它包括神经网络、数据库技术、信息检索、模式识别、图像与信号处理和空间数据分析等多门学科技术。该文主要对传统BP神经网络算法进行改进，并将改进后的算法应用于数据挖掘的技术上，且将仿真实验结果与传统算法的实验结果做了对比，结果显示改进后的算法可以提高数据的分类与识别。%Data mining technique is an effective tool for digging out the hidden information from large data ,including neural networks ,database technology ,information retrieval ,pattern recogni‐tion ,image and signal processing and spatial data analysis and so on .The paper mainly improves the traditional BP neural network algorithm ,applies the improved algorithm to data mining technology , through comparing the simulation experiment results with that of the traditional algorithm ,which show s that the improved algorithm can improve the classification and identification of data .
Institute of Scientific and Technical Information of China (English)
焦爱红; 袁力哲; 陈燕生
2011-01-01
Fault diagnosis algorithm based on adaptive genetic algorithm and BP neural network (AGA-BP) was presented to avoid the defect of tradition BP neural networks. The adaptive genetic algorithm was used to optimize initial weights and thresholds of the BP neural network in earlier stage of iterative calculation, and the error hack propagation algorithm with self study speed was used to improve the network problems of slow convergence speed in the later stage. The AGA-BP algorithm was used to diagnose grinding bum fault. The result was compared with that of the general network algorithm. It testifies the method is correct and valid.%针对传统BP神经网络的不足,提出基于自适应遗传算法的BP神经网络故障诊断算法.在迭代计算前期,采用自适应遗传算法对神经网络的权值和阈值进行全局优化;在迭代计算后期,利用改进的BP算法在近似最优解附近进行局部寻优.将该算法用于磨削烧伤的故障诊断之中,并将结果与基于改进BP网络的诊断结果进行比较,证明该方法的正确性和有效性.
Institute of Scientific and Technical Information of China (English)
王赟松; 刘钦龙; 高卫中
2004-01-01
标准BP神经网络算法收敛速度慢是限制其广泛应用的主要原因.为此,以标准BP算法为基础,应用最小二乘法理论,提出了一种收敛速度快的BP算法--NLMSBP算法.仿真结果表明,和标准BP算法及其它改进形式比较,NLMSBP算法收敛速度大大提高,稳定性并未降低,这为BP神经网络应用于实时性要求高的场合提供了算法基础.该算法缺点是计算量大,所需计算机内存大,不适于大型网络的计算.%That standard backpropagation(BP) algorithm for training neural networks converges slowly is the main reason why it cannot be used widely in practical applications. Therefore, a new kind of BP algorithm, called the NLMSBP algorithm for short, is put forward in this paper by using solutions for a nonlinear least mean square problem. The experimental results have proved that the algorithm converges very fast and has good stability compared with the standard BP algorithm and the other modifications. It is suitable for training the network with a few thousands of weights and offsets and high training precision demand. If the computer memory is enough, the superiority of the algorithm over the others is very notable. Indeed, it is worth popularizing.
Institute of Scientific and Technical Information of China (English)
刘琳岚; 樊佑磊; 舒坚; 臧超
2011-01-01
Link quality prediction can provide the basis for the upper layer protocol of wireless sensor networks (WSNs) choosing the path to transport data so as to improve the data transfer rate and save the energy. The prediction method of link quality of WSNs based on BP neural network is proposed which uses BP neural network to predict the sequence of PRR. It includes the setting of prediction windows and the design of BP neural network. The results show that the link quality prediction based on BP neural network has higher prediction accuracy compared with EWMA alone.%链路质量预测可以为无线传感器网络上层协议选择路径进行数据传输提供依据从而达到提高数据传输率、节省能量的目的.提出一种基于BP人工神经网络的WSNs链路质量预测方法,使用BP神经网络对PRR的序列进行预测,包括预测窗口的设置和BP神经网络的设计两个阶段.实验结果表明,基于BP神经网络的链路质量预测方法与单独使用EWMA方法相比具有预测精度高的优点.
Institute of Scientific and Technical Information of China (English)
王德明; 王莉; 张广明
2012-01-01
To improve the short-term wind speed forecasting accuracy for wind farm, a prediction model based on back propagation(BP) neural network combining genetic algorithm was proposed. Autocorrelation analysis was used to discover historical wind speeds which have significant influence on predicted wind speed. The input variables of BP neural network predictive model were historical wind speeds, temperature, humidity and air pressure. Genetic algorithm was used to optimize the weights and bias of BP neural network. Optimized BP neural network was applied to predict wind speed an hour before, two hours before and three hours before individually. The simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.%为了提高风电场短期风速预测精度,提出将遗传算法和反向传播(BP)神经网络相结合的预测模型.采用自相关性分析找出对预测值影响最大的几个历史时刻风速,以历史时刻的风速、强度、湿度和气压作为BP神经网络预测模型的输入变量；利用遗传算法的全局搜索能力获得BP神经网络优化的初始权值和阈值;采用优化后的BP神经网络分别建立1、2、3h的短期风速预测模型.实验结果表明,该方法较BP神经网络具有预测精度高、收敛速度快的优点.
Institute of Scientific and Technical Information of China (English)
王东; 徐超; 万强
2015-01-01
Modeling of mechanic joint is a challenge for the complex multi - scale,multi - physics and nonlinear physics behaviors on the interface,introducing additional flexibility and damping to the overall structural dynamics. The Iwan model is applied to model and simulate the joint beam system. The nonlin-earity characteristics are extracted by EMD method and applied to train the backpropagation neural net-works. Then,the nonlinear mechanic model is identified by the experimental nonlinearity of jointed beam,which is applied to simulate the joint interface invested by the result of experiment. The results show that:based on the BP neural networks,the nonlinear characteristics can be applied to establish the nonlinear mechanic model of joint interface and the simulation and experimental results have a good coher-ence.%连接界面上存在的多尺度、多物理场和非线性的物理机理是引起结构能量耗散和刚度非线性的主要原因。采用 Iwan 模型模拟连接结构进行连接梁的动力学仿真，利用 EMD（Empirical Mode Decomposition，EMD）提取时域信号的非线性特征训练 BP 神经网络，再设计连接梁实验辨识连接界面的非线性力学模型参数，将辨识建立模型运用在连接结构中进行数值仿真并与实验结果对比。结果表明：利用 EMD 非线性特征进行 BP 神经网络训练能够建立有效的连接界面非线性力学模型，仿真结果与实验结果具有较好的一致性。
Institute of Scientific and Technical Information of China (English)
魏俞涌; 许航飞; 张一龙
2012-01-01
After the analysis of the disadvantages of traditional wiper, this paper introduces a method of constructing the control model of automatic windshield wiper based on BP neural network. A model of pattern recognition based on BP neural network is built and train it with specialists' experience data.and then tested it. And we give the learning process and algorithm of BP neural network. The result indicates that this model based on BP neural network is effective to handle uncertainties and nonlinearities of the automatic windshield wiper system, without use of a sophisticated mathematical model.%在分析了传统雨刮器缺点的基础上,提出了一种基于BP神经网络的模式识别模型,用专家的经验数据训练它,并测试了它;给出了BP神经网络的学习过程及算法.结果表明这个基于BP神经网络的模型不使用精确的数学模型即可有效处理智能雨刮器系统的不可靠性和非线性.
Vonk, E.; Jain, L.C.; Veelenturf, L.P.J.
1995-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
Research of Image Compression Based on Quantum BP Network
Directory of Open Access Journals (Sweden)
Hao-yu Zhou
2013-07-01
Full Text Available Quantum Neural Network (QNN, which integrates the characteristics of Artificial Neural Network (ANN with quantum theory, is a new study field. It takes advantages of ANN and quantum computing and has a high theoretical value and potential applications. Based on quantum neuron model with a quantum input and output quantum and artificial neural network theory, at the same time, QBP algorithm is proposed on the basis of the complex BP algorithm, the network of a 3-layer quantum BP which implements image compression and image reconstruction is built. The simulation results show that QBP can obtain the reconstructed images with better quantity compared with BP in spite of the less learning iterations.
A BP Neural Network Based Method for Geological Missing Data Processing%基于 BP 神经网络的地质缺失数据处理方法
Institute of Scientific and Technical Information of China (English)
张玲玲; 李国清; 姜光成; 李威; 胡乃联
2015-01-01
In the process of geological exploration,due to the limitation of technical and equipment objective conditions,there are lots of basic geological data missing.It causes that the geological data is not complete and accurate as building the deposit model,and has a direct impact on the accuracy of the orebody shape and reserves estimation.In order to provide the complete and believable data,so that the deposit model will be more realistic. Firstly the generation mechanism of geological missing data is studied to find out the method which geological missing data obeys.By means of comparing and analyzing the features and applicable conditions of Expectation Maximization(EM) algorithm,Markov Chain Monte Carlo(MCMC) method and Back Propagation(BP) Neural Network,then an interpolation method of geological missing data which based on BP neural network is selected and introduced,and the relative model of processing geological missing data is built up.Finally the whole method is applied in a certain gold mine in Shandong.It has been proved that the model can achieve interpolation of most of the geological missing data,and the results are reliable.In short,it is feasible and effective using the model to solve the integrity problem of geological data caused by basic data missing.%在地质勘探过程中，由于技术、设备的客观条件限制，造成了部分基础地质数据的缺失，这使得矿床建模时地质数据不够完整准确，直接影响了矿体形态及储量估值的精度。为了向矿床模型的构建环节提供完整且可信的基础地质数据，首先研究了地质缺失数据的产生机制，并通过对比分析期望—极大化算法（EM 算法）、马尔可夫—蒙特卡洛方法（MCMC 方法）以及 BP 神经网络等数据插补方法的特点及适用条件，提出了基于 BP 神经网络的地质缺失数据处理方法，构建了地质缺失数据处理的 BP 神经网络模型，并在山东某金矿进行了实际应
Road Roughness Detection and Simulation based on BP Neural Network%基于BP神经网络的路面不平度检测与仿真
Institute of Scientific and Technical Information of China (English)
崔丹丹; 张才千; 韩东
2014-01-01
The road roughness increases the vibration of the vehicle which seriously affects the life of the road and ride comfort. In order to identify and analyse road surface power spectral density, a method based on BP neural net-work to detect road roughness was proposed. The four degrees of freedom vehicle vibration model was used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS/CAR as the input samples and the road surface power spectrum density as output samples, the nonlinear mapping was found by the application of BP neural network. Another simulation input data were used in the trained network as the road spectrum identification. The results show that this method has better abil-ity of anti-noise and ideal identification accuracy, and the road surface spectrum of identification fits the imitated road surface spectrum.%在对路面不平度优化检测问题的研究中，由于路面不平使车辆振动加剧，严重影响了路面的使用寿命和乘坐的舒适性。功率谱密度是评价路面不平度的常用指标，为了识别分析路面功率谱密度，提出了一种采用BP神经网络的路面不平度检测方法。以四自由度车辆振动模型为基础，把ADAMS/CAR中车辆平顺性仿真得到的汽车质心垂直加速度谱和俯仰角加速度谱为输入样本，以路面功率谱密度为输出样本，应用BP神经网络建立非线性映射。将仿真数据代入已训练好的网络中进行路面功率谱识别，仿真结果表明：上述方法识别出的功率谱密度与实际功率谱密度的平均误差仅为1.23%，具有较强的抗噪声能力和较理想的识别精度。
The Application of BP Networks to Land Suitability Evaluation
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods' limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.
Institute of Scientific and Technical Information of China (English)
师洪涛; 杨静玲; 丁茂生; 王金梅
2011-01-01
建立风电功率预测系统并提高其预测精度是大规模开发风电的关键技术之一。基于数值天气预报,建立了反向传播（BP）神经网络风电功率预测模型,并采用某风电场实际数据分析了影响该模型预测精度的因素。针对原始风速及功率序列日特性不明显、BP神经网络不能完全映射其特性的缺陷,提出了一种基于小波—BP神经网络的预测模型。该模型利用小波将风速与功率序列在不同尺度上进行分解,并使用多个BP神经网络对各频率分量进行预测,最后重构得到完整的预测结果。研究表明该模型可有效提高预测精度。%Establishing the wind power prediction system and improving the prediction accuracy is one of the key techniques for exploiting wind power.Based on numerical weather prediction,a wind power prediction model using the back propagation（BP） neural network is proposed.Factors that affect the prediction accuracy are analyzed using actual data of a certain wind farm.In the light of inconspicuous day characteristic of the original wind speed and the failure of the BP neural network to completely map its power sequence,a prediction model based on wavelet-BP neural network is proposed.With the wavelet-BP neural network model,the wind speed and power sequence are decomposed into different scales.Then the sub-sequences of different frequency components are predicted using multiple BP neural networks.Finally,the output data of BP neural networks are reconstructed to obtain the complete wind power predicting results.It is shown by the research results that the prediction accuracy of wavelet-BP neural network is effectively improved.
基于BP神经网络的手势识别系统%Gesture recognition system based on BP neural network
Institute of Scientific and Technical Information of China (English)
2013-01-01
A design of gesture recognition is provided, which use ADXL335 acceleration sensor to collect the triaxial acceleration information of five fingers and the back of the hand, pick up the gesture characteristic quantity through ZigBee wireless network transmission, and use BP neural network algorithm for error analysis. Finally, verification by MATLAB simulation shows that the system has high recognition rate in the test, and it is stable.% 给出了采用ADXL335加速度传感器来采集五个手指和手背的加速度三轴信息，并通过ZigBee无线网络传输来提取手势特征量，同时利用BP神经网络算法进行误差分析来实现手势识别的设计方法。最后，通过Matlab验证，结果表明，该系统在测试中识别率较高，系统稳定。
应用BP神经网络预测油页岩含油率%Application of BP neural network in oil content prediction
Institute of Scientific and Technical Information of China (English)
胡启华; 范晶晶; 张新
2014-01-01
Method of△logR and advanced method of△logR arre usually adopted to calculate oil content of oil shale with log data. These methods easily cause some errors in the process of calculating parameters, and these methods are based on linear relation between oil content and characteristic log values. However, it was absolutely a nonlinear relation between them in the actual heterogeneous stratum. Therefore, BP neural network based on LM ( Levenberg-Marquardt ) algorithm was adopted to calculate the oil content in Jurassic strata of northern Qaidam basin. Firstly, mathematical statistics distribution feature of log data were analyzed with Matlab; Ssecondly, oil content values were predicted with BP neural network based on LM algorithm after the excellent samples had been chosen; finally, a matrix composed of 40 link weights and 11 thresholds was the parameter interpretation model of oil content. Results of the BP neural network prove that theoretical calculating values match well with the core experimental measuring values, and the mean square error can be controlled within 0. 191 8. Therefore, this parameter interpretation model can be promoted in the area of the same geology background.%根据测井资料计算油页岩含油率多采用△logR法或改进的△logR法，这些方法中参数获取过程中易产生诸多误差，且这些方法是建立在油页岩含油率与特征测井曲线值是线性关系的基础上的，而在实际非均质性地层中，测井对油页岩含油率参数的响应在本质上必然是非线性的。基于此，运用BP神经网络来预测柴达木盆地北部地区侏罗纪油页岩含油率。首先分析研究区段测井数据的数理统计分布特征，在优选学习样本的基础上再采用一种基于LM（ Levenberg-Marquardt）算法的BP神经网络进行含油率预测，最后得出一组由40个连接权值与11个阈值组成的含油率参数解释模型，油页岩含油率预测值与岩心实验室
Application of BP neural network in evaluation of artistic voice%BP神经网络在评价歌唱艺术嗓音中的应用
Institute of Scientific and Technical Information of China (English)
李小武; 罗兰娥
2012-01-01
The singing voices were recorded from 30 young music students who come from Hunan University of Science and Engineering. Their acoustic parameters, such as Fl, F3, F0, vocal range,jitter, disturbance of Fl, disturbance of F3 and average energy were extracted by the way of voice analysis, BP Neural network analysis was used to evaluate the singing voices objectively. The results were then compared with those of the subjective evaluation performed by the experienced professionals. The error between the two evaluation approachs was within 3.4%, The results show that the neural network analysis can be used as an objective instrument to evaluate the singing quality of artistic voices. This is helpful to instruct, select and train professional singers.%录制湖南科技学院30名无喉病、无上呼吸道感染的声乐专业青年大学生专业训练歌声信号,利用语音分析技术提取歌声声学参数第一共振峰、第三共振峰、基频、音域、基频微扰、第一共振峰微扰、第三共振峰微扰、平均能量,使用BP神经网络方法客观评价歌声质量,并与资深声乐专业教师的主观评价进行比较,误差在3.4％之内.结果表明BP神经网络方法利用评价参数能正确客观评价歌声质量,有助于科学地指导选拔和训练艺术嗓音人才.
Institute of Scientific and Technical Information of China (English)
吴淦洲; 张玲; 王伟城
2013-01-01
为获得沙姜总黄酮提取的优化工艺，以乙醇体积分数、溶剂用量、提取温度和提取时间作为影响因素，进行了沙姜总黄酮提取的单因素试验和正交试验。以获得的25个单因素试验结果和9个正交试验结果作为训练样本，设计和训练了4-5-1结构的3层 BP 人工神经网络。再运用训练好的网络对设计的这些工艺组合进行预测，获得了2组总黄酮得率较高的提取工艺。结果显示，通过人工神经网络获得沙姜中总黄酮物质的最佳提取工艺为：以80%的乙醇溶液为溶剂，料液比为1：90，在75℃下提取10 h，在此条件下得到的总黄酮得率为0.950 mg/g，比正交试验最佳工艺条件下获得的总黄酮得率提高了2.4%。% Single factor tests and orthogonal experiments were taken to acquire optimum extraction craft of flavonoids from Kaempferia galangal, and the alcohol concentration, solvent amount, extraction temperature and extraction time were considered to be influencing factors. The results of 25 single tests and 9 orthogonal experiments were used as training samples to design and train a 4-5-1 three-layers BP neural network. Then these designed extraction conditions was predicted by the terminative neural network, and 2 combinations of extraction conditions which could bring about high yield of flavonoids were acquired. The study results showed that in the condition of 80% ethanol as the solvent, the solid-liquid ratio of 1:90, at temperature 75 ℃ and extracting 9 h. Under these conditions, the yield of flavonoids was 0.950 mg/g, obtaining about 2.4% more flavonoids than that of orthogonal experiments.
Institute of Scientific and Technical Information of China (English)
李德富; 翁克瑞; 杨娟; 诸克军; 李志; 曹洪
2012-01-01
目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别.为克服这一问题,结合模糊C均值算法和BP神经网络实现难采储量的分类.首先基于效益指标运用模糊C均值算法自动搜索储量的最佳类别,再利用BP神经网络建立储量效益指标类别与储量属性指标之间的关系表达式.在已知储量指标值的情况下,通过此关系式即可求得储量的类别.最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策.%Currently, the classification and evaluation criterion of reserves were determined through the scope of the, criteria value, which required all criteria values were just right in the existing range of criteria. Otherwise it would be difficult to divide the reserves category. To overcome this problem, this paper combined with Fuzzy C-Means clustering algorithm (FCM) and BP neural network method to classify difficult recoverable reserves. First use FCM to automatically search for the optimal category of reserves, based on performance indicators. And then establish the relational expression between the reserves category and reserves properties by BP neural network. So in the case of the criteria value known, the categories of reserves can be obtained through this relational expression. Finally take the case of an oil field in the 10th Oil Production Plant of PetroChhm Daqing Oilfield LLC, and evaluate the recoverable reserves, which conducts the rolling development of recoverable reserves.
Identification of Mining Road Roughness Based on GA-BP Neural Network%基于 GA-BP 网络的矿山路面不平度辨识
Institute of Scientific and Technical Information of China (English)
谷正气; 朱一帆; 张沙; 马骁骙
2014-01-01
BP neural network optimized by GA was used to identify the mining road.A fourteen degree-of-freedom vehicle vibration model was set up.The vehicle seat acceleration obtained by simu-lation was regarded as an ideal input sample of neural network,and the fitting road roughness was re-garded as an ideal output sample of neural network based on inverse transformation principles,then the nonlinear mapping model between them was built by network training.Road roughness was iden-tified under the conditions of different grade roads through fitting,various pit roads and different loads of dump truck.Identification ability was verified for complex mining roads due to high correla-tion coefficient and small relative error in this method.The accuracy of the method was verified through vehicle road test.Compared with simulation results of ride comfort under common C-class roads,it is shown that identification road is more closer to actual one,so as to achieve the purpose of improving the simulation accuracy of the models.%提出利用经遗传算法优化的 BP 神经网络辨识矿山路面的方法。建立了14自由度自卸车仿真模型，将仿真得到的座椅加速度作为网络理想输入样本，基于逆变换原理拟合出的路面不平度作为网络理想输出样本，通过网络训练，建立了两者之间非线性映射模型。对拟合出的不同等级路面、各种凹坑路面及自卸车不同载重下路面不平度进行辨识，辨识路面与测试路面相关系数高、相对误差小，验证了该方法具有对复杂矿山路面的辨识能力。通过整车道路试验，证明了该方法的准确性。与自卸车常用 C 级路面下的平顺性仿真结果的对比显示，采用该方法得到辨识路面更加接近实际路面，达到了提高模型仿真精度的目的。
Application of ABC-based BP Neural Network in Integrated Navigation System%ABC优化BP神经网络算法在组合导航中的应用研究
Institute of Scientific and Technical Information of China (English)
孙佳兴; 张晓林; 侯冰
2016-01-01
The Back Propagation neural network based on Artificial Bee Colony algorithm is presented in this paper for integrated navigation. Firstly, on the condition that the BDS receiver receives signal normally, the SINS information such as the velocity and position is recognized as the input of ABC-based BP neural network and the output of this network is the output information of Kalman filter, so that the ABC-based BP neural network is trained and the corresponding math model is established. Then, in the case that the BDS receiver receives abnormal signal, the SINS information is recognized as the input of ABC-based BP neural network, and the established model is used to predict the output adjustment information, which makes the SINS adjusted. Finally, simulation results indicate that compared with the traditional BP neural network, the ABC-based BP neural network has better performance on positioning accuracy.%针对北斗/捷联惯导组合导航,提出一种基于人工蜂群ABC(Artificial Bee Colony)算法的反向传播BP(Back Propagation)神经网络算法.首先,在北斗卫星导航系统接收机正常接收信号时,将捷联惯性导航解算信息(速度、位置)作为网络输入,卡尔曼滤波输出信息(速度、位置校正量)作为网络输出,对ABCBP神经网络进行在线训练,建立ABCBP神经网络的映射数学模型.然后,在北斗卫星导航系统接收机信号失效情况下,将惯性导航解算信息作为网络输入,利用建立好的ABCBP神经网络预测输出校正量信息,以此来校正捷联惯导系统SINS(Strapdown Inertial Navigation System).最后,通过飞机飞行半物理仿真实验验证该算法的性能.仿真结果表明,ABCBP神经网络算法在定位精度方面具有更加优越的性能.
A keyword extraction method based on BP neural network%一种基于BP神经网络的关键词抽取方法
Institute of Scientific and Technical Information of China (English)
白晓雷; 黄广君; 段建辉
2014-01-01
为了进一步提高Web信息抽取的准确性和效率，通过分析传统中文关键词抽取方法，文章提出了一种基于BP神经网络的中文关键词抽取方法。该方法在分析和提取术语特征的基础上，给出了确定网络隐层节点数的表达式和多个术语特征表达式，以此确定网络参数，实现中文关键词的抽取。实验结果表明，该方法查全率、查准率和查找性能较高，具备较好的应用前景。%In order to further enhance the accuracy and efficiency of Web information extraction ,a Chi-nese keyword extraction method based on back propagation(BP) neural network is proposed by analy-zing of the traditional Chinese keyword extraction method .The network hidden layer nodes expres-sions and terminology characteristic expression are given on the basis of analyzing and extracting term characteristic so as to determine the network parameters and achieve the Chinese keyword extraction . The experimental results show that the proposed method possesses high recall ratio ,precision ratio and look-up performance ,and it has a good application prospect .
BP Network Based Users' Interest Model in Mining WWW Cache
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP(back propagation) neural network. By this method, the users' interest in the WWW cache can be described and the neural network of users' interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users' interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.
Directory of Open Access Journals (Sweden)
Caihong Li
2013-01-01
Full Text Available The tank capacity chart calibration problem of two oil tanks with deflection was studied, one of which is an elliptical cylinder storage tank with two truncated ends and another is a cylinder storage tank with two spherical crowns. Firstly, the function relation between oil reserve and oil height based on the integral method was precisely deduced, when the storage tank has longitudinal inclination but has no deflection. Secondly, the nonlinear optimization model which has both longitudinal inclination parameter α and lateral deflection parameter β was constructed, using cut-complement method and approximate treatment method. Then the deflection tank capacity chart calibration with a 10 cm oil level height interval was worked out. Lastly, the tank capacity chart was corrected by BP neural network algorithm and got proportional error of theoretical and experimental measurements ranges from 0% to 0.00015%. Experimental results demonstrated that the proposed method has better performance in terms of tank capacity chart calibration accuracy compared with other existing approaches and has a strongly practical significance.
Institute of Scientific and Technical Information of China (English)
阎兴頔; 杨文; 马贺贺; 侍洪波
2012-01-01
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammo- nia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the pro- duction efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
HAND GESTURE RECOGNITION BASED ON BP NEURAL NETWORK IN COMPLEX BACKGROUND%复杂背景下BP神经网络的手势识别方法
Institute of Scientific and Technical Information of China (English)
王先军; 白国振; 杨勇明
2013-01-01
In light of the characteristics of skin colour in gesture images, the combination of threshold segmentation of skin colour in RGB space and cluster characteristics in YCbCr colour space as well as the application of background model effectively reduce the interference of similar skin colours in background and achieve the detection and segmentation of hand image in complex background. Seven constant Hu moment descriptors of image are used to characterise different binary hand gesture contours. At last, the BP neural network is applied to hand gesture recognition. Experimental results demonstrate that this method has higher recognition rate and better robustness.%针对手势图像的肤色特点,结合肤色在RGB空间的阈值分割和YCbCr颜色空间上的聚簇特性,以及背景模型的应用,有效减少了背景中类肤色的干扰,完成了手部图像在复杂背景下的检测和分割；并采用图像的7个不变Hu矩描述子来表征不同二值化的手势轮廓；最后采用BP神经网络进行手势识别.实验结果表明该方法有较好的识别率和鲁棒性.
基于BP神经网络的语音情感识别研究%Speech Emotion Recognition Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
徐照松; 元建
2014-01-01
随着科技的迅速发展，人机交互越来越受到人们的重视，语音情感识别更是学术界研究的热点。将BP神经网络算法用于语音情感识别研究，并在汉语情感数据集上进行了相关实验，识别的准确率达到了91．5％，相较于SVM算法分类精度提高了5％。%With the rapid development of technology ,human-computer interaction more and more suffer people’s attention . Research on speech emotion recognition is the focus of academic .In this article ,we use the BP neural network algorithm to research on speech emotion recognition and conducted experiments on chinese sentiment data sets ,recognition accuracy rate reached 91 .5 percent ,compared to the SVM accuracy is improved by 5% .
The Research of Tourism Forecasting Demand Based on BP Neural Network%基于BP神经网络的旅游需求预测研究
Institute of Scientific and Technical Information of China (English)
张华
2014-01-01
在旅游需求众多的影响因素中，旅游者的个人可支配收入情况以及旅游服务质量是影响旅游需求最为主要的因素。本文在对旅游需求影响因素进行简单分析的基础上，以国内某地区2003年-2012年10年间该地区旅游需求相关数据，采用BP神经网络技术对该地区2013年和2014年的旅游需求进行预测。%Many in the tourism demand factors,the tourists as well as personal disposable income affect the quality of tourism services most major tourism demand factors.Based on the factors affecting tourism demand on the basis of a simple analysis to a domestic region in 2003 -2012,10 years in the area of tourism demand data,using BP neural network technology to the region in 2013 and 2014 to forecast demand for travel.
Institute of Scientific and Technical Information of China (English)
张佳伟; 张自嘉
2012-01-01
In order to improve the photovoltaic power forecasting accuracy,the influencing factors of photovoltaic power system's output are analyzed and a particle swarm optimization algorithm is built for BP neural network prediction model of photovoltaic power forecasting. The particle swarm optimization algorithm is used to optimize the internal connection weights and thresholds of neural network in this model. Combining the advantages of the particle swarm optimization and BP neural model, the model achieves a better convergence speed, generalization performance and prediction accuracy. Taking photovoltaic power plant historical data and weather conditions as samples, the model completes training and prediction. The prediction results show that with the particle swarm optimization, BP neural network model prediction accuracy is higher than typical BP neural network, which verifies the effectiveness of the method.%对光伏发电影响因素进行了分析,建立了粒子群算法优化的前向神经网络光伏系统发电预测模型.该模型利用了粒子群算法来优化神经网络内部连接权值和阈值,兼具粒子群和BP神经模型的优点,具有较好的收敛速度,泛化性能与预测精度.将光伏电站发电历史数据与天气情况作为样本,运用所建立的模型进行了训练与预测.结果表明,经过粒子群优化的BP网络模型预测精度高于典型BP网络,验证了该方法的有效性.
Distribution Network Fault Diagnosis Method Based on Granular Computing-BP
Directory of Open Access Journals (Sweden)
CHEN Zhong-xiao
2013-01-01
Full Text Available To deal with the complexity and uncertainty of distribution network fault information, a method of fault diagnosis based on granular computing and BP is proposed. This method uses attribute reduction advantages of granular computing theory and self-learning and knowledge acquisition ability of BP neural network. It put granular computing theory as the front-end processor of the BP neural network, namely simplify primitive information making use of granular computing reduction, and according to the concepts of relative granularity and significance of attributes based on binary granular computing are proposed to select input of BP, thereby reducing solving scale, and then construct neural network based on the minimum attribute sets, using BP neural network to model and parameter identify, reduce the BP study training time, improve the accuracy of the fault diagnosis. The distribution network example verifies the rationality and effectiveness of the proposed method.
Institute of Scientific and Technical Information of China (English)
林维勇; 张小平
2013-01-01
采用基于Levenberg-Marquardt算法改进的BP神经网络构建中药现代化产业综合绩效评估模型,避免了传统BP神经网络训练时间长、收敛慢、易陷入局部极小值的问题.使用Matlab软件建立中药产业现代化发展能力综合绩效评估模型,并通过训练和仿真验证了该网络模型的有效性以及比BP神经网络的其他改进算法训练模型更具高效性.%The improved BP neural network based on Levenberg-Marquardt algorithm is used to build the comprehensive performance assessment model for modern industry of traditional Chinese medicine, which avoids the problems of long training time, slow convergence and easily trapping into local minimum the traditional BP neural network has. A comprehensive performance evaluation model for the development capacity in modern industry of traditional Chinese medicine is established with Matlab software. Through training and simulation the validity of the network model is verified together with its higher efficiency than the BP neural network algorithm and other training model of improved algorithms.
Institute of Scientific and Technical Information of China (English)
易洪雷; 丁辛
2001-01-01
Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to train and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.
Institute of Scientific and Technical Information of China (English)
吴雄喜
2013-01-01
Based on BP neural network,the mechanical properties parameters of AZ91 magnesium alloy under different annealing conditions were obtained by homogenizing annealing. The results show that BP neural network can map relationship between heat treatment process and material properties very well,and prediction accuracy is very good.%基于BP神经网络法，利用均匀化退火工艺改善AZ91镁合金的组织结构，获得了不同退火状态下材料的力学特性参数。结果表明，BP神经网络能够很好地映射热处理工艺与材料性能间的关系，实验值与预测值重合度很好。
Institute of Scientific and Technical Information of China (English)
李春生; 谭民浠; 张可佳
2011-01-01
In order to ensure oil field production for sustainable development, aim at oil field production, the predictive model based on improved BP neural networks is put forward. The structure of traditional BP neural networks and its training algorithm are studied, some weak points of it are found, such as easy to fall into local minimum value and slow convergence. The BP neural networks improved by L-M algorithm is put forward. At the end, simulation experiment of the predictive model based on improved BP neural networks is illustrated. The result of which proves the practicability and the feasibility of this algorithm and high use value in the oil well production prediction.%为了保证油田生产持续稳定地发展,针对油田单井产量提出了基于改进型BP神经网络的预测模型.对传统的BP神经网络的结构和训练算法进行了研究,发现它存在易于陷入局部极小,收敛速度慢等问题.提出了使用LM算法的改进型BP神经网络.最后给出了基于改进型BP神经网络的单井产量预测模型仿真实验.结果证明该算法的实用性和可行性,在油井产量预测方面有一定的实用价值.
基于改进的BP神经网络的柴油发动机故障诊断%Research of diesel engine fault diagnosis based on improved BP neural network
Institute of Scientific and Technical Information of China (English)
巴寅亮; 王书提; 谢鑫
2015-01-01
Diesel engine with high pressure common rail fuel injection technology, improves the com-prehensive performance of diesel engine, but the high pressure common rail diesel engine electronic con-trolled system is more complex, increasing the difficulty of diesel engine fault diagnosis. This paper intro-duce the BP neural network and LM algorithm, and carry on the research on fault diagnosis of engine e-lectronic controlled system based on improved BP neural network. Taking the Great Wall Harvard GW 2. 8TC engine as the experimental object, keeping the engine at idle speed condition, setting up some fault assumption for the engine, collecting the failure data flow of the engine by kinder KT600 fault diag-nosis instrument, using improved BP neural network to establish diagnosis model. The diagnosis results show that the convergence rate of improved BP neural network is quickly, it is effective to diagnose elec-tronic controlled system fault of diesel engine by improved BP neural network.%柴油发动机采用高压共轨燃油喷射技术，提高了柴油机的综合性能，但高压共轨柴油机电控系统比较复杂，增大了柴油机故障诊断的难度。该文介绍了BP神经网路及LM算法，并利用改进的BP神经网络对发动机电控系统故障进行诊断研究。以长城哈佛GW2.8 TC发动机为实验对象，让发动机在怠速状态下，对发动机进行故障设置，利用金德KT600故障诊断仪采集发动机的故障数据流，运用改进的BP神经网络建立诊断模型，诊断结果表明改进的BP神经网络的收敛速度快，运用改进的BP网络诊断柴油机电控系统故障是行之有效的。
Institute of Scientific and Technical Information of China (English)
朱龙俊; 范君艳
2012-01-01
A three layers' input-output model of diffused silicon pressure sensor is built using BP neural network, and then using the improved differential evolution algorithm to optimize the weights and thresholds of BP neural network in MATLAB simulation. Through the training, the compensated diffused silicon pressure sensor's output full-scale error can be achieved 0.035%. Theresults show that the BP neural network modeling based on the improved differential evolution algorithm is meaningful to improve the accuracy of the pressure sensor.%采用BP神经网络来建立扩散硅压力传感器的输出输入模型，其网络模型具有三层结构，采用改进型的差分进化算法来优化BP神经网络的权值和阀值，并在MATLAB中进行了仿真。经训练得到补偿后扩散硅压力传感器的输出满量程误差可达到0．035％，结果表明采用基于改进型差分进化算法的BP神经网络建模对提高智能差压传感器的测量准确度具有参考价值。
Institute of Scientific and Technical Information of China (English)
辛菊琴; 蒋艳; 舒少龙
2013-01-01
Personal recommendation is very effective to find the useful information from database of products for customers in electronic commerce. The paper investigates personal recommendation algorithms based on customer preference model and BP neural networks. In details, a customer preference model is proposed and BP neural network is used to train the model. Movielens database is used to verify the validity of BP neural network model. A content-based personal recommendation algorithm is proposed.%个性化推荐是目前解决电子商务中产品信息过载问题的有效工具之一.对综合用户偏好模型和BP神经网络的个性化推荐算法进行了研究.具体讨论了如何建立用户偏好模型,采用神经网络训练得到目标用户的偏好模型,通过Movielens数据库验证该模型的有效性.提出了一个基于内容的个性化推荐算法.
Energy Technology Data Exchange (ETDEWEB)
Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)
1996-12-31
The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.
Detection of Fraudulent Financial Statements Based on BP Neural Network%基于BP神经网络的虚假财务报告识别
Institute of Scientific and Technical Information of China (English)
邓庆山; 梅国平
2009-01-01
针对虚假财务报告的特点,设计了一种基于BP(反向传播)神经网络的虚假财务报告识别模型.根据1999～2002年的年度审计报告意见,从上市公司中,选择确定了44家虚假财务报告样本,并按照一定的标准选择了44家真实财务报告样本,这88个样本构成训练数据集.类似地,从2003～2006年的上市公司中,选择了73家虚假财务报告样本和99家真实财务报告样本,这172个样本构成测试数据集.10个财务指标被选择为识别变量,使用训练数据集对BP神经网络模型进行训练,并将训练后的模型对测试数据集进行测试,取得了较好的实验结果.%Considering the characteristics of fraudulent financial statements(FFS),this paper designs a FFS detection model based on BP neural network.To carry out the experiment,we choose 44 FFS according to the auditing reports and 44 true financial statements according to some specific standards during 1999-2002 as training data set.Similarly,73 FFS and 99 true financial statements during 2003-2006 are chosen as testing data set.Ten financial ratios are chosen as detection variables.We train the model by using training data set and apply the trained model to the testing data set,good experimental results are obtained.
基于改进的BP神经网络的Overlay网络流量预测%Overlay network traffic prediction based on advanced BP neural network
Institute of Scientific and Technical Information of China (English)
傅秀文; 郑明春
2012-01-01
With the increase of the scale of Internet, network traffic prediction on Overlay has become a research focus gradually. Compared with traditional networks, the features of Overlay network mean that traditional predictions are out of its demand. It proposes a new method that is based on neural network using particle swarm-based simulated annealing, and applies reverse calculation, starts from the ideal optimal value and finds the optimal solution through the shortest path. This method increases the probability of success to find the optimal solution and cut off the running time. Through the simulation it deduces that the proposed method is better than the traditional one obviously.%随着网络规模的增长,Overlay网络流量预测已经日渐成为研究热点.与传统网络相比,Overlay网络本身的特性决定了传统的预测方法已不能适应它的要求.提出一种基于模拟退火的粒子群神经网络来预测Overlay网络的流量,运用反向计算方法,从理想最优值出发,近距离寻找最优解,缩短了求解时间并加大了找到最优解的几率.通过实验仿真可以看出,改进的BP神经网络方法的预测效果要明显好于传统的BP神经网络.
基于BP神经网络的邮轮旅游需求预测%A prediction model for cruise tourism demand based on BP neural network
Institute of Scientific and Technical Information of China (English)
贾鹏; 刘瑞菊; 孙瑞萍; 杨忠振
2013-01-01
科学分析邮轮旅游需求规模是正确引导邮轮产业规划、投入和发展所面临的重要议题.本文首先采用定性分析和定量统计相结合的方法研究邮轮旅游需求的影响因素,并在此基础上建立基于BP神经网络的邮轮旅游需求预测模型,然后基于美国邮轮市场的统计数据进行模型的训练及测试,最后将模型应用于我国邮轮旅游需求规模的预测,精度达到95％以上.%Scientific analysis on cruise tourism demand is the key issue to correctly guide the planning,investment,and development of the cruise industry.First,the method combining the qualitative research with quantitative analysis is used to identify the influential factors of cruise tourism demand.Based on the BP neural network,a model for forecasting the cruise tourism demand is built,and then the model is trained and tested on the basis of statistical data of U.S.A.cruise tourism market.Finally,a model that is used to forecast the demand of Chinese cruise tourism market is proposed and the results indicate that the prediction reaches at the precision above 95％.
基于BP神经网络的文本验证码破解%Text-based CAPTCHA Cracking Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
高原
2012-01-01
以某游戏网站的验证码为例，介绍了基于文本图像验证码的识别和破解过程。破解过程分为字符提取、字符修正和字符识别。在字符提取阶段需要对图片进行预处理降低提取难度，提取主要采用了近似颜色统计法；在字符修正阶段对比了传统的旋转算法和改进旋转算法，然后归一化字符；在字符识别阶段采用了BP神经网络方法，对验证码的识别正确率达70％，每个验证码的平均破解时间为1．625s。%This paper introduces the method for cracking the CAPTCHA of one game site in detail. Our work is mainly divided into three parts： character extraction, character correction and character recognition. In the character extraction phase, we need to preprocess to reduce the extraction difficulty and this phase mainly adopts similar color statistics ; in the character correction phase, an improvement is made of the traditional rotating to normalize the characters ; and in the character recognition phase, BP neural network method is used with a success rate of 70% and an average time of cracking the web site of 1. 625 s.
Tactile Pattern Recognition Based on BP Neural Network%基于神经网络的触觉感知方向识别研究
Institute of Scientific and Technical Information of China (English)
周嵘; 吴皓莹
2016-01-01
触觉感知信息的模式识别可以有效提高人机交互的效率,为此设计了一种触觉传感单元功能模块,可以在2D平面内识别人机接触的方向信息. 采用PCA算法来提取触觉感知数据特征,从而去除数据的噪音并且降低维度;采用BP神经网络对人机接触方式进行识别分类,提高鲁棒性. 对于不同实验对象的训练样本和测试样本进行数据采集,结果表明该方法可以实现93 .1%的模式识别正确率.%To improve the efficiency of communication in human-robot cooperation through tactile information, this paper proposes a method to recognize human intended direction in 2-D using an equipment with tactile arrays.The PCA method is em-ployed in this study to extract essential information thus reduce computation complex and increase robustness.BP neural network is implemented for classifying the intended direction of human operators.Three members of the project team were involved in the study.The efficiency of proposed algorithm is investigated.Experimental results shows that the proposed method could achieve 93.1%recognition accuracy if both the training data and validation data contain tactile images from all the users.
Cancer classification based on gene expression using neural networks.
Hu, H P; Niu, Z J; Bai, Y P; Tan, X H
2015-12-21
Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.
Simulation on Predictive Control Algorithm Based on BP Neural Network Model%BP神经网络模型预测控制算法的仿真研究
Institute of Scientific and Technical Information of China (English)
程森林; 师超超
2011-01-01
为克服被控对象参数变化导致控制精度降低的问题,研究了一种BP神经网络模型预测控制算法.借助最小二乘递推算法在线预测系统模型参数,利用BP神经网络在线预测PID参数以控制被控对象.该算法基于模型预测,首先在线性系统中验证其控制效果,然后将非线性问题作线性处理,采用BP神经网络模型预测PID控制器予以实现控制非线性系统.仿真曲线显示BP神经网络PID控制器用于线性系统可达到高精度控制要求；对于非线性系统有自适应及逼近任意函数的能力.仿真研究表明,该算法与传统BP神经网络PID控制器相比,其自适应能力更强,稳定性更好,控制精度更高.%To overcome the problem of lower control precision caused by parameters varying of the controlled object, the paper proposed a sort of predictive control algorithm based on BP neural network model. In the paper, it applies the predictive parameter of PID controller based on BP neural network on line to control the controlled object, and the system model parameter was on line predicted by means of least recursive squares algorithm. The algorithm would be based on model prediction. It first validats its control effect in the linear system, and then the non-linear problem would be treated as the linearity. The non-linear system would be controlled by use of predictive control algorithm based on BP neural network model. The simulation curves showes that it could achieve high control precision in the linear system to PID controller of BP neural network, and own the ability of adaptation and approaching arbitrary function. The simulation researches show that it is stronger in adaptation, better in stability, and higher in control precision compared with the traditional BP neural network PID controller.
Institute of Scientific and Technical Information of China (English)
林志贵; 姚芳琴; 冯林强; 杜军兰; 李建雄
2015-01-01
针对目前营养盐检测主要是通过化学方法实现，无法获得在线检测的问题，利用营养盐与其影响因子之间的关系，提出结合自适应遗传算法与弹性BP神经网络的预测模型。利用改进的自适应遗传算法，通过交叉、变异获取弹性BP神经网络的初始权值与阈值，加速预测过程。该模型通过营养盐影响因子数据，预测亚硝酸盐浓度。仿真结果表明：基于弹性BP神经网络的预测模型预测营养盐浓度是可行的，其预测得到的亚硝酸盐浓度值的相对误差主要集中于0~30%；结合自适应遗传算法与弹性BP神经网络的预测模型的预测效果好于基于弹性BP神经网络的预测模型。%Currently nutrients are detected by the chemical method. A chemical method cannot get online detection. To solve the problem, based on the relationship between nutrients and their impact factors, a prediction model which combined Adaptive Genetic Algorithm and Elastic BP Neural Network is put forward in this paper. Using the improved Adaptive Genetic Algorithm, the initial weights and thresholds of Elastic BP Neural Network are obtained by the crossover and mutation to accelerate the prediction process. The imporoved model predicts the nitrite by using the data of its impact factors. Simulation results show that it is feasible to predict the nutrient concentration by using the prediction model based on the Elastic BP Neural Network. The relative error of nitrite concentration value mainly focuses on 0-30%. The prediction model based on Adaptive Genetic Algorithm and Elastic BP neural network is better than that based on Elastic BP Neural Network.
Institute of Scientific and Technical Information of China (English)
马健; 盛魁
2013-01-01
对某医院2010-2012年中药的销售及变化情况进行了对比分析。为了更好地保证医院中药的库存数量，提出了一种基于遗传算法优化BP神经网络组合模型，用改进的遗传BP神经网络进行中药销售量预测，并将预测结果和单纯使用BP网络的预测结果进行比较，实验证明遗传 BP神经网络模型具有更高的预测准确度，为医院中药销售及科学管理中药库存量提供科学依据。%Sales and its changes of traditional Chinese medicine in 2010-2012 in a hospital were com-pared and analyzed.To better ensure inventory quantity of traditional Chinese medicine in the hospital, we proposed a optimized BP neural network model based on genetic algorithm.Predicted sales of tradition-al Chinese medicine using optimized BP neural network was compared with the predicted result using a sim-ple BP network.Experiments show that genetic BP neural network model has higher prediction accuracy, providing scientifical reference for sales of traditional Chinese medicine and scientifically managing invento-ry quantity of traditional Chinese medicine in hospitals.
Generalized classifier neural network.
Ozyildirim, Buse Melis; Avci, Mutlu
2013-03-01
In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.
Analysis on Gush of Foundation Pit by BP Neural Network%基于BP神经网络的基坑突涌分析
Institute of Scientific and Technical Information of China (English)
张云; 梁勇然
2001-01-01
在对基坑突涌机理进行深入分析的基础上，建立了基坑突涌的神经网络模型，结合实例对神经 网络进行了训练和检验，结果表明将后传播神经网络运用于基坑突涌的分析和预测是合理、可靠的。%Neural network model for gush of foundation pit is established based on analysis of gush mechanism. The neural network is trained and tested in association with real examples and the results indicate that the use of back propagation neural network to analyze and to predict gush of foundation pit is reasonable and reliable.
面向轻汽油醚化的BP神经网络的模型预测控制%Light Gasoline Etherification Predictive Control with BP Neural Network Model
Institute of Scientific and Technical Information of China (English)
程换新; 伊飞
2012-01-01
针对催化裂化轻汽油（Fcc轻汽油）醚化的过程提出了BP神经网络的模型预测控制，通过控制Fcc轻汽油的流速，来实现重油量浓度指标的控制。应用BP神经网络建立该过程的预测模型，并采用迭代优化的控制算法，根据相应的性能指标，不断地修正神经网络的权值，从而整定下一批次的控制信号。通过Matlab里的神经网络工具箱，建立一个有参考模型的神经网络预测控制器，观测最终的实际输出。%Predictive control with BP Neural Network model for etherification of Fcc light petrol is proposed. The control of heavy fuel oil concentration is realized by controlling the flow rate of Fcc light petrol. The prediction model for the process is built up with BP Neural Network with adopting iterative optimization algorithm, and the weights of neural network is corrected continuously based on the performance indicators to determine the next batch controlling signal. Neural network predictive controller is built with a reference model by using Neural Network toolbox in matlab, and observes the actual output.
Quasi-BP neural network inversion of gravity gradient tensor%重力梯度张量的拟BP神经网络反演
Institute of Scientific and Technical Information of China (English)
郭文斌; 朱自强; 鲁光银
2011-01-01
基于重力梯度张量是反映重力场空间变化率的参数,比传统的重力异常具有更高的分辨率和更丰富的信息,将改进的BP神经网络算法应用于重力梯度张量的反演中并分析其反演效果.该算法是一种基于RPROP算法的拟BP神经网络反演算法,采用三层神经网络结构,用隐层神经元表示物性单元的密度值,根据RPROP算法自动修改各单元密度值,从而得出场源空间的密度分布.研究结果表明:采用这种算法对重力梯度张量进行反演计算,收敛速度快,对初始模型依赖性小,可准确反映出异常体形态特征和密度特征.%Based on the fact that gravity gradient tensor is a parameter which can reflect the spatial variation of gravity field, and that it has a higher resolution compared to the traditional gravity anomaly, a method for interpretation of gravity gradient tensor was proposed. The method is a kind of quasi-BP neural network algorithm which is based on RPROP algorithm. A three-layer network and the hidden layer neurons denote physics value were used. The physics value was automatically modified according to RPROP algorithm, and the physical distribution of field source was gotten. The results show that the method has a fast convergence speed and little dependence on initial model used in the inversion of gravity gradient tensor date, and can reflect the shape and density characters of anomalous body.
Institute of Scientific and Technical Information of China (English)
梁哲浩; 鲁伟
2015-01-01
目的：探讨超声结合人工神经网络技术在女童中枢性性早熟诊断中的应用价值。方法选用170例性早熟女童进行常规超声检查子宫、卵巢，以其中130例的子宫体积、卵巢体积以及双侧卵巢最大卵泡内径为输入变量，以中枢性性早熟或非中枢性性早熟为输出变量，建立反向传播（BP）神经网络，并对另40例性早熟病例分类。结果利用 BP 神经网络结合常规超声检查对中枢性性早熟诊断的敏感性、特异性和准确率分别为95．0％、85．0％、90．0％。结论神经网络结合超声检查对中枢性性早熟的诊断和鉴别诊断具有较大的价值。%Objective To explore the value of ultrasonic combined with Back‐propagation artificial neural network in the diagnosis of central precocious puberty .Methods In 170 girls with precocious puberty ,the uterine and ovarian were ex‐amined with ultrasound ,in which 130 cases of uterine volume ,ovarian volume and bilateral ovarian follicles biggest diame‐ter were taken as inputs ,the central precocious puberty or non‐central precocious puberty as output variable .The back‐propagation (BP) neural network was established using such data .The other 40 cases were sorted by this BP neural net‐work .Results The diagnostic sensitivity ,specificity and accuracy of the BP neural network combination of ultrasound were 95 .0% ,85 .0% and 90 .0% ,respectively .Conclusion The BP neural network in combination of ultrasound is help‐ful in diagnosing central precocious puberty .
基于BP神经网络的盘管泄漏检测方法研究%Study of the coil-leak detective method based on the BP neural network
Institute of Scientific and Technical Information of China (English)
袁寅; 袁昌明; 王强
2011-01-01
The present paper is devoted to the study of the coil-leak detective method based on the BP neural network in hoping to extract its boundless application prospect. As a matter of fact, with the ever-increasing chemical safety demands, traditional offline pipe leak detection methods, such as pressure-keeping methods, which fail to meet the needs of on-line detection and control of the leakage of water coil of the reaction kettle for their poor real-time up-to-date performance. Flow balance method, though still effective in online leak-detection, also fails to meet the challenges of the fast-changing working conditions. Therefore, scientists began to face the challenge by using the flow balance method combined with neural network. In order to study the validity of this detection method, we have established an experimental platform of coil leak detection based on S7 - 300PLC. The platform can not only be able to simulate the leak of water coil, collect the flow data, but also produce warning alarms and help to control some sudden, unexpected leakage. Therefore, we have made an analysis of the flow changes in the inlet and outlet of the water coil of the reactor by means of a series of simulated experiments with the coil leakage, including the fast changing situations of working conditions and the leakage variations, we have also extracted characteristic signals (RMS) from the flow signal to protract RMS curve of flow. Careful comparison of the RMS curves of normal, leak and fast changing situations of working conditions, has offered us possibilities-to make clear the features of some quite different conditions. We have extracted RMS of the flow to construct the input matrix of the neural network. Through searching for a large number of experimental data to train the BP neural network, it becomes possible to work out the optimal neural network structures by comparing the network training error results of various structures. It is the BP neural network model we have
Institute of Scientific and Technical Information of China (English)
王勇; 陈梅
2014-01-01
从数码相机的RGB信号重构物体表面的光谱反射率是光谱颜色管理研究中的重要课题之一.提出了一种基于误差反向传播前馈神经网络(BP)和主元分析法(PCA)实现色卡的表面光谱反射率重构的新算法.通过对三种色卡进行光谱重构实验研究了BP神经网络的最优结构和主元数的最佳选择,验证了算法的精度.实验结果表明,采用适当的BP神经网络和主元分析相结合的新算法能够精确重构同类色卡的表面光谱反射率.%Reconstructing the spectral reflectence of the object surface from RGB signals of digital camera is one of the important studies of spectral color managament.A new algorithm based on back propagation (BP) neural network and principal component analysis (PCA) is proposed to realize the spectral reflectence reconstruction of color atlas.The optimal structure of BP neural network and the number of principal components are studied in the spectral reflectence reconstruction experiments of three color atlases and the accuracy of the algorithm is also testified.The experimental results show that the new algorithm of appropriate BP neural network combined with PCA is satisfied to accurately reconstruct the spectral reflectence of the same kind of color atlas.
Institute of Scientific and Technical Information of China (English)
黄康勤; 郑景辉
2013-01-01
目的:建立基于BP神经网络的地高辛血药浓度预测神经网络拟合模型,并在已建立的神经网络模型的基础上,进行地高辛血药浓度预测和影响因素的敏感度分析,利用本研究的建模结果,为BP神经网络建模的方法学提供一定的参考依据,并能帮助医务人员做出正确的决策和分析.方法:在SPSS Clementine12.0中进行建模和预测,预测结果用SPSS17.0进行ROC分析.结果:BP神经网络的拟合度和预测准确度为85.671％,其中性别、AST、日总剂量、TBIL、单次剂量对患者的治疗结果影响最大.结论:根据患者的一般资料和临床常规资料建立的地高辛血药浓度预测神经网络模型是可行有效的.%Objctive: Establish simulation model of neural network for prediction of digoxin blood concentration based on BP neural network, on the basis of the neural network model established perform digoxin blood concentration prediction and sensitivity analysis of influencing factors, with the modeling results, provide some references for BP neural network modeling methodology, and help medical personnel to correctly make decision and analyze; Method: Model and predict in SPSS Clementinel2.0. The prediction results are subject to ROC analysis with SPSS17.O. Results: Degree of fitting and forecasting accuracy of BP neural network is 85.671%. Among them, gender, AST, total daily dose, TBIL and single dose have the greatest impact on the treatment of patients. Conclusion: Neural network model for prediction of digoxin blood concentration established according to the patient's general materials and clinical routine data is feasible and effective.
Institute of Scientific and Technical Information of China (English)
吴俊利; 张步涵; 王魁
2012-01-01
进行较准确的风速预测对含大规模风电场的电力系统进行经济调度具有重要意义.针对目前神经网络法、时间序列法、卡尔曼滤波法等算法在短期风速预测上精度不高的缺陷,引入Adaboost算法对前馈(back propagation,BP)神经网络算法进行改进,提出了基于Adaboost的BP神经网络算法,并将该方法应用于短期风速预测.经算例分析,该算法在超前1h和2h的风速预测精度优于其他2种算法,且该算法在高风速段(10m/s以上)平均绝对百分比误差低于7.5％,具有较高的工程应用价值.%It is significant for economic dispatching of power grids containing large-scale wind farms to forecast wind speed more accurately. In allusion to the defect of insufficient accuracy in current short-term wind speed forecasting by neural network, auto-regressive moving-average (ARMA) time series analysis, Kalman filtering and so on, the Adaboost algorithm was led in to improve back propagation (BP) neural network algorithm, and an Adaboost-based BP neural network method was proposed and applied to short-term wind speed forecasting. Results of analyzing calculation example showed that using the proposed Adaboost-based BP neural network the accuracy of one or two hour-ahead wind speed forecasting was superior to respective forecasting accuracy by neural network and ARMA time series analysis, and the mean absolute percentage error of wind speed forecasting by the proposed algorithm was lower than 7.5% in high wind speed period (higher than10m/s). Thus the proposed method is applicable in engineering application.
全息图的小波域BP神经网络压缩实现%Implementation of hologram compression using BP neural network in wavelet domain
Institute of Scientific and Technical Information of China (English)
侯阿临; 吴亮; 廖庆; 王崇锦; 郭俊良
2014-01-01
计算全息图的有效存储和快速传输对于实现真正意义上的动态三维全息显示有着十分重要的意义，然而计算全息图信息量庞大，不利于传输和存储，这就迫切需要对大数据量的全息图进行快速高效的压缩。基于此，提出一种基于小波域BP神经网络的全息图压缩技术，即先用小波变换对全息图进行预处理，通过将小波基与全息图的内积进行加权和来实现全息图的特征提取，然后将提取的特征向量代入神经网络以完成函数逼近、分类，实现全息图的压缩。该方法可获得124.52∶1的压缩比且仍能获得较清晰的再现像，实验结果很好地证明了该方法的可行性和有效性，且算法结构简单，运算速度快，能在较大压缩比下恢复出令人满意的再现像。%Holographic storage and fast transmission are great significance for realizing the true dynamic 3 -D holo-graphic display,but the huge amount of information calculation of computer-generated hologram (CGH)is not condu-cive to the transmission and storage.So there is an urgent need for fast and efficient compression method aiming at holograms with large amounts of data.Based on this,a BP neural network algorithm of hologram compression in wave-let domain is proposed.Firstly,computer-generated hologram pretreatment is carried out by wavelet transform.Sec-ondly,the inner product of wavelet and holograms are weighted and used to implement the feature extraction of holo-gram.Then the extracted feature vectors are substituted into neural network so as to implement the function approxi-mation,classification and hologram compression.The compression ratio can reach 1 24.52 ∶1 and still get a clear im-age reproduction.The experimental results clearly show the feasibility and effectiveness of the method.The proposed algorithm has the advantages of simple structure and fast calculation speed,and can recover satisfied reconstructed im-age at the
旅游危机预警的BP神经网络模型及应用%Application of BP Neural Network Model in Tourism Crisis Early Warning System
Institute of Scientific and Technical Information of China (English)
王汉斌; 李晓峰
2012-01-01
The development of tourism faces many crises, such as war, disease and natural disasters. This thesis analyzed the factors that affect tourism development, selected the early warning indicators which gave tourism the most profound influence, combined with the date sample of related indexes, applied the BP neural network technology and established a warning system based on the BP neural network model. With the neural network toolbox in the MATLAB for early warning simulation experiment and detection, it proved that the model had a good training performance and high early warning accuracy, which is useful for tourism crisis early warning, detection and analysis in China.%从分析影响旅游业发展的因素出发,选择了对旅游业发展影响最大的危机预警指标,并结合相关数据样本,应用BP神经网络技术,研究建立一种基于BP神经网络模型的旅游危机预警系统,借助MATLAB中的神经网络工具箱进行仿真训练和检测,训练结果表明模型性能良好,预警准确率高,能够很好的用来对旅游危机进行预警、检测和分析研究.
Institute of Scientific and Technical Information of China (English)
金玉琴; 周金海; 赵群; 张兴德
2013-01-01
缓释制剂的处方优化属于多因素、多水平的复杂优化问题，人工神经网络很适于处理这类复杂的多变量非线性关系。本文在对缓释制剂特性及影响其处方设计质量的重要因素进行细致分析的基础上，应用BP人工神经网络建立缓释制剂处方质量预测模型。研究结果表明，BP神经网络可以有效地进行缓释制剂处方质量预测，是缓释制剂处方优化的有力工具。%Optimization on sustained release preparation formulation is a multi-factor, multi-level complex optimiza-tion problem. Artificial neural network is very suitable for dealing with such complex multivariable nonlinear system. Based on analyzing the characteristics of sustained release preparation and the main influential factors of its quality, this paper focused on building a quality forecast model for sustained release preparation formulation by using BP neural network. The results showed that the BP neural network can effectively forecast the quality of sustained re-lease preparation formulation. It is a powerful optimization tool of sustained release preparation formulation.
BP神经网络在心理障碍诊断中的应用研究%THE APPLIED RESEARCH OF BP NEURAL NETWORK IN THE DIAGNOSTIC OF MENTAL DISORDERS
Institute of Scientific and Technical Information of China (English)
崔玉洁; 熊海灵; 朱明强
2012-01-01
This system illustrates the basic principles of the neural network algorithm and establishes a psychological diagnosis model based on L-M algorithm which uses common mental illness among college students as subjects. By fully using L-M algorithm' s global optimization and local convergence characteristics with BP neural network optimization, the psychological diagnosis model based on improved BP algorithm is established to realize simple mode recognition. Simulation outcomes illustrate that the model reduces training iterations and time with high accuracy. When applying the neural network into establishing a psychological diagnostic system, it is effective too.%以高校大学生常见心理疾病作为研究对象,充分利用L-M算法的全局寻优性及局部收敛性的特点对BP神经网络进行优化,建立基于改进的BP算法的心理诊断模型,实现简单的模式识别.仿真结果表明:该模型减少了训练迭代次数,缩短了训练时间,具有较高的准确性,应用该神经网络建立心理障碍诊断系统也是有效的.
基于BP神经网络的汽车发动机凸轮轴数控磨削加工工艺优化%Optimization of NC Camshaft Grinding Process Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
吴明
2013-01-01
随着汽车工业的迅速发展，作为汽车发动机的关键零件，凸轮轴的需求量越来越大，对其加工质量和加工效率的要求也越来越高。本文概述了凸轮轴磨削加工的现状，介绍了人工神经网络特别是BP神经网络的相关理论，最后采用BP神经网络算法对凸轮轴磨削加工部分工艺参数进行优化。%Camshaft is a key part of automobile engine. With the rapid development of automobile industry, the demand for camshaft is growing and the requirements for its processing quality and efficiency are increasingly high. In this paper, the current situation of camshaft grinding is summarized. The artificial neural network theory, especially BP neural network, is introduced. Finally, some processing parameters for camshaft grinding are optimized based on BP neural network algorithm.
Institute of Scientific and Technical Information of China (English)
门志国; 彭秀艳; 王兴梅; 胡忠辉; 孙双双
2012-01-01
为取得更有效的船舶运动预报效果,提出了一种利用遗传算法(GA)优化单输出三层反向传播(BP)神经网络辨识Volterra级数核的算法.在船舶航行姿态时间序列的混沌特性识别基础上,分析了GA、BP神经网络和Volterra级数模型的特征.利用GA优化BP神经网络获得最优的初始权值和阈值,根据BP神经网络算法求得最终的最优权值和阈值.进行Taylor级数分解,得到Volterra级数各阶核,对船舶的横摇运动时间序列进行多步预报.仿真实验表明:所提方法预报精度高、时间长,具有有效性和适应性.%In order to obtain more effective prediction results of ship motion, a method is proposed using the genetic algorithm ( GA ) optimized single-output three-layer back propagation ( BP ) neural network to identify Volterra series kernels. The GA, the BP neural network and the features of the Volterra series model are further analyzed based on the chaos characteristic identification of ship motion attitude time series. The best initial weights and thresholds are obtained by using the GA optimized BP neural network. The final optimal weights and thresholds of model parameters are obtained by the BP neural network algorithm. The multi-step prediction of the time series of a ship roll motion is done by making Taylor series decomposition to obtain Volterra series kernels of each order. The simulation experiments show that the proposed algorithm has high precision and long prediction time and effectiveness and adaptability.
An Intelligent Virus Detection Method Based on BP Neural Network%一种基于BP神经网络的智能检测病毒方法
Institute of Scientific and Technical Information of China (English)
朱俚治
2014-01-01
After several decades of development,the virus technology today is not merely the simple virus technology,but having com-bined with other technologies such as hacking and so on. So the hazard and transmission speed of virus today is over the original virus. The contagiousness of the virus and virus variant technology applied in virus has made them show certain intelligence. In order to deal with the intelligence viruses present,put forward an intelligent virus detection method after referring to existing methods. While using the sandbox as the operating environment of virus detection,also use the BP neural network as the tools. Afterwards,a new method of virus detection is given,which will be able to help to detect whether the suspected program exists viruses.%病毒技术历经半个世纪的发展，如今已不是单纯的病毒技术，而是融合了其他黑客等技术，因此当今病毒的危害性和传播速度远远超过了病毒原始形态。病毒的传染性以及病毒的变种技术在病毒上的应用使得病毒呈现一定的智能性。因此，为了应对病毒的变种技术以及病毒其他方面所呈现出的智能性，文中参考已有的检测病毒方法之后，提出了一种具有智能性的检测病毒的方法。文中使用沙箱作为检测病毒的运行环境，并使用BP神经网络作为检测病毒的工具，然后给出了一种具有一定智能性的病毒检测方法。该方法能够对被怀疑的非法变化的程序中是否存在病毒做出判断。
Automobile Transmission Shift Control Based on MMAS and BP Networks
Directory of Open Access Journals (Sweden)
Jianxue Chen
2013-08-01
Full Text Available The neural network control model of automobile automatic transmission has been developed, which make the optimum shift decision based on the vehicle velocity, the vehicle acceleration and the throttle opening. The MAX-MIN ant syste (MMAS is introduced to train the neural network weights and thresholds. The basic theory and steps of MMAS algorithm are given, and applied in the automatic transmission shift control. Experimental results show that the automatic transmission shift control system based on MMAS, comparing to the system based on ACO-BP, has better capability of gear recognition, and can make shift decision promptly and effectively.
Chaotic diagonal recurrent neural network
Institute of Scientific and Technical Information of China (English)
Wang Xing-Yuan; Zhang Yi
2012-01-01
We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure andlearning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.
Chung-Ming Kuan
2006-01-01
Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
Optimizing neural network forecast by immune algorithm
Institute of Scientific and Technical Information of China (English)
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
2006-01-01
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
Institute of Scientific and Technical Information of China (English)
刘议聪; 朱泓光; 宋永强
2016-01-01
To get the global optimal point, propose an optimize BP neural network by an improved particle swarm optimization (PSO) to implement nuclide identification. It changes inertia weight and learning factor dynamically with self-adaption to optimize the weight value and threshold value of BP neural network. It gets the global optimal value of the particle swarm by training BP neural network to identify models. Finally, it implements nuclide identification by using the optimal weight and threshold value. The experiment shows our proposed method can not only converge to the optimal value faster but also do a good balance between local search and global search. Therefore, it significantly improves the convergence speed and the accuracy of nuclide identification.%为获得全局最优点,提出一种改进粒子群算法优化BP神经网络实现核素识别方法.该算法用一种动态改变惯性权重与学习因子的自适应方法,优化BP神经网络的阈值与权值,通过训练BP神经网络识别模型得到粒子群的全局最优解,利用最优权值与阈值实现核素识别.分析结果表明:该方法不仅能更快地收敛于最优解,同时能更好地平衡全局搜索和局部搜索能力,有效地改善算法的收敛速度和识别精度.
Institute of Scientific and Technical Information of China (English)
廖寅飞; 刘炯天; 王永田; 曹亦俊
2011-01-01
分析了影响旋流-静态微泡浮选柱气含率的因素,选取循环压力、进气量和起泡剂浓度3个主控因素作为BP神经网络模型的基本特征量,建立了浮选柱气含率与主控因素之间的相关关系和BP神经网络预测模型,并对气含率进行了预测分析.结果表明:BP神经网络能合理地表达浮选柱气含率与其主控因素之间的非线性映射关系,预测结果与实测值之间的相对误差一般小于5%,达到了较高的预测精度.%The factors that affect the gas holdup in cyclonic-static micro-bubble flotation column were analyzed. Taking the main controlling factors, including pressure of circulating pump, air flow and frother concentration, as the basic characteristic quantity of BP neural networks models, the correlation between the gas holdup, the factors, and the BP neural networks prediction model were proposed for prediction of the gas holdup in flotation column. The results show that the BP neural networks model can actually reflect the nonlinear relationship between the gas holdup in flotation column and main controlling factors. A better accurate prediction in which the errors between the predicted values and measured values are less than 5 ％ is achieved.
Institute of Scientific and Technical Information of China (English)
刘然; 傅攀
2014-01-01
为更好地对滚动轴承进行状态监测和故障诊断，采集3种不同状态下的滚动轴承振动信号，根据振动信号特点提取其时域和频域的相关特征，然后分别利用SVM （支持向量机）和BP神经网络进行模式识别。不断减少每种状态下训练样本集的个数，利用2种不同的方法进行模式识别。当每种状态下的样本个数为3个时，支持向量机仍然能准确地将测试样本进行分类，而BP神经网络完全无法识别。实验结果表明，支持向量机比BP神经网络更适合于小样本的故障诊断。%In order to make rolling bearing condition monitoring and fault diagnosis better ,three kinds of different states vibration signals of rolling bearing were collected , the features of different states' vibration signals in time and frequency domain were extracted .The support vector machine(SVM) and BP neural network was used to conduct the pattern recognition .In the case of decreasing the number of training samples in each state ,two methods were applied to pattern recognition .When the number of samples in each state was 3 ,the SVM was still able to classify the test samples accurately ,but the BP neural network could’t identify completely .Experimental results show that SVM is more suitable for small samples in fault diagnosis than BP neural network .
Institute of Scientific and Technical Information of China (English)
尤杰; 车轶; 仲伟秋
2011-01-01
Based on the test data analysis method, characteristic parameters of the existing buildings, i.e. service time, construction time, in-situ inspection time of structure, design value of concrete strength, and carbonation depth of concrete were extracted, and the artificial neural network model was developed to predict the degradation of concrete strength of the existing buildings. The back propagation (BP) algorithm was improved by using the momentum method and adaptive adjustment method. Both minimum and maximum values of concrete strength were predicted using the trained BP neural network and were compared with the measured values.Results show that using BP neural network to predict the degradation of concrete strength of existing buildings is feasible. Results of this study can provide references for the existing building seismic performances of large area surveys.%在分析检测数据的基础上,提取了结构服役时间、结构建造时间、结构检测时间、混凝土设计强度和混凝土碳化深度等特征参数,建立了预测既有建筑混凝土强度退化的人工神经网络模型.采用动量法和自适应调整法改进了BP算法;采用训练好的BP神经网络对既有混凝土强度最小值和混凝土强度最大值进行了预测,并与实测值进行了对比.结果表明:利用BP神经网络对既有建筑混凝土强度退化进行预测是可行的,该研究成果可为既有建筑大面积的抗震性能普查提供参考.
Institute of Scientific and Technical Information of China (English)
沈波; 王李仁; 许旭艳; 郑能雄
2011-01-01
[目的]探索BP神经网络在细菌性痢疾预测模型的应用,为细菌性痢疾的预防控制措施提供科学依据.[方法]用Matlab7.2软件包中的神经网络工具箱,以1988～2007年的资料建立福州市山区菌痢流行的BP神经网络模型,并以2008年的资料验证其预测成功率.[结果]神经网络经学习和训练,训练误差下降并趋于稳定,回代相关系数为0.815,模型的预测成功率为10/12.[结论] BP神经网络在气象要素与菌痢发病之间建模是可行的,可以作为预测菌痢流行的一种新方法.%[Objective]To explore application of BP neural network model in prediction of bacillary dysentery, in order to provide the scientific data for making strategies.[Methods]The forecasting model for bacillary dysentery was established by using the neural network toolbox of Matlab7.2 software package.In the studies of forecasting model, the data in Fuzhou from 1988 to 2007 were chosen to analyze.The established forecasting model was also tested by the data of bacillary dysentery in 2008.[Results]After training the neural network, the error of performance decreased and the coefficient of regression was 0.815.The efficiency of the forecasting model for bacillary dysentery was 10/12.[Conclusion]BP neural network model is feasible to analyze the relation of meteorological factors and bacillary dysentery.BP neural network model could be used as a new effective method for forecasting of bacillary dysentery.
Institute of Scientific and Technical Information of China (English)
郭亮; 陈维荣; 贾俊波; 韩明; 刘永浩
2011-01-01
针对光伏电池复杂难以建模的非线性特性,本文提出一种基于粒子群算法(PSO)的反向传播(BP)神经网络建模方法.神经网络具有很强的非线性拟合能力,但同时也存在收敛速度慢、容易陷入局部极值、建模精度不高等缺点.本文采用粒子群算法来优化神经网络的内部连接权值,以改善神经网络的性能,并基于这种改进的神经网络构建光伏电池动态模型.测试及仿真结果表明,通过此法建立的光佚电池模型辨识精度高,收敛速度快,取得了较好的效果.%In view of serious complexity and nonlinearity of photovoltaic array, its modeling is very difficult, therefore a Back Propagation (BP) Neural Network model based on Particle Swarm Optimization (PSO) algorithm is proposed. Neural Network has excellent nonlinear fitting ability, but it also has some shortcomings, such as low convergence speed, easy to fall into local optimal value, and low accuracy, etc. PSO algorithm is used to optimize the inner connection weights of Neural Network; therefore the performance of Neural Network is promoted. Modeling of photovoltaic array is based on this improved Neural Network. Test and simulation results showed the high identification accuracy and high convergence speed of this model. Key words: photovoltaic array; neural network; particle swarm optimization algorithm Constructing stable nodes to suppress common-mode EMI for power converters Abstract: In this paper, a simple approach to suppress common mode conducted electromagnetic interference (EMI) is proposed. The method is explained in detail with Boost converter adopted as a typical example. Most of common mode conducted EMI due to the high dv/dt nodes in the converter can be suppressed by constructing stable potential nodes ( dy/dr almost zero) in the circuit, which is implemented by changing placement of the boost inductor and using the common-anode diode instead of the common-cathode one. It is verified by
Recognition of Continuous Digits by Quantum Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper describes a new kind of neural network-Quantum Neural Network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15 percent error reduction is achieved on a speaker-independent continuous digits recognition task compared with BP networks.
Institute of Scientific and Technical Information of China (English)
朱凤林; 韩卫
2013-01-01
On the basis of the nonlinear reflection ability of artificial neural network, we established three multi-layer feedforward neural network models in Matlab 7.1 simulation platform to monitor the Baishi reservoir deformation in Liaoning Province. The three models adopt different modified BP algorithms, i. e. LM algorithm, BR algorithm, and GDX algorithm. According to the fitting and prediction results, we compared the application results of the three models and concluded that the BP network based on LM algorithm was more suitable for building dam' s displacement monitoring model to realize real-time and effective monitoring.%基于人工神经网络的非线性映射能力,应用Matlab7.1网络仿真平台,结合辽宁省白石水库多年大坝位移实测数据,建立了3种不同改进BP算法的多层前馈神经网络模型.并通过LM算法、BR算法、GDX算法的BP网络模型的拟合、预报结果,对3种模型的应用效果进行了比较分析,得出了LM算法的BP网络更适合用于建立坝顶位移监控模型的结论,以实现对大坝位移实时、有效的监控.
Institute of Scientific and Technical Information of China (English)
张冉
2011-01-01
随着3G网络的全面普及，手机广告目前已逐渐成为商家抢占市场的一种营销手段，但手机广告投放的精准性是目前比较突出的一个问题。本文介绍了BP神经网络以及AdaBoost算法的基本原理，研究了应用AdaBoost结合BP神经网络算法的增量学习模型，该模型基于用户历史点击记录来预测手机用户感兴趣的广告类别，以提高手机广告投放的精准度。%With the overall popularity of 3G networks,mobile advertising business has become a marketing tool to seize the market,but the precise nature of mobile advertising is a more prominent issue.This article describes the BP neural network and the basic principles of AdaBoost algorithm to study the application of BP neural network algorithm AdaBoost with incremental learning model that records based on user click history to predict the mobile phone users are interested in advertising categories,in order to improve the mobile advertising the accuracy.
Institute of Scientific and Technical Information of China (English)
丁硕; 常晓恒; 巫庆辉
2014-01-01
Three common numerical optimization algorithms are first studied, including conjugate gradient algorithm, quasi-newton algorithm and LM algorithm. The three kinds of algorithms are used to improve BP neural network respectively and the corresponding classification models based on BP neural network are established. Then the models are used in pattern classification of two-dimensional vectors, and their generalization abilities are also tested. The classification results of different classification models based on BP network are compared with each other. Simulation results show that for small or medium scale networks, BP neural network improved by LM algorithm has the most precise classification result, the fastest convergence speed and the best classification ability. The one improved by conjugate gradient algorithm has the biggest error, slowest convergence speed and worst classification ability. While the classification precision, convergence speed and classification ability of quasi-newton algorithm lie between the above two algorithms.%研究了共轭梯度算法、拟牛顿算法、LM 算法三类常用的数值优化改进算法，基于这三类数值优化算法分别对BP神经网络进行改进，并构建了相应的BP神经网络分类模型，将构建的分类模型应用于二维向量模式的分类，并进行了泛化能力测试，将不同BP网络分类模型的分类结果进行对比。仿真结果表明，对于中小规模的网络而言， LM数值优化算法改进的BP网络的分类结果最为精确，收敛速度最快，分类性能最优；共轭梯度数值优化算法改进的BP网络的分类结果误差最大，收敛速度最慢，分类性能最差；拟牛顿数值优化算法改进的BP网络的分类结果误差值、收敛速度及分类性能介于上述两种算法之间。
The comprehensive evaluation of software quality based on LM-BP neural network%基于LM-BP神经网络的软件质量综合评价
Institute of Scientific and Technical Information of China (English)
郑鹏
2016-01-01
由于传统软件质量评价存在主观性等缺陷。针对这种情况，提出基于LM‐BP神经网络的软件质量综合评价方法。算法以ISO／IEC 9126为软件质量度量标准，解决了标准BP算法存在的问题，建立了LM-BP神经网络软件质量综合评价模型，为软件质量综合评价提供了一种新的方法。实验结果表明，LM-BP神经网络的软件质量综合评价能客观、定量、快速且准确得到软件质量综合评价结果，该评价模型具有客观性和实用性。%Because traditional software quality evaluation has some defects such as subjectivity , we proposed a method based on levenberg marquardt-back propagation(LM-BP) neural network software quality comprehensive evaluation .Based on ISO/IEC 9126 software quality model ,the algorithm solves the problems existing in the standard BP algorithm ,establishes the LM-BP neu‐ral network software quality comprehensive evaluation model ,and offers a new method for com‐prehensive evaluation of software quality .Experimental results show that the LM-BP neural net‐work software quality comprehensive evaluation is objective ,quantitative ,fast and accurate .The evaluation model is objective and practical .
Institute of Scientific and Technical Information of China (English)
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
Institute of Scientific and Technical Information of China (English)
丁彦蕊; 徐星宇; 须文波
2012-01-01
微生物的热稳定性与代谢网络的拓扑特征之间存在着密切的关系.分析了460个微生物的22个代谢网络拓扑特征,分别利用BP神经网络和概率神经网络建立分类模型,以比较两种算法预测微生物热稳定性的效果.通过分析隐层数目和扩展常数对分类效果的影响发现,相对于BP神经网络,简单易用、稳定性好的概率神经网络更适合于从代谢网络特征角度预测微生物的热稳定性.当耐热菌与常温菌数比例为1∶1、扩展常数为0.09时,概率神经网络对常温菌、耐热菌的预测率分别为72.83％和82.61％.预测率也表明,聚集系数、介数等代谢网络拓扑特征影响着微生物的热稳定性.%There is a significant relationship between microorganism thermostability and metabolic network topology. In this paper, we firstly analyzed 22 metabolic network parameters of 460 microorganisms, and then, we built the classification models using BP Neural Network and Probabilistic Neural Network respectively to compare the efficiency of predicting the microorganism thermostability. After studying the influence of the hidden layer number and spread constant on the classification results, we found Probabilistic Neural Network is suitable for predicting the microorganism thermostability by using metabolic network parameters because of its easiness of use and high stability comparing with BP Neural Network. When the ratio of thermophilic microorganism number : mesophilic microorganism number is 1:1, and the expand constant is 0.09, the prediction accuracies of mesophlic and thermophilic microorganism are 72. 83% and 82.61% respectively. The prediction accuracies also show that the metabolic network parameters including clustering coefficient and betweeness can influence the microorganism thermostability.
Institute of Scientific and Technical Information of China (English)
张培; 陈光大; 张旭
2011-01-01
It is unnecessary to establish concrete function expression, the known discrete data can be fitted by using neural network to extend hydraulic turbine combined characteristic cure. And we can also add boundary conditions to predict unknown zones, so as to raise the work efficiency and data precision in data treatment concerning hydraulic turbine combined characteristics. This paper intro- duces the use of gP neural network and RBF neural network in extending hydraulic turbine combined characteristic curve. I.astly, the results of the two methods are compared and some conclusions are obtained.%利用神经网络对水轮机综合特性曲线进行数据处理和延伸，不必建立具体的函数关系表达式，就可对已知的离散数据进行拟合。并且还可以结合边界约束条件对未知区域内的数据进行预测，从而提高了水轮机综合特性曲线数据处理的工作效率和数据精度。分别介绍了用BP神经网络和RBF神经网络对水轮机综合特性曲线数据处理和延伸的方法。并采用一机组的样本数据进行训练，比较2种方法的训练结果得出结论。
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
Institute of Scientific and Technical Information of China (English)
林虹江; 周步祥; 冉伊; 詹长杰; 杨昶宇
2015-01-01
In the constant pressure control method , there is a big error when the BP neural network is adopted to pre-dict the voltage at the maximum power point .In view of this problem , the genetic algorithm was proposed in this paper to optimize the BP neural network , and then the optimized algorithm was used to predict the voltage at the maximum power point ofthe photovoltaic system and this value was substituted for the constant voltage parameter of the MPPT control algorithm for the photovoltaic power generation system based on constant voltage .At the same time , in combi-nation with the constant voltage control method , a simulation model of the improved constant voltage photovoltaic sys-tem MPPT control based on the GA-BP neural network learning algorithm was built .Finally the simulation results of examples proved that the proposed photovoltaic system MPPT control algorithm based on GA-BPNN could track down the photovoltaic maximum power point quickly and accurately , and compared with the BP neural network algorithm , the perturbation and observation method and the FUZZY control algorithm , the MPPT control algorithm had better sta-bility and higher precision .%针对恒压控制法中采用BP神经网络预测最大功率点处电压存在较大误差的情况，提出了用遗传算法来优化BP神经网络，然后用优化后的算法来预测光伏系统最大功率点之处的电压，并以此值代替基于恒电压的光伏发电系统MPPT控制算法中的恒电压参数；同时结合恒电压控制法建立了基于GA-BP神经网络学习算法的改进恒压型光伏系统MPPT控制的仿真模型。最后算例仿真结果证明所提的基于GA-BPNN的光伏系统MPPT控制算法能够快速准确地进行光伏最大功率点跟踪，并且相比于 BP 神经网络算法、干扰观察法及FUZZY控制算法其稳定性更好、精度更高。
Remote Sensing Image Segmentation with Probabilistic Neural Networks
Institute of Scientific and Technical Information of China (English)
LIU Gang
2005-01-01
This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.
Institute of Scientific and Technical Information of China (English)
黄奇瑞
2015-01-01
针对非监督分类算法分类精度不高、监督法分类算法的训练样本需要人工选择且容易误选的问题,提出了一种基于模糊C均值聚类( FCM)和BP神经网络相结合的遥感影像自动分类算法. 首先利用FCM对影像进行初始聚类,然后根据聚类结果,由该算法自动选取其中的纯净像元作为训练样本,并送入BP网络进行学习,用最终训练得到的BP神经网络分类器对TM遥感影像进行分类,实验结果表明该算法具有较高的分类精度,能够满足大尺度地物类别判定的需要.%As for the problems that low classification accuracy of non-supervise classification algorithm and training sample of super-vise classification algorithm needs manual selection which is easy to be made wrongly, there is an automatic classfication algorithm of remote sensing image which is based on the combination of FCM and BP neural network. First, this paper uses FCM to make initial clusters of images. Then in accordance with the results of clusters, this paper picks out the endmembers which are automatically select-ed by the algorithm as the traaning samples, sends the samples to study in BP network and uses the BP neural network classifier which is got from the final training to classify the TM remote sensing images. The result shows that the algorithm owns high accuracy which could meet the requirements of determination of object types in a large scale.
Institute of Scientific and Technical Information of China (English)
戚海永
2013-01-01
利用正交试验法对输送带速度、喷气压力和分选次数等因素对煤矸分选效果进行了试验研究,并利用非线性PSO-BP神经网络对分选效果进行了预测.试验结果表明,分矸率与混矸率呈现相反的趋势,第3组试验,即带速为0.11 m/s,喷气压力为0.7 MPa,分选次数为3的时候,分选效果最好,混矸率最低；非线性PSO-BP神经网络预测效果与传统PSO-BP神经网络相比,具有更高的准确性,与试验值更加吻合,对煤矸分选预测具有更高的参考价值.%The influences of velocity of conveyance belt,air jet pressure and separation number on coal and gangue separation effects were studied by orthogonal experiments,and nonlinear PSO-BP neutral network was applied to predict the separation effects.The experimental result show:the gangue separation ratio and the gangue mixture ratio had the opposite variation trend; in the third group of experiment,while the belt velocity being 0.11 m/s,air jet pressure being 0.7 MPa and separation number being three,the gangue separation effects were the best and the gangue mixture ratio was the lowest; contrasted with traditional PSO-BP neural network,the nonlinear PSO-BP neural network had higher prediction accuracy that was much closer to the experimental value,which offered references for prediction of coal and gangue separation effects.
Institute of Scientific and Technical Information of China (English)
李勤朴; 何立夫
2015-01-01
针对传统BP神经网络入侵检测算法学习效率低, 收敛速度慢的缺点, 本文提出了一种改进型的BP神经网络入侵检测算法, 并进行了系统原型的设计. 实验表明,改进型的算法比传统型的算法具有更快的学习速度和更准确的报警率, 结果令人满意.%Aiming at the shortcomings of low learning efficiency and slow convergence speed of intrusion detection algorithm based on traditional BP neural network, this paper presents an improved intrusion detection algorithm based on improved BP neural network, and designs the system prototype. The simulation results show that the improved algorithm has faster learning speed and more accurate alarm rate than the traditional algorithm. And the results of the experiment are satisfactory.
Institute of Scientific and Technical Information of China (English)
张成凤; 徐长生
2012-01-01
For the structure design and calculation of floating crane arm, BP neural network algorithm is used to simulate the relations between the arm frame structure optimization design variables and arms frame structure stress and displacement, and calling the minimize function of MATLAB optimal toolbox to optimized the arm constraint conditions and the objective function. Comparing and analyzing the result after optimization and the values before optimization, the BP neural network optimization algorithm is fully verified.%针对起重船臂架结构设计计算,采用BP神经网络算法模拟出臂架结构优化设计变量与臂架结构应力、位移之间的映射关系,并调用MATLAB优化工具箱中的最小化函数对臂架结构的约束条件以及目标函数进行优化处理,并将优化后的结果与优化之前的数值进行对比分析,充分地验证了BP神经网络优化算法的优越性.
Institute of Scientific and Technical Information of China (English)
陈丽娟; 余隋怀; 初建杰; 卢慧颖
2011-01-01
在分析了影响维护人员素质的因素后提出了基于BP神经网络对维护人员综合素质进行评估的方法.利用BP神经网络建立了维护人员综合素质的评价模型,并通过使用改进算法进行了训练.训练测试的结果表明,该方法具有较高的可行性和有效性,为全面评估维护人员的素质提供了一种方法.%The factors that affect the equipment maintenance personnel are comprehensively analyzed, and a method is proposed to evaluate comprehensive quality of equipment maintenance personnel based on BP neural network.Evaluation model of equipment maintenance personnel is established by using BP neural network, and the improved algorithm is used for training.Training test results show that this method has a high feasibility and effectiveness, and provides a way to assess the quality of equipment maintenance personnel comprehensively.
PSO算法与BP神经网络在电力系统辨识中的比较研究%Comparison of PSO and BP Neural Network in Power System Identification
Institute of Scientific and Technical Information of China (English)
张鹏
2012-01-01
System identification is one of important issues in control engineering research field, BP neural network and particle swarm algorithm, to power systems with STATCOM identification object are first analysed. Respectively BP neural network and PSO accomplish its system identification, analysis and compare convergence precision of two algorithms are used. The results show that the PSO algorithm has advantages in system identification.%系统辨识是控制工程领域中研究的重要问题之一.首先对BP神经网络和微粒群算法进行了深入分析.以含STATCOM电力系统为辨识对象,分别采用BP神经网络和微粒群算法对其进行辩识分析.对两种算法的收敛精度进行了分析比较.结果表明PSO算法在系统辨识上具有优势.
Institute of Scientific and Technical Information of China (English)
蒋兴恒; 朱素蓉
2011-01-01
A BP neural network improved by Levenberg-Marquardt algorithm was illustrated, established and applied to forecast cigarette sales to overcomie disadvantages of general time series analysis. Cigarette sales data were normalized and repeated training and simulating on the model with Matlab software. Compared with real sales data, the forecast of the improved BP neural network is proved accurate.%针对一般时间序列分析方法中预测方法的不足,采用改进的BP神经网络对卷烟销量进行预测.介绍说明改进的BP神经网络Levenberg- Marquardt算法原理,对卷烟销量数据进行归一化处理,建立卷烟销量神经网络预测模型,利用Matlab软件对数据进行训练、仿真.与实际销量进行对比分析,证明采用改进的BP神经网络预测结果准确.
Institute of Scientific and Technical Information of China (English)
赵晨飞; 韩卿; 兀旦晖
2012-01-01
计算机配色可提高油墨配色的速度和精度,具有重要作用.基于两种颜色空间和BP神经网络的计算机配色系统被研究,实验结果表明基于光谱颜色空间配色系统的配色精度更高,可应用于目前的各种印刷方式中,基于BP神经网络的配色系统具有很强的适用性,推进了印刷的数字化流程.%Computer color matching can improve the ink s color matching speed and accuracy, which plays an important role. In this paper the two kinds of computer color matching system based on two color spaces and BP neural network are disscussed. The experimental results show that the color matching system based on the spectral color space is of higher accuracy which can be applied to the current various printing way. The color matching system based on BP neural network has strong applicability, can improve the printing's digital workflow.
Institute of Scientific and Technical Information of China (English)
熊辉霞; 张耀庭; 苗雨
2011-01-01
为了进一步研究体外预应力混凝土结构体外索极限应力的确定方法,在分析影响体外预应力混凝土结构体外索极限应力因素的基础上,运用神经网络预测的原理,采用误差反向传播神经网络即BP神经网络对体外索的索力进行了预测.预测结果表明,应用BP神经网络模型能准确的预测体外索的索力.预测结果和试验结果相比误差在10%以内,可以满足实际工程的需要.%In order to determine the maximum stress of the externally prestressed concrete structures, various factors that influence the stress were analyzed, and the theory of BP (Back Propagation) neural network was used to predict the external tendon force. The results show that the BP neural network model can predict the limit of the external tendon force and the error of the results is less than 10％ compared to the experiment results, which is good enough to satisfy practical engineering needs.
Institute of Scientific and Technical Information of China (English)
席绪奇; 姚志成; 何志昆; 赵曦晶
2013-01-01
在对开环干涉型光纤陀螺仪大量实验数据分析的基础上，分别对光纤陀螺仪零位温度漂移建立BP神经网络温度补偿，标度因数与温度、输入角速率建立多项式模型。利用建立的模型对实验数据进行温度补偿。补偿结果表明，BP神经网络补偿效果优于多项式模型，零偏和零偏稳定性减小了一个数量级，补偿效果明显。%Based on analysing lots of experimental data of open-loop interference type fibre optic gyro (FOG), we set up the BP neural network temperature compensation for zero temperature drift of FOG and the polynomial model for scale factor , temperature and input angular rate respectively .The established model is used to make temperature compensation on experimental data .The results of compensation show that, BP neural network is better than polynomial model in compensation effect , the zero-bias and the stability of zero-bias is reduced by an order of magnitude , the compensation effect is noticeable .
Institute of Scientific and Technical Information of China (English)
李文峰
2013-01-01
通过统计河南省许昌小麦白粉病发生发展的气象生理指标及历年该病发生的资料，构建了小麦白粉病发病的气象预报模型。 BP神经网络小麦白粉病发病趋势预测模型应用了BP人工神经网络的函数映射能力并采用检验函数，拟合精度和预报精度都较高，经过对比优于多元线性回归模型，能很好地实现预期效果，对许昌小麦白粉病发病的预测预防工作具有一定的现实指导意义。%Using the physiological indicators and meteorological statistics of wheat powdery mildew over the years in Xuchang,Henan,the incidence of powdery mildew weather forecast model applied BP artificial neural network function mapping capability and test function. The fitting accuracy and forecast accuracy of BP neural network trend forecasting model for wheat powdery mildew is higher than the multiple linear regression model,which can well realize the desired effect. The study has a certain practical significance on Xuchang wheat powdery mildew disease prediction and prevention work.
基于改进的小波-BP神经网络的风速和风电功率预测%Wind speed and power prediction based on improved wavelet-BP neural network
Institute of Scientific and Technical Information of China (English)
肖迁; 李文华; 李志刚; 刘金龙; 刘会巧
2014-01-01
为了提高超短期风电功率预测精度，使用改进的小波-BP神经网络方法进行研究。针对预测模型普遍存在的延时问题，先通过离散小波变换将信号分解为高低频段的信号，再用遗传算法优化的 BP神经网络分别进行建模，最后求和各层预测信号。由于功率和风速具有混沌特性，用C-C法联合优化重构相空间的参数，以嵌入维数为神经网络输入层节点数。应用于山东某风电场，仿真结果表明，与BP神经网络模型相比，该算法预测风速和功率精度较高，但风速预测值经过实际功率曲线转换后，功率预测精度变差。%In order to improve the forecasting accuracy of ultra-short-term wind power, the improved wavelet-BP neural network method is applied. To solve the widespread delay problems of the prediction model, the original signal is decomposed into high and low frequency signal by the discrete wavelet transform. Moreover, genetic algorithm is used to optimize the BP neural network model separately. Finally, the summation of all the prediction results is gotten. As the wind speed and power series have chaos characteristics, the C-C method is used to optimize parameters of phase space reconstruction and the embedded dimension is taken as the input layer’s node number of neural network. It is applied in a wind farm, in Shandong Province, and the simulation results show that it has higher prediction accuracy than BP neural network model in forecasting wind speed and power. With the conversion of wind speed prediction results by the measured power curve, the power prediction accuracy goes bad.
Institute of Scientific and Technical Information of China (English)
王福斌; 刘杰; 陈至坤; 王静波
2012-01-01
When tracking and identifying the bucket target of excavator robot by using visual information,there exist rotation,translation and zoom situations for the bucket images collected in real time.To improve the identifying ability of the bucket target,a recognition method of bucket target was proposed based on invariant moments and BP neural network.The method extacted seven moment characteristic quantities of bucket image with invariant performance against translation,rotation and zoom.They can act as the training and testing samples for improved BP neural network after being normalized.Using the trained neural network to identify bucket target,the simulation result shows that this method has high recognition ability.%在利用视觉信息跟踪、识别挖掘机器人铲斗目标时,实时采集的铲斗图像存在旋转、平移、缩放等情况.为提高对铲斗目标的识别能力,提出了基于不变矩和神经网络相结合的铲斗目标识别方法.提取铲斗图像对于平移、旋转、缩放具有不变性能的7个不变矩特征向量,归一化后作为改进BP神经网络的训练样本及测试样本.应用训练后的神经网络对铲斗目标进行识别,仿真表明该方法具有较好的识别能力.
基于BP神经网络的我国农民收入预测模型%A Prediction Model of Farmers' Income in China Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭庆春; 何振芳; 李力; 孔令军; 张小永; 寇立群
2011-01-01
According to the related data affecting the farmers' income in China in the years 1978 -2008, a total of 13 indices are selected, such as agricultural population, output value of primary industry, and rural employees. According to standardized method and BP neural network method, the farmers' income and the artificial neural network model are established and analyzed. Results show that the simulation value agrees well with the real value; the neural network model with improved BP algorithm has high prediction accuracy, rapid convergence rate and good generalization ability. Finally, suggestions are put forward to increase the farmers' income, such as promoting the process of urbanization , developing small and medium - sized enterprises in rural areas, encouraging intensive operation, and strengthening the rural infrastructure and agricultural science and technology input.%依据1978～2008年影响我国农民收入因素的相关数据,选取从事农业的人口、第一产业产值、乡村就业人员数等13个指标,依据标准化方法和BP神经网络方法,建立了关于农民收入的人工神经网络模型,并进行具体分析.结果表明,模拟值与真实值吻合较好,改进BP算法的神经网络模型预测精度高,收敛速度快,具有良好的泛化能力.在此基础上,提出了增加农民收入的建议:一是推进城镇化进程;二是发展农村中小企业;三是鼓励集约经营;四是加强农村基础设施建设和农业科技投入.
基于BP神经网络的金刚石涂层钻头失效在线检测%Based on BP Neural Network of Diamond Coating Drill Failure On-line Detection
Institute of Scientific and Technical Information of China (English)
彭广盼; 傅蔡安
2013-01-01
In order to find the on-line detection methods of diamond coated drill failure in the drilling process, it studied the form of diamond coated drill failure; Then research the drilling torque characteristics when the drill failure occurs. Based on the torque characteristics, we selected five parameters as neural network input, such as torque slope. We use Kistler 9277 A5 force dynamometer to detect the torque during the drilling process, with the BP neural network calculations and get the drilling drill failure model. The verification results show that the BP neural network-based detection method can effectively detect the diamond coated drill failure in the process of drilling.%为了找到金刚石涂层钻头在钻削过程中钻头失效的在线检测方法,本文对钻头失效的形式进行了分析,然后对钻头失效发生时钻削扭矩的特性进行了研究.采用Kistler 9277A5测力机来检测钻削过程的扭矩值；在分析了扭矩特性的基础上,选取扭矩斜率等5个参数作为神经网络的输入；然后通过BP神经网络的建模,得出钻削过程中钻头的失效模型.验证结果表明,本文提出的基于BP神经网络的检测方法可以有效的检测金刚石涂层钻头钻削加工过程中的失效情况.
Institute of Scientific and Technical Information of China (English)
刘继清; 徐明
2011-01-01
In view of the weakness of current intrusion detection system, a new intrusion detection system model based on the combination of KPCA technology and BP Neural Network is put forward. Against the high dimensions problem of complicated network data, KPCA technology as a method of characteristics extraction is used to decrease the dimensions and simplifie the size of neutral network and reduces the operations work. A large a-mount of experiments with KDD99 dataset have been conducted and the results show that the new system is with higher adaptable ability and higher speed detection rate in nowadays complicated network circumstances than the intrusion detection system only uses BP neural network.%针对当前入侵检测系统的弱点,将KPCA技术和BP神经网络相结合,提出了一种多核入侵检测分类系统的设想.该系统针对一些复杂网络数据维数较高的特点,引入核主成分分析技术对其进行降维处理,从而简化了神经网络规模,降低了神经网络的运算量.通过对KDD99数据集进行仿真实验表明,与仅使用BP神经网络的入侵检测系统相比,该系统具有很强的泛化能力和较高的检测效率.
Layered learning of soccer robot based on artificial neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.
Application of BP Neural Network in Distortion Correction of Large FOV Display%BP神经网络用于大视场显示设备的畸变校正
Institute of Scientific and Technical Information of China (English)
田立坤; 刘晓宏; 李洁
2012-01-01
Geometric distortion may appear in large Field-of-View ( FOV) electro-optic image display equipment, which is caused by the optical system. To improve image distortion correction effect and overcome the limitations of the traditional BP algorithm, such as local minimum and slow convergence speed, Levenberg-Marquardt algorithm based on optimizing theory was used. Then the distortion correction method based on BP neural network containing two hidden layers was proposed, which could achieve high precision of mapping between distortion image and original image self-adaptively without knowing the mathematic model. The algorithms were analyzed and compared in depth in the Matlab platform. The simulation result shows that the BP neural network algorithm with double hidden layers can be realized easily, achieve high precision, and has good data processing ability. Compared with the distortion correction model based on polynomial fitting method, all the precision indexes of the distortion correction model based on BP neural network with double hidden layers are improved observably.%大视场光电成像显示设备中会出现光学系统引起的图像几何畸变现象.为了提高显示设备畸变校正效果,并克服传统BP算法存在局部极小点、收敛速度慢等缺点,采用了基于优化理论的LM算法来改进传统BP神经网络算法.提出一种含有双层隐含层的BP神经网络畸变校正方法,可在不确知畸变数学模型情况下,实现自适应地建立畸变图像与原始图像之间的高精度映射关系.在Matlab平台上进行算法的深入分析和比较.仿真结果表明,双隐含层BP神经网络算法易于实现、数据处理能力强、校正精度高.与多项式拟合方法的畸变校正模型相比,基于双隐含层BP神经网络算法的畸变校正模型的各项精度指标提升显著.
Institute of Scientific and Technical Information of China (English)
李净; 冯姣姣; 王卫东; 张福存
2016-01-01
(Levenberg-Marquardt) algorithm is used to optimize the BP neural network (LM-BP neural network is abbre-viation of BP neural network for the optimization). This article simulates solar radiation using LM-BP neural network, H-S and A-P climate models at Urumqi, Kashi, Hami, Xining and Guyuan radiation stations and uses MPE, MBE and RMSE indexes of accuracy assessment to test the three models. The results indicate that LM-BP neural network has the highest accuracy in model simulations, showing satisfactory performance com-pared with the simulation results of traditional two climate models, simulated and observed values of fitting de-gree model is superior to H-S and A-P climate models. So we selects the LM-BP neural network model to simu-late solar radiation in Northwest China. Basing on the meteorological data from 159 weather stations in North-west, we apply the BP neural network optimized LM (Levenberg-Marquardt) algorithm to simulate the total month solar radiation during 1990-2012 in these meteorological observation stations. Then the solar radiation value of the 159 weather stations and the measured radiation data of the 25 radiation observation station to ob-tain the spatial-temporal distribution of annual average solar radiation by interpolation, and analyzes. These re-sults indicate that average annual total radiation in 1990-2012 in Northwestern ranges from 262 MJ/m2 to 643 MJ/m2, presenting the distribution pattern of high in the middle, low on both end. LM neural network is a prom-ising method for solar radiation simulation, which can be used in the simulation of solar radiation in the area of no radiation observation.
Institute of Scientific and Technical Information of China (English)
简晓春; 王利伟; 闵峰
2012-01-01
为实现对汽车排放污染物CO的实时检测，提出采用神经网络软测量技术，以BP神经网络基本原理为基础，引入LM优化算法。选用发动机运转参数中的转速和节气门开度为变量，建立面向LMBP神经网络的汽车排放污染物CO的检测模型，并对该神经网络进行学习训练和模拟验证。结果表明：该方法可行、有效，仿真结果非常接近实测数据，且LMBP算法收敛速度快、预测精度高。同时，也可将该神经网络模型应用于CO的实时控制中，提高控制的实时性和精度。%To realize real-time detection of the CO emission of Vehicle, the article put forward using neural network soft measurement, based on BP neural network basic principle, and brought in LM optimization algorithm. The engine running parameters： the rotation speed and the throttle percentage were chosen as variables; LMBP neural network detection model for the CO emission of Vehicle was estabished, and the neural network was trained and simulated. The results showed that this method was feasible and effective. The simulation results were very close to the measured data, and the convergence speed and the forecast precision of LMBP algorithm was high. In the meantime, it could also be applied in the real-time control of CO,which improved the instantaneity of control and the precision.
Research and Application of Rough Set-BP Neural Network Based on MEA%基于MEA的粗糙集神经网络研究及应用
Institute of Scientific and Technical Information of China (English)
高金兰; 高骞
2011-01-01
将思维进化算法、粗糙集和神经网络相结合,提出一种基于MEA的粗糙集神经网络,用于变压器故障诊断.此模型采用思维进化算法全局寻优的特点,搜索粗糙集属性约简离散断点的位置以及神经网络的连接权值和阈值,避免了常规粗糙集属性约简时复杂的手工试凑以及BP神经网络收敛速度慢、精度不高等缺点,有利于更快地收敛于全局最优解,提高系统的诊断速度和准确率.仿真结果表明了方法的有效性.%The mind evolutionary algorithm is combined, the rough set and the neural network, and a rough set-neural network based on MEA is proposed applying in transformer fault diagnosis. This model uses global optimization characteristics of the mind evolutionary algorithm to search rough set discrete breakpoints and neural network connection weights and thresholds, it avoids the conventional rough set complex handwork reduction and slow convergence and low precision of BP neural network, and benefits to find the global optimal solution quickly and improves the diagnostic speed and accuracy. Simulation experiment verifies the validity of this method.
Soil Suitability Evaluation Based on BP Neural Network%基于BP神经网络的土壤适宜性评价——以溪洛渡水电站嘎勒移民安置区为例
Institute of Scientific and Technical Information of China (English)
陈琨; 赵小蓉; 王昌全; 黄萍萍; 赵燮京
2009-01-01
人工神经网络具有大规模并行处理、分布式储存、自适应性、容错性等特点,可以解决复杂的非线性问题.本文将BP人工神经网络应用到溪洛渡水电站嘎勒移民安置区土壤适宜性评价中,构建了影响土壤适宜性的评价因子训练集,对隐层神经元数量的选择、训练过程的建立等问题进行了探讨.通过MATLAB神经网络工具箱对专家样本的学习,建立具有泛化能力的土壤适宜性评价BP神经网络模型,确定网络模型结构为9-7-1,均方误差为0.00033,并对预测地块进行评价,得出评价区域以中等适宜性的土壤为主的结果.%Artificial neural network with the characteristics of massively parallel processing,distribuion storage,self-adaptive,fault tolerance and etc.,could be used to solve complex nonlinear problems.Therefore,BP artificial neural network was applied for soil suitability assessment,the impact on soil suitability to build evaluation factors,the training set,and the number of hidden layer neurons in the choice of the establishment of the training process and other issues were discussed. Through the neural network study of samples,the generalization ability of neural network model was established to evaluate the prediction block obtained in line with the actual results of the evaluation.
Institute of Scientific and Technical Information of China (English)
Xiao-rui Wang; Yuan-han Wang; Xiao-feng Jia
2009-01-01
Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and, mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively unproved the recommended values in the original prospecting data. This is of
Institute of Scientific and Technical Information of China (English)
方石; 张坚; 陈朝宏
2011-01-01
Taking aim at the low SNR in detecting magnetic signal of ship, a detection algorithm based on wavelet transform and BP neural network was proposed. Based on the characteristic of magnetic signal of ship, firstly the signal was decomposed by wavelet transform, and the low frequency components in last level were taken out to filter out the high frequency noise. Then the low frequency components were processed by BP neural network to pick up characteristic signal of ship target. The results of experiment by ship model showed that the algorithm increases SNR markedly, and enhances the detection ability of magnetic signal of ship.%针对水中兵器探测舰船磁场信号时信噪比较低的问题,提出了一种小波变换结合反向传播(backpropagation,BP)神经网络的检测方法.根据舰船磁场信号的时频特征,首先对信号进行小波分解,提取最后一层的低频分量,滤除高频噪声;再采用BP神经网络对低频分量进行学习,提取舰船目标特征信号.将此算法应用于船模实测实验,结果表明,该算法可以显著提高信噪比,增强了对舰船磁场信号的检测能力.
Institute of Scientific and Technical Information of China (English)
肖恭伟; 刘国林; 曹淑敏; 孙志阳
2016-01-01
According to the recursion in different section of time,and the monitoring data of wa-ter level in three hydrological sites including the east reach of Jianxi,Shuiji,and Jianyang,we find that the optimal node number of hidden layer of BP neural network is 10 through complex calculation,and establish a mathematical model to forecast the water level in Qilij ie Station using the BP neural network.On this basis,we can amend the forecast results with recursion in differ-ent section of time.The caculation results show that this method improves the forecast accuracy. It can be pointed out through calculation that the correction method of recursion in different sec-tion of time is a better choice due to the coincidence between results and facts.%在分析预报误差的时间分段递推修正方法的基础上,以建溪流域东游、水吉、建阳三个水文站点的水位监测数据为基础,计算得到BP神经网络隐含层最优节点数目为10,建立了 BP 神经网络对七里街测站水位预报的数学模型。在此基础上,利用时间分段递推修正方法对预报的结果进行修正,计算结果表明,时间分段递推修正方法使得预报精度提高很多,其结果与实际更加符合。
Institute of Scientific and Technical Information of China (English)
马少华; 曹三民
2011-01-01
Fingerprint identification technology is one of the most widely used biological recognition technology today. In the process of fingerprint identification, image processing, feature extraction, matching process have large amounts of data to be addressed and the calculation is also very troublesome. The BP neural network has good self-learning ability, strong classification ability and fault tolerance and it is feasible to be used in fingerprint identification. Meanwhile, the BP neural network also contains some problems such as slow computing speed, the gradient descent method can＇t deal with non-differential transfer function. This paper adopts the particle swarm optimization to optimize BP algorithm and improves the speed and accuracy of fingerprint recognition.%指纹识别技术是当今应用最广泛的生物识别技术之一。在指纹识别过程中，图像处理、特征提取、匹配等过程数据量庞大，计算比较烦琐。BP神经网络具有良好的自学习能力、强大的分类能力和容错能力，将其应用到指纹识别中是可行的。为改进BP神经网络计算速度较慢，梯度下降法不能处理一些不可微传递函数的问题，采用粒子群算法对BP算法进行优化，提高了指纹识别的速度和准确度。
基于BP神经网络的变压器内部故障保护%Internal fault protection based on BP neural network transformer
Institute of Scientific and Technical Information of China (English)
苏美玲; 邹晓松; 何杰
2016-01-01
本文研究了基于BP神经网络方法的变压器内部故障保护。运用MATLAB/SUMILINK对变压器励磁涌流、励磁涌流与故障电流的差异进行了数字仿真。利用MATLAB的人工神经网络工具箱，建立了BP神经网络模型，对励磁涌流和故障电流的样本进行训练及测试并对训练好的网络进行验证。表明BP神经网络可以较为正确地区分励磁涌流和故障电流，用于变压器内部故障保护。%This paper discussed a transformer protection based on Back- Propagation Network .Digital simulation were made on the inrush current of the transformer and on the comparison between the inrush current and the fault current of the transformer. Back- Propagation Network model was set up by using the MATLAB artificial neural network toolbox. The results show that the Back- Propagation Network almost can correctly distinguish between excitation inrush current and the fault current of the transformer.
DEFF Research Database (Denmark)
Krogh, Anders Stærmose; Riis, Søren Kamaric
1999-01-01
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...
Institute of Scientific and Technical Information of China (English)
李文琦; 黄水平; 李海朋
2014-01-01
目的：比较BP神经网络模型和Cox比例风险模型在生存分析中的预测性能，进一步探讨BP神经网络模型在生存分析中的应用。方法：采用Monte Carlo 模拟数据集，如不同样本量、不同删失比例、不同协变量间关系及是否满足等比例风险假定的理论研究和胃癌根治术患者预后预测的实例分析，分别建立BP神经网络模型和Cox比例风险模型，最终使用一致性指数C对其预测性能进行比较。结果：当样本量为100、删失比例为60％、80％及样本量为300、删失比例为80％时，BP神经网络模型的预测性能高于Cox比例风险模型（P＜0．05）。协变量不满足等比例风险假定、协变量间存在三维交互作用和非线性关系时，BP神经网络模型的预测性能较Cox比例风险模型好（P＜0．05）。实例研究中发现，BP神经网络模型预测的一致性指数C（0．835）高于Cox比例风险模型（t配对＝4．311，P＜0．001）。结论：BP神经网络模型在生存分析的应用中对样本删失比例、是否满足PH假定、协变量间复杂交互作用和非线性关系具有非特异性，对资料限制较少，且预测一致性高，值得在生存分析中进一步推广应用。%Aim:To compare their prediction performance of BP neural network model and Cox proportion hazard mod -el in survival analysis and to explore the superiority of BP neural network model in survival analysis .Methods: Monte Carlo was used to generate the data sets under the condition of different sample size , different degree of censoring , number of variable and interactions , non-linear effect , distinct distribution of covariate and proportional vs non-proportional hazard . Then BP neural network model and Cox model were built , and their prediction performance was compared using concord-ance index C.Results:In the research on simulation data sets , when the sample size of 100, proportion of censoring of
Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks
Institute of Scientific and Technical Information of China (English)
费翔; 何小燕; 罗军舟; 吴介一; 顾冠群
2000-01-01
Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.
Institute of Scientific and Technical Information of China (English)
黄丽红; 王小川; 陈仁杰
2013-01-01
[Objective] To build a water quality evaluation model based on back propagation (BP) neural network for better water hygiene management of the swimming pools in Shanghai.[Methods] Based on the exports' proposed standards of water quality grading for swimming pools,random samples were selected.A BP neural network was adopted for training and modeling.The established model was then applied to rapid evaluation of water quality grade of swimming pools in Changning District,Shanghai.[Results] With the help of BP neural network,the prediction accuracy of the water quality evaluation model was up to 95.2％ for the training data.Among the water samples from swimming pools in Changning District in 2009,no severe water.pollution was found and more than half (54.44％) of the water samples were at general grade.There were more lightly polluted samples from the community clubs than from the sports institutes and the star-rated hotels.The quality of samples taken from sports institutes was higher than those from the star-rated hotels and the community clubs.[Conclusion] The accuracy of the BP neural network model is high for water quality evaluation.Except for the water sampled from the community clubs' swimming pools,the quality of water in swimming pools of Shanghai Changning District is at a general acceptable level.%[目的]应用反向传播(back-propagation,BP)神经网络模型,构建游泳场所水质综合评价模型,为加强游泳场所水质的卫生管理和保障游泳者的身体健康提供相关依据. [方法]运用专家评价的游泳场所水质分级标准并根据随机数产生样本,使用BP神经网络进行训练与建模.将建立好的BP神经网络模型用于上海市长宁区游泳场所快速水质等级判断. [结果]构建的基于BP神经网络的游泳场所水质综合评价模型对训练数据的预测准确率达到95.2％.2009年上海市长宁区游泳场所水样中,无重度污染水样.水质一般的
Institute of Scientific and Technical Information of China (English)
马草原; 孙富华; 朱蓓蓓; 尹志超
2015-01-01
For current tracking control problems in active power filter (APF), a BP neural network adaptive PI controller based on improved gradient algorithm is designed. It combines the neural network technology with PI controller structure. Compared with the traditional PI controller, it has a simple structure, and easy to on-line adjustment. Meanwhile, in order to overcome the local minimum and slow convergence problem when using neural network algorithm to weight correction coefficient, the gradient algorithm is improved and the algebraic method instead of gradient descent method is used to solve the problem of the local minimum arise, and makes convergence faster. Simulation experiments show that the improved adaptive neural network PI controller has faster response and higher compensation accuracy, thus to make the system more stable, and the harmonic distortion of grid current is lower.%针对有源电力滤波器的电流跟踪控制问题，设计了一种基于改进梯度算法的BP神经网络自适应PI控制器。该控制器将神经网络技术与PI参数设计相结合，与传统的PI控制器相比，该控制器具有结构简单、易于在线调整等优点。同时，为了克服采用神经网络算法修正权值系数时，会存在局部极小、收敛速度慢的问题，对 BP 神经网络采用的梯度算法进行改进。利用代数法代替梯度下降法，从而解决了易出现局部极小问题，且使收敛速度更快。仿真实验表明，改进后的神经网络自适应PI控制器较传统的PI控制器有更快的响应速度和更高的补偿精度，从而使系统更稳定，而且电网电流的谐波畸变率更低。
Institute of Scientific and Technical Information of China (English)
袁铸; 申一歌
2017-01-01
In the trajectory optimization of precision agriculture robot , taking automatic control as the goal , it introduced the optimization algorithm combined with BP neural network and the computed torque method of automatic controller , which intended to reduce motion errors during the work and improve the work efficiency .In this paper , it first established mathematical model of agricultural robot , kinematics and dynamics analysis;then, it designed the agricultural robot mo-tion control system by using BP neural network to uncertain dynamics factors to judge , and put forward the solution to the factor of adaptive learning method .Finally the system used MATLAB simulation .Experimental result shows that the com-bined with BP neural network and the computed torque method of automatic controller , which can effectively optimize the robot motion path , and improve the overall operation efficiency of the robot , the system is stable and reliable , and the ex-ternal environment interference factors with strong adaptive ability to learn .%以农业机器人精密轨迹优化自动控制为目标，在优化算法中引入BP神经网络与计算力矩法结合的自动控制器，旨在减少作业过程中的运动误差，提高其工作效率。首先，建立农业机器人数学模型，分析其运动学和动力学原理；然后，设计了农业机器人运动控制系统，引入BP 神经网络对不确定动力学因素进行判断，并提出解决该因素的自适应学习法；最后，对该系统运用 MatLab 进行了仿真。试验表明：以 BP 神经网络与计算力矩法结合的自动控制器可以有效优化机器人运动路径，提高机器人整体作业效率，系统运行稳定、可靠性强，且对外部环境的干扰因素具有较强的自适应学习能力。
Institute of Scientific and Technical Information of China (English)
熊伟晴; 燕晓波; 姜守旭; 李治军
2015-01-01
For the research and practice of modern intelligent transportation systems, short-term traffic flow prediction is an essential element. The main content of this paper is to establish a traffic prediction model for short-term traffic flow forecasting , using a rule-based fuzzy system, nonlinearly combine traffic flow forecasts resulting from an adaptive Kalman filter ( KF) and BP neural network model, which is referred as KBF model . Organic combination of traditional methods and artificial intelligence methods, on one hand, makes use of the powerful dynamic nonlinear mapping ability of artificial neural network, so as to improve the prediction accuracy;On the other hand, takes full advantages of the static linear sta-bility of the Kalman filter to solve the problem that the forecasts recognition rate is not satisfactory and the credibility is not high while using a BP neural network only. Verified by experiments, this model is useful for traffic flow forecasting with high accuracy and high reliability.%本文研究短时交通流预测。短时交通流预测是智能交通系统研究和实践的必要基础。本文提出和建立了一个短时交通流量预测模型，该模型利用一个基于规则的模糊系统，非线性地组合BP 神经网络模型和自适应卡尔曼滤波模型的交通流量预测结果，使得短时交通流量的预测结果更加准确可靠。该模型将传统方法和人工智能方法有机结合，一方面，利用人工神经网络强大的动态非线性映射能力，从而提高预测精度；另一方面，充分发挥卡尔曼滤波的静态线性稳定性，解决了单独使用BP神经网络进行预测时识别率不理想和可信度不高的问题。实验结果表明，本文提出的短时交通流预测模型具有较高的准确度和可靠度。
基于BP神经网络建立的川崎病早期诊断模型%BP Neural Network Model for Early Diagnosis of Kawasaki Disease
Institute of Scientific and Technical Information of China (English)
黄江; 陈剑锋
2011-01-01
In order to diagnose Kawasaki Disease during early phase, clinical symptoms (temperature, rash, conjunctival injec-tion, erythema of thelips, and oral mucosal changes) and laboratory data (white blood cell, neutrophil, platelet, c -reactive protein, and erythrocyte sedimentation rate) of 156 children with Kawasaki disease or infectious diseases were used to develop a BP neural net-work model. 90 random cases were trained using MATLAB software for setting up the BP neural network model. The other 66 cases were analyzed to predict diagnosis of Kawasaki disease using this model. Results showed that the predict accuracy in patients with Ka-wasaki disease and children with infectious diseases were 97. 4% and 92. 9% , respectively. Our result indicates that the BP neural network model is likely to provide an accurate test for early diagnosis of Kawasaki disease.%为早期诊断川崎痛,应用BP神经网络原理建立川崎病的诊断模型.以156例川崎病与非川崎病患者的体温、皮疹、口腔黏膜改变、实验室检查结果等9项指标等作为BP神经网络的输入参数,在MATLAB7程序中对其中随机抽取的90例学习样本进行训练并建模.以剩余的66例作为测试样本进行预测,结果表明该模型对川崎病和非川崎病的预测准确率分别为97.4％、92.9％,提示此模型可有效地判别出川崎病与非川崎病,可用于川崎病的早期辅助诊断.
Critical Branching Neural Networks
Kello, Christopher T.
2013-01-01
It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…
Institute of Scientific and Technical Information of China (English)
戴洪磊; 韩李涛; 陈传法
2012-01-01
矿山瓦斯突出与爆炸事故的预测预报是当前我国煤矿安全生产中急待解决的问题之一。引入BP神经网络的拟牛顿（Newton）优化算法,在保留空间实体相关和多种分布并存的前提下,讨论了建立拟牛顿优化算法BP神经网络瓦斯灾害预测预报模型的数学模型设计、网络结构设计和程序设计3个部分,并以济宁二号井为实例进行了测试。结果表明：该模型稳定、快速、预测精度高,能够较好地模拟矿山瓦斯突出与爆炸事故特征,对瓦斯灾害作出较准确的预测。%The forecasting of gas outburst and explosion accidents is one of the most pressing problem in current China's coal mine safety production.Introducing the Quasi-Newton optimization algorithm of BP neural network,this paper discusses the mathematical model,network architecture and programming design of establishing the gas disaster forecasting model of Quasi-Newton optimization algorithm BP neural network under the premise of keeping the relationship among the spatial entities and their distributions,and an instance of Jining No.2 coal mine is tested.The result shows that this model is stable,fast and high prediction accuracy,which can simulate the mine gas outburst and explosion accidents characteristics and make more accurate predictions on the gas disaster.
Institute of Scientific and Technical Information of China (English)
李俊峰; 汪月乐; 胡升; 何慧灵
2016-01-01
The stellar classification was an important research field for understanding the formation and evolution of stars and galaxies.With large sky surveys and its massive data,the speed and accuracy of the celestial automatic classification was very important.The depth confidence neural network (DBN),support vector machines (SVM)and BP neural networks used in the star classification were compared in this paper.And the applicability of star classification with these three methods was analyzed. First,K,F stars are classified according to the depth of confidence neural network and BP neural network and support vector machine.Then the K1,K3,K5 sub-type and F2,F5,F9 sub-type were separately identified.Finally,the data which did not be-long to the k sub-type were excluded by a secondary classification model based on SVM support vector machine .The results shows that:the depth of belief networks is better for K,F-type star classification,but it is poor for K,F sub-type classification results;The recognition rate of SVM is high for the K,F-type stars and the classification effects of this method is better for K, F-type stars than the corresponding sub-type stars by comparison;The recognition rate of BP neural network is ordinary general for K,F-type stars and their sub-types.The experiment showed that the accuracy of excluding non-k-sub-type data can be up to 100% which indicates that the unknown spectral data can be screened and classified with SVM.%恒星的分类对了解恒星和星系形成与演化历史具有重要的研究价值。面对大型巡天计划及由此产生的海量数据,如何迅速准确地将天体自动分类显得尤为重要。通过对SDSS DR9的恒星光谱数据进行深度置信神经网络(DBN)、神经网络和支持向量机(SVM)等算法分类的对比,分析三种自动光谱分类方法在恒星分类上的适用性。首先利用上述三种方法对K,F恒星进行识别分类,然后再分别对 K1,K3和 K5次型和F2,F5,F9次型识别,
Term Structure of Interest Rates Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.
Institute of Scientific and Technical Information of China (English)
罗广恩; 崔维成
2012-01-01
Artificial neural network is an important method for predicting the fatigue crack growth rate. In this paper, the Bayesian regularized BP neural network is established to predict the fatigue crack growth rate of metal.The experimental data of each material at different stress ratio R are divided into two parts. One is used for training neural network, the other is used for testing the network. Experimental data of four different types of materials taken from literature were used in the analyses. The results show that the neural network has strong fitting and generalization capability. And the generalization capability of neural network is improved by reducing the training data near the threshold.So the neural network can be used for predicting the crack growth rate of different stress ratios R based on the existing data. Furthermore, it will provide a reliable and useful predictor for fatigue crack growth rate of different metals.%人工神经网络是进行预报裂纹扩展率的一个重要方法.文章针对不同金属的疲劳裂纹扩展速率分别建立贝叶斯正则化BP( Back Propagation)神经网络,将各材料在不同应力比R下的疲劳裂纹扩展速率试验数据分为两部分,一部分用来进行训练网络,另一部分用来测试训练好的网络,检验其泛化能力.将从文献中获取的4种不同金属材料的疲劳试验数据作为算例,来检验网络的性能.计算结果表明贝叶斯正则化BP神经网络不仅对训练样本有很好的拟合能力,而且对于未训练过的测试样本也有较好的预测能力,即有较强的泛化能力.同时,指出了建立网络时减少门槛值附近的试验样本点,可以提高网络的预测能力.研究结果表明,该方法可以方便地获得不同应力比R下的疲劳裂纹扩展速率,从而达到减少试验次数,充分利用已有数据的目的.并且可以进一步应用于其他金属的疲劳裂纹扩展速率的预报.
Neural networks and graph theory
Institute of Scientific and Technical Information of China (English)
许进; 保铮
2002-01-01
The relationships between artificial neural networks and graph theory are considered in detail. The applications of artificial neural networks to many difficult problems of graph theory, especially NP-complete problems, and the applications of graph theory to artificial neural networks are discussed. For example graph theory is used to study the pattern classification problem on the discrete type feedforward neural networks, and the stability analysis of feedback artificial neural networks etc.
Decoupling Control Method Based on Neural Network for Missiles
Institute of Scientific and Technical Information of China (English)
ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao
2005-01-01
In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.
Institute of Scientific and Technical Information of China (English)
陈远方; 张熳; 王小莉; 戎毅; 彭海燕; 管芳
2015-01-01
目的：探讨适合全国乙肝发病率的预测模型，为乙肝预测预警系统提供参考。方法应用2004－2012年全国乙肝月发病率数据，分别建立 ARIMA 模型和 BP 神经网络模型，利用建立的模型预测2013年1－12月乙肝发病率，采用实际发病率验证与比较两种模型的预测效果，评价指标为平均绝对误差（MAE）、平均绝对误差率（MER）和非线性相关系数（RNL）。结果全国2004－2013年乙肝月发病率在2.79／10万～9.44／10万间波动，序列具有明显的长期趋势。建立的乘积 ARIMA（0，1，1）（0，1，1）12模型预测的 MAE、MER、RNL 分别为0.445、0.065、0.909，BP 神经网络模型分别为0.635、0.093、0.872。ARIMA 模型预测的平均绝对误差和平均绝对误差率要低于 BP 神经网络模型（△MAE＝0.190，△MER＝0.028），非线性相关系数要高于BP 神经网络模型（△RNL＝0.037）。结论 ARIMA 模型和BP 神经网络模型均适用于我国乙肝发病率的预测，且前者的预测效能和非线性拟合能力略优于后者。%Objective To explore suitable prediction models for hepatitis B incidence in China;to provide reference for fore-casting warning system of hepatitis B.Methods ARIMA model and Back-Propagation (BP)neural network model were estab-lished based on monthly incidence of hepatitis B from 2004 to 2012.Predication performance of both models were verified by monthly incidence of hepatitis B in 2013.Mean absolute error(MAE),mean error rate(MER)and nonlinear correlation coeffi-cient(RNL)were used to compare prediction effects of above two models.Results The monthly incidence of hepatitis B from 2004 to 2013 were in the range of 2.79/105 -9.44/105 ,demonstrating obvious long-term trends.The MAE,MER,RNL be-tween actual values and predicted values of the monthly incidence of hepatitis B in 2013 using the fitting ARIMA(0,1,1)(0,1, 1)12 model and BP neural network model were 0
Institute of Scientific and Technical Information of China (English)
肖劲飞; 侯媛彬; 王瑞; 王勉华
2015-01-01
Aiming at the problems that the current amplitude and copper consumption of state stator is relatively large in the traditional fixed flux DTC speed control system with double closed-loop, a variable flux DTC speed control system with three closed-loops is put forward in this paper. The simulation results indicate that the torque ripple, current amplitude and copper consumption of SRM are obviously reduced by using this speed control system. Furthermore, aiming at the features that the control parameters of the variable flux DTC speed control system with three closed-loops are complex and the requirement for real time is high, this paper combines BP neural network PID controller and the traditional PI controller has been composited with the consideration of characteristics of this variable flux DTC speed control system, and the BP-PI controller are used as the rate fixer in this paper. The simulation results shows that this composited controllers can overcome the defects of single neural network PID controller with traditional PI controller to establish BP-PI controller. The simulation shows that the BP-PI controller effectively overcomes the defects in single BP network PID controller, has better dynamic and static performance than those of traditional PI controller, and obviously improves the adaptability and robustness of this control system.%文章针对传统定磁链双闭环DTC调速系统稳态时定子电流幅值较大、电机铜耗增加的问题，提出了变磁链给定的解决方案，建立了变磁链三闭环DTC调速系统。仿真结果证明，此调速系统动、静态性能良好，既能充分抑制SRM的转矩脉动，又能在稳态时降低定子电流的幅值，解决了电机高速时铜耗较大的问题。另外，针对变磁链三闭环DTC调速系统控制参数复杂，实时性要求高的特点，文章将BP神经网络PID控制器与传统PI控制器复合，构成了BP-PI控制器。仿真表明，BP-PI控制器有效克
刨花板热压控制系统BP神经网络整定PID控制%Particleboard Hot press Control Based on BP Neural Networks PID Method
Institute of Scientific and Technical Information of China (English)
韩宇光; 曹军; 朱良宽
2011-01-01
According to some phenomena such as non-linear characteristic, the special process requirements of steady control without overshoot, and base on BP neural networks control, self-tuning control and PID control theory, we adopt the BP self-tuning PID strategy to control the slab thickness. The method on the PID parameters by BP optimization implement online self-tuning, which can consummate the performances of PID controller and improve control precision and stabilization of the electro-hydraulic position servo system. It achieves the control characteristics of hot press technological requirements. Furthermore, the simulation results also verify that the BP self-tuning control all can get the non-overshoot, fast and stable response in the two different sets of PID initial parameters circumstances.%针对刨花板热压控制系统中热压控制存在的非线性、纯滞后和时变性等现象,根据BP神经网络PID和常规PID控制的控制思想,提出了BP神经网络PID的控制策略,实现了对PID参数的在线自整定,完善了PID控制器的性能,提高了系统的控制精度.仿真结果表明,与常规PID控制器相比,该控制器明显提高了热压控制系统的动态性能和稳定性.
Green Construction Assessment of Construction Project Based on BP Neural Network%基于BP神经网络的建筑工程绿色施工评价
Institute of Scientific and Technical Information of China (English)
姜虹
2015-01-01
通过对绿色施工理论的探讨，构建了建筑工程绿色施工评价的指标体系。基于BP人工神经网络评价方法构建了建筑工程绿色施工评价模型，并运用该模型对一实际工程项目的绿色施工进行了模拟。%This article discusses the green construction theory and builds a construction project green construction assessment index system. The construction project green construction assessment model is built based on BP artificial neural network, and the model is used to simulate the green construction of a practical project.
基于BP神经网络的企业绿色竞争力评价研究%Research on Evaluation of Green Competitiveness Based on the BP Neural Network
Institute of Scientific and Technical Information of China (English)
韩意; 姚大鹏
2014-01-01
本文在阐明企业绿色竞争力内涵及特征的基础上，构建企业绿色竞争力评价指标体系，给出了基于BP神经网络的企业绿色竞争力评价模型，以期为企业构建绿色竞争力，实现可持续发展提供支持和参考。%The enterprise green competitiveness evaluation index system is constructed based on clarifying the meaning and characteristic of green competitiveness. And then, the evaluation model of green competitiveness based on BP neural network is provided, so as to provide support for enterprise to construct green competitiveness and attain sustainable development.
基于BP神经网络的企业应急物流风险管理%Risk Management for Enterprise Emergency Logistics Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
张杰; 汤齐
2012-01-01
在客观分析企业应急物流风险的前提上,基于MATLAB工具箱--BP神经网络提出有效的评价方法,从应急物流的角度来评价突发事件的风险大小;同时建立了风险预警模型,最后提出对应的风险控制策略,为企业顺利应对突发事件提供行之有效的参考依据.%In this paper, we proposed an effective evaluation method of enterprise emergency logistics risks based on BP neural network, meanwhile established the corresponding risk warning system and finally gave the risk control strategy.
Institute of Scientific and Technical Information of China (English)
张曾莲
2011-01-01
在明确高校科研能力内涵的基础上,分析高校科研能力的构成,提出高校科研能力评价指标.采用BP神经网络对30所高校的科研能力进行评价,测试结果表明,该方法训练速度快,且错误率低.%Based on specific content of university research capacity, the paper analyzeis the composition of university research capacity, and proposes the research capacity evaluation indicators of universities. It uses BP neural network to evaluate the research capacity of 30 universities, the test results show that training speed is not fast bat also in a low error rate.
Institute of Scientific and Technical Information of China (English)
许少华; 陈可为; 梁久祯; 郑生民
2001-01-01
提出了一种基于神经网络与图象处理技术相结合的沉积微相自动识别方法.该方法是先将数字化测井曲线和地层参数预处理转化为二值点阵图象模式，经过点阵数据编码压缩提取和记忆曲线所表征的地层模式特征，然后利用超线性BP算法与遗传算法相结合的方法训练多层前馈神经网络.所得神经网络稳定、学习收敛速度快，同时具有很强的记忆能力和推广能力，此模型对解决沉积微相自动识别问题具有良好的适应性.%We propose an automatic sedimentary facies identification methodbased on combination of neural network with image process technology. First, we translate digital well logging curves and stratum parameters into binary image modes. Second, through contracting binary data codes, we distill and store stratum mode characters token by well logging curves. Last, we combine BP algorithm with genetic algorithm to train a multilayers forward neural network. The neural network has the advantages of being stable, fast learning, awfully memorable and generalized ability. This model is suitable to solve problems of sedimentary facies identification.
Forecast Method of Road Freight Traffic Based on BP Neural Network%基于神经网络算法的公路货运量预测方法
Institute of Scientific and Technical Information of China (English)
王栋
2014-01-01
Shanxi Province was taken as an example for the road freight traffic forecasts by using gray correlation method. The predictors are GDP, the first industry, the secondary industry, industrial added value, per capita GDP ,total fixed asset investment and the total retail sales of social consumer goods. The prediction model of road freight traffic is established on base of BP neural network,and then verified with tests. The results show that road freight traffic can be predicted accurately by the model based on BP neural network,and the maximum error is less than 5 . 3%. It can improve the forecast ability of road freight traffic and provide a method for road freight traffic.%以陕西省为例，运用灰色关联分析法确定公路货运量的影响因素分别为地区生产总值、第一产业增加值、第二产业增加值、工业增加值、人均地区生产总值、全社会固定资产投资和社会消费品零售总额.将所确定的因素作为公路货运量的预测指标，建立基于BP神经网络的公路货运量预测模型，并对模型进行应用测试.结果表明：该模型具有较高的精度，最大误差为5.3%，可以提高公路货运量预测的准确度，为我国公路货运量的预测研究提供方法支撑.
Institute of Scientific and Technical Information of China (English)
王新为; 朱青松; 谭安胜; 张永生
2014-01-01
以舰艇防空作战目标选择决策和规划需求为背景，针对萤火虫算法求解精度不高且收敛速度较慢的问题，提出可动态调整步长的改进萤火虫优化算法。在改进萤火虫优化算法的基础上，建立基于改进萤火虫优化算法的BP神经网络目标群威胁判断结构模型。通过改进萤火虫算法优化BP神经网络的初始权值和阈值，能够更好地预测测试集。实验结果表明，该方法可快速、准确地实现目标群威胁判断。%Setting the ship air defense system as a background, aiming at the problem of the accuracy can not meet the re⁃quirements and the convergence is slow in glowworm swarm optimization, the glowworm swarm optimization adjusting the a⁃daptive step size dynamically is put foruard. It Establishes judge model improved the glowworm swarm optimization and BP neural network based on the improved glowworm swarm optimization algorithm. Optimization of BP neural network by impro⁃ving the firefly algorithm the initial weights and thresholds, prediction can be better on the test set. Experimental results show that, the method can realize the threat assessment of target group quickly and accurately.
Institute of Scientific and Technical Information of China (English)
鲍晓华; 刘冰; 朱庆龙; 刘健
2011-01-01
High voltage submersible motor works in deep water all the year, and its operating insulation performance deteriorated by the complex environment. Due to the special installed circumstances of the motor, it can not be readily maintained, so prediction of the insulation life expectancy, reducing the losses caused by motor deterioration have a great significance. The impacting factors of the insulation life-expectancy of the high voltage submersible motor are analyzed, at the same time, the ways of using BP neural network to predict the insulation life-expectancy of the high voltage submersible motor were proposed. The accelerated life experiment proved that using BP neural network to predict motor insulation life-expectancy can gain actual requirements.%高压潜水电机常年在深水中工作,受到复杂环境的影响,运行绝缘性能恶化,又由于电机安装环境特殊,不能随时被检修,所以预测其绝缘寿命,进而减少因电机绝缘性能恶化而带来的损失具有重大意义.分析了影响高压潜水电机绝缘寿命的因素,同时提出了利用BP神经网络对高压潜水电机绝缘寿命预测的方法,通过加速寿命试验证明,利用BP神经网络对电机寿命预测可达到实际要求.
BP神经网络在双伺服同步运动系统中的应用%Application of BP Neural Network in Double Servo Synchronous Motor System
Institute of Scientific and Technical Information of China (English)
郭丽; 石航飞; 陈志锦; 杨凯; 李勇
2011-01-01
In the double servo synchronous motor system, the current speed often surpasses or lags the given speed,however the traditional PID algorithm can't solve this problem. Therefore, introduce a new PID regulator which combines the traditional PID with BP neural network's PID algorithm, it can control the movement of two servo motors. The traditional PID is used for controlling the two axles during normal operation. However, BP neural network PID algorithm is used to modify the parameters of the position regulator and parameters of the speed regulator in the process of debugging.Through this method, we can achieve the axis B track the speed and orbit of the axis A accurately.%针对传统的PID调节器不能解决双伺服同步运动系统中经常出现的超调和滞后问题,提出一种将传统PID和BP神经网络的PID调节器相结合的方式来控制两伺服电机轴的运动.其中,传统PID算法用于系统正常运行时的控制,而BP神经网络的PID算法用于调试过程中修改位置调整器和速度调节器的参数.该方案能实现轴B准确地跟踪轴A的速度和轨迹而运动.
基于BP神经网络的音乐情感分类及评价模型%Music emotion classification and evaluation model based on BP neural network
Institute of Scientific and Technical Information of China (English)
赵伟
2015-01-01
针对多音轨MIDI文件，提出一种多音轨MIDI音乐主旋律识别方法，通过对表征音乐旋律特征的音高、音长、音色、速度和力度5个特征向量的提取，构建基于BP神经网络的情感模型，并且用200首不同情感特征的歌曲对其进行训练和验证。实验结果显示取得了较好的效果。%The audio track of music melody includes a lot of useful information of music melody, which is the basic of music character recognition and also the premise work in the design of the performance plan of music foundation .Five eigenvectors:pitch, length, tone tempo and strength are extracted for the expression of music melody, by which, the basic music character recognition system can be set up. A emotion model is formed by using BP neural network.200 songs with different emotional characteristic songs will be used as the sample data for the training and validation of the neural network. The results of validation shows the effectiveness of the emotion model.
Institute of Scientific and Technical Information of China (English)
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.
Hedonic Housing Price Model Via BP Neural Network%Hedonic住宅特征价格模型的BP神经网络方法
Institute of Scientific and Technical Information of China (English)
司继文; 韩莹莹; 罗希
2012-01-01
In this paper, hedonic pricing model is used to assess the housing price in Washington, USA. For the pricing model, in this paper, the crime variables around the house are included. The model is built by hedonic pricing method through using traditional OLS method and neural network to simulate and with data modified by Box-cox transformation. The result shows the change in criminal rate makes the housing price change, and as the distance of crime to the housing and the types of crimes changes, the house price changes from -5. 78% to 2. 08%. In July of 2007 and the whole 2008, the influences of crime on housing price are different. It also shows that neural network is more accurate than the traditional OLS method with 5. 74% higher degree of approximation, and shows better features.%房地产在金融市场中占有举足轻重的地位,其价格变化对整个金融市场有着显著的影响.采用特征价格模型,对美国一线城市2007年6月及2008年的房价进行了相关定价研究.对传统特征价格模型的属性因子进行了扩充,加入房产周边犯罪率因子进行模拟；在数值方法计算方面,首先对数据进行了Box-cox变换,分别采用BP神经网络及传统的最小二乘法进行数值模拟分析,结果表明,房价随犯罪事件类型及发生距离房地产的远近有—5.78％～2.08％的变化；在2008年与2007年6月的不同时段内,犯罪率的变化对房价的影响有所不同.BP神经网络模拟的价格与实际交易价格曲线比传统最小二乘模拟的价格曲线精度高出5.74个百分点.
Institute of Scientific and Technical Information of China (English)
崔东文; 金波
2014-01-01
This paper focuses on several key issues of a BP neural network when it is applied to the comprehensive evaluation of water conservancy in a state of relative prosperity. Based on the analytic hierarchy process (AHP), 30 representative indicators were selected out of more than 100 water conservancy indicators, in order to build up a comprehensive evaluation system of water conservancy in a state of relative prosperity and grading standards as well. In practical application, the BP neural network has shortcomings, including the slow convergence and likely occurrence of local extreme values. To overcome these shortcomings, an LM-BP neural network model was established for comprehensive evaluation of water conservancy in a state of relative prosperity. In this case, training and testing samples were generated between standard thresholds using the random interpolation method. A concept of network fitness is proposed as well. The performance of the proposed model was evaluated using the network fitness, the average relative error, and three other statistical indicators. After the evaluation of the model achieved the expected accuracy and generalization ability, it was applied to the comprehensive evaluation of water conservancy in a state of relative prosperity in Wenshan Zhuang and Miao Autonomous Prefecture, and compared with traditional BP and RBF models. The results are as follows: ( a) In both the training samples and testing samples, the LM-BP model had higher evaluation accuracy than traditional BP and RBF models by nearly an order of magnitude, indicating that the LM-BP model has high accuracy and generalization capability and is applicable to the comprehensive evaluation of water conservancy in a state of relative prosperity in Wenshan Zhuang and Miao Autonomous Prefecture. In addition, the LM-BP model has the advantages of fast convergence and a high degree of stability. (b) In the year 2010, water conservancy in a state of relative prosperity in Wenshan
A Modified Algorithm for Feedforward Neural Networks
Institute of Scientific and Technical Information of China (English)
夏战国; 管红杰; 李政伟; 孟斌
2002-01-01
As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.
Neural networks in seismic discrimination
Energy Technology Data Exchange (ETDEWEB)
Dowla, F.U.
1995-01-01
Neural networks are powerful and elegant computational tools that can be used in the analysis of geophysical signals. At Lawrence Livermore National Laboratory, we have developed neural networks to solve problems in seismic discrimination, event classification, and seismic and hydrodynamic yield estimation. Other researchers have used neural networks for seismic phase identification. We are currently developing neural networks to estimate depths of seismic events using regional seismograms. In this paper different types of network architecture and representation techniques are discussed. We address the important problem of designing neural networks with good generalization capabilities. Examples of neural networks for treaty verification applications are also described.
Institute of Scientific and Technical Information of China (English)
王金凤; 杨晨; 毛邓添; 苟斐斐
2013-01-01
为研究水基绒囊钻井液当量静态密度随井深变化规律，以绒囊钻井液PVT实验数据为样本，建立了反映绒囊钻井液压力、温度和密度关系的BP神经网络。以BP神经网络为基础，建立与压力和温度相关的井深与绒囊钻井液井下静态当量密度预测模型。模型计算结果相比多元回归预测结果，与PVT实测数据相对误差更小，与磨80-C1井现场测定的0~2500 m钻井液静态当量密度结果更吻合。用建立的BP神经网络模型预测磨80-C1井所在的磨溪地区绒囊钻井液2500~6000 m的静态当量密度，发现绒囊钻井液随井深增加密度逐渐减小，表明绒囊未在高温高压下被压缩成连续相，随井深增加，温度使气囊膨胀作用比压力压缩作用更明显，间接证明了绒囊结构抗压缩能力强的同时，也表明温度加强了绒囊封堵作用。同时，BP神经网络应用于预测钻井液井下静态当量密度，为井下密度预测提供了一种新的数学处理方法。%In order to study the equivalent static density changing rule of water-based fuzzy ball drilling lfuid with different well depth, BP neural network is established, based on the PVT experimental data of fuzzy ball drilling lfuid, relfecting the relationship between pressure, temperature and drilling lfuid density. On the basis of the network,the equivalent static density prediction model in different well depth is established, and the well depth is associated with pressure and temperature. Compared with the result predicted by multiple regressions, the BP neural network method can obtain higher accuracy, and are more consistent with the static equivalent density in Well M80-C1 at the depth of 0~2 500 m. We predicted the equivalent static density of water-based fuzzy ball drilling lfuid in Moxi area at the depth of 2 500~6 000 m by BP neural network, discovering it decreased with depth increase, which proved the fuzzy ball was not compressed
DEFF Research Database (Denmark)
Hansen, Lars Kai; Salamon, Peter
1990-01-01
We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...
Institute of Scientific and Technical Information of China (English)
XU Min; ZENG Guang-ming; XU Xin-yi; HUANG Guo-he; SUN Wei; JIANG Xiao-yun
2005-01-01
Bayesian regularized BP neural network(BRBPNN) technique was applied in the chlorophyll-a prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS 11.0 software, the BRBPNN model was established between chlorophyll-a and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.00078426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll-a declined in the order of alga amount > secchi disc depth(SD) > electrical conductivity (EC) . Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-a concentration, total phosphorus(TP) and total nitrogen(TN) were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data(chlorophyll-a prediction) and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.
Institute of Scientific and Technical Information of China (English)
洪月华
2011-01-01
In the research and application of Wireless Sensor Networks（WSN）,the use of data mining to improve energy efficiency is an important direction.A distributed data mining algorithm based on rough set theory and BP network was designed and applied to wireless sensor networks.Raw data are discretized and reduced rough set attributes.Minimun condition attributes set is obtained by distributed data mining algorithm.Finally,the reduced decision attributes were used to construct BP neural network classification data.Constructed data mining algorithm can be integrated in each sensor network node.We simulated the distributed data mining algorithm.The simulation result had indicated： This distributed data mining algorithm can reduce data dimension,eliminate data redundancy,decrease communication traffic and lengthen the WSN working hours.%利用数据挖掘来提高网络中能量利用率是无线传感器网络（WSN）的一个重要研究方向.本文构建了基于粗糙集与神经网络相结合的无线传感器网络分布式数据挖掘算法.该算法用粗糙集对节点内的原始数据进行离散化与属性约简后得到的最简决策表训练BP神经网络,再将构造好的BP神经网络集成在每个传感器节点上.仿真结果表明,该算法可以降低数据维数,消除冗余数据、减少网络通信量、延长网络寿命.
Single-tree biomass modeling of Pinus massoniana based on BP neural network%基于BP神经网络的马尾松立木生物量模型研究
Institute of Scientific and Technical Information of China (English)
王轶夫; 孙玉军; 郭孝玉
2013-01-01
The purpose of this study was to explore and verify the applicability of BP neural network model on the single-tree biomass estimation for Pinus massoniana. The optimal model had been built after the topology was determined through screening 12 algorithms and choosing the number of inputs, outputs and hidden nodes. To explain the impact of input variable number on the accuracy, double input BP model was compared with single one. Also, to explain the impact of output variable number on the accuracy, multiple output BP model was compared with single one. And to verify the feasibility, the optimal BP model was compared with allometric equation. The results showed that; 1 ) the algorithm of optimal model LM-DH-8-WtWaWr was Levenberg-Marquardt algorithm, with DBH and height as input variables, total weight, weight of above ground and weight of root as output variables, and the number of hidden nodes was 8. 2 ) Adding input and output variables would not decrease the accuracy of BP neural network model. 3) The optimal BP model LM-DH-8-WtWaWr had a good performance in estimating the biomass of P. massoniana and its accuracy was higher than the relative growth model. The BP model can be used to estimate several quantities at once, which makes the estimation of single-tree biomass more simply.%以马尾松为例,探索并验证BP神经网络模型在立木生物量估测上的适用性.通过12种算法的筛选、输入变量和输出变量的确定以及隐层节点数的选择,确定最优的模型拓扑结构,构建单隐层BP神经网络模型；对比单输入变量与多输入变量模型、单输出变量与多输出变量模型,并分析模型的输入变量数和输出变量数对模型估测精度的影响；将优选BP模型与传统相对生长模型进行对比以验证BP模型的可行性.结果表明:1)最优BP模型LM-DH-8-WtWaWr的训练算法为Levenberg-Marquardt算法,输入变量为D、H,输出变量为Wt、Wa、Wr,隐层节点数为8.2)输入变量
Probabilistic runoff forecasting model based on BP artificial neural network%基于BP神经网络的概率径流预测模型
Institute of Scientific and Technical Information of China (English)
周娅; 郭萍; 古今今
2014-01-01
本文采用多元线性回归模型模拟贝叶斯分析的先验分布和似然函数,并结合反向传播神经网络(BackPropagation Neural Network)建立基于BP神经网络的贝叶斯概率径流预测模型,将模型应用于石羊河出山口六河水系的年径流预测中.为降低BP神经网络的“黑箱”特性对预测精度的影响,在实例应用中结合了区域的水文特性对数据进行预处理,结果表明该方法有效的提高了模型的预测精度;同时相对于确定性水文预测方法而言,贝叶斯概率水文预报定量地、以分布函数形式描述水文预报的不确定度,能向用户提供更多、更全面的信息,为决策提供更有价值的技术支持.
基于LM-BP网络的粮食产量预测%Forecasting Corn Production Based on LM-BP Neural Network
Institute of Scientific and Technical Information of China (English)
郭庆春; 可振芳; 李力
2012-01-01
A corn production porecasting method based on improved LM-BP was proposed. According to measurement and a-gricultural significance principle, 9 factors of grain-sown area, fertilizer input, effective grain irrigated area, stricken area, rural electricity consumption, total agriculture mechanism power, the population engaged in agriculture, rural residents family productive assets, the average net income of rural households were extracted as the network input; corn production was extracted as the network output. The LM algorithm could minimize the error, and the modeling results were evaluated with the correlation coefficients, relative error, etc. For training sample set, the correlation coefficient between the simulated value and the actual value was 0.996, the average relative error was 0.47%; for testing sample set, the correlation coefficient between the forecasted value and the actual value was 0.994, the average relative error was 0.56%. The results showed that the improved LM-BP model could improve simulation precision and stability of the model. This method is effective and feasible for com production prediction.%利用Levenberg-Marquardt (LM)算法对BP神经网络法进行改进,提出了基于改进型LM-BP神经网络模型的粮食产量预测方法.提取了粮食作物播种面积、化肥施用量、粮食作物有效灌溉面积、受灾面积、农村用电量、农业机械总动力、从事农业的人口、农村居民家庭生产性固定资产原值、农村居民家庭平均纯收入9个因子作为输入因子构筑模型,粮食产量作为网络输出,通过LM算法使网络误差最小化,最后使用相关系数、相对误差等指标对模型的模拟结果进行检验.结果表明,训练样本集中模拟值和实际值的相关系数为0.996,平均相对误差为0.47％；检测样本集中,预测值和实际值的相关系数为0.994,平均相对误差为0.56％；该模型具有较高的拟合精度和预测精度,将此网络模
Institute of Scientific and Technical Information of China (English)
闫化军; 章毅
2004-01-01
In this paper,the problem of prediction of protein/domain structural class based on their secondary structure contents has been investigated via BP neural networks (Multiple Layer perceptron with Back-Propogation algorithm) plus a competitive layer.By embedding a competitive layer into the network,the prediction accuracy can be significantly improved.With a small training set and a simple network architecture,a high prediction accuracy has been achieved,i.e.,self-consistence accuracy 97.62%,jack-knife test accuracy 97.62% and extrapolating accuracy 90.74% on average.It is believed that the neural networks of this paper can provide a more appropriate protein/domain structural class assignment criterion with a complete classification attribute vector and a bigger and more representative training set built up.%运用加入竞争层的BP网络,研究了基于蛋白质二级结构内容的域结构类预测问题.在BP网络中嵌入一竞争,层显著提高了网络预测性能.仅使用了一个小的训练集和简单的网络结构,获得了很高的预测精度:自支持精度97.62%,jack-knife测试精度97.62%,及平均外推精度90.74%.在建立更完备的域结构类特征向量和更有代表性的训练集的基础上,所述方法将为蛋白质域结构分类领域提供新的分类基准.
Institute of Scientific and Technical Information of China (English)
秦晏旻; 李雪英; 任静; 蒋洪德
2011-01-01
Film cooling is necessary for modern gas turbine.Its cooling effectiveness is sophisticated influenced by multi parameters.The BP neural network is applied to predict the adiabatic film cooling effectiveness of the cooling system with multi geometry and flow parameters.The input parameters of neural network are chosen as blowing ratio,density ratio,free stream turbulence intensity,area ratio and length ratio.A database covering the real operation range is build up.Prediction from the neural network trained by Bayesian Regulation backpropagation is compared to an existing correlation.The result shows a good accuracy and wide application range of the neural network model.It implicates that the developed model is promising to be applied on the film cooling system.%气膜冷却作为当代燃机高温透平中必需的冷却手段,其冷却性能在多种参数的影响下表现复杂。采用BP神经网络模型对多种几何、流动参数变化下的气膜冷却系统的绝热气膜冷却效率进行预测。选择气膜冷却系统的吹风比、密度比、主流湍流度、面积比和长径比作为神经网络的输入参数,以燃气轮机透平叶片气膜冷却的实际运行工况为范围建立数据库。计算结果表明,采用贝叶斯归一化法训练后建立的气膜冷却神经网络模型在预测精度上要优于经验公式法,而且参数适用范围更广,具有良好的发展应用前景。
Institute of Scientific and Technical Information of China (English)
王瑛; 赵谦; 曹玮
2011-01-01
根据简单多数原则引入专家动态权数,与人工神经网络BP算法相结合,构建E-BP科技奖励综合评价智能模型.实证分析表明,该模型减少了传统科技奖励评价方法中受专家主观因素和模糊随机因素的影响,使评价结果更加客观、合理.%According to the principle of simple majority, the introduction of dynamic experts weight and artificial neural network BP algorithm for combining E-BP model to build a comprehensive evaluation of intelligent model of Science and Technology Award.Empirical analysis shows that the model to reduce the traditional methods of science and technology award by the expert's subjective evaluation factors and fuzzy stochastic factors, therefore, the results to be more objective and reasonable.
Research on Stock Price Reversal Points Prediction Based on BP Neural Network%基于BP神经网络的股票价格反转点预测
Institute of Scientific and Technical Information of China (English)
王建国
2015-01-01
The stock market has an important role in the whole financial market and the prediction of the stock price reversal point is one of the most abstractive and the most significant research topic. And the BP neural network which has been proved to be the ability of achieving any nonlinear mapping function whatever its complexity ,is very suitable for resolving the problem with a complex internal mechanism such as stock price prediction. Aims to achieve stock price reversal points prediction with BP model.%股票市场在整个金融市场中起着很重要的作用。而股票价格反转点的预测是最具有吸引力并且有意义的研究问题之一。 BP神经网络作为已被证明为具有实现任何复杂非线性映射的功能的多层预测模型特别适合于求解股票预测之类的内部机制复杂的问题。旨在利用BP神经网络模型的预测能力实现对股票价格的反转点预测。
Institute of Scientific and Technical Information of China (English)
曾淇威
2012-01-01
研究了在MATLAB环境下，利用BP神经网络解决硬盘播出系统电平诊断中的分类问题。首先阐述了BP神经网络的一种故障诊断模型，其次分析了基于集合结构的电平诊断实现原理，最后研究了BP神经网络对电平诊断的求解方法。结论表明：利用在基于BP神经网络的MATLAB环境下解决电平诊断问题时仿真效果明显，有利于实际工程技术的应用。% In this paper the diagnosis classification problems for hard disk broadcasting system are solved using BP(Back Propagation)neural network on MATLAB condition. First, based on BP neural network, a fault diagnosis model is proposed. Second, the realization principle of level diagnosis is analyzed based on set structure. Final y, the method for solving the level diagnosis with BP neural network is investigated. In conclusion:not only the simulation effect is obviously for the solution of level diagnosis problems on MATLAB condition based on BP neural network, but also facilitates the engineering technology application.
Institute of Scientific and Technical Information of China (English)
张扬; 李四平; 赵社戌
2012-01-01
本文使用BP神经网络对方钢管混凝土轴心受压试验的数据进行拟合，并将BP神经网络的拟合曲线与10次多项式的拟合曲线进行了对比。结果表明，建立的BP神经网络模型能够准确拟合方钢管混凝土轴心受压的试验数据，具有较好的非线性拟合效果，拟合精度高。%By creating a model based on BP neural network, data fitting of square concrete-filled steel tube concentrically loaded in compression was carried out in the article. The fitting result based on BP neural network was compared with the ten - order polynomial fitting result. The BP neural network model could accurately fit the data of square concrete-filled steel tube. The performance of BP neural network model in nonlinear fitting was excellent and the fitting precision was high.
Fuzzy Multiresolution Neural Networks
Ying, Li; Qigang, Shang; Na, Lei
A fuzzy multi-resolution neural network (FMRANN) based on particle swarm algorithm is proposed to approximate arbitrary nonlinear function. The active function of the FMRANN consists of not only the wavelet functions, but also the scaling functions, whose translation parameters and dilation parameters are adjustable. A set of fuzzy rules are involved in the FMRANN. Each rule either corresponding to a subset consists of scaling functions, or corresponding to a sub-wavelet neural network consists of wavelets with same dilation parameters. Incorporating the time-frequency localization and multi-resolution properties of wavelets with the ability of self-learning of fuzzy neural network, the approximation ability of FMRANN can be remarkable improved. A particle swarm algorithm is adopted to learn the translation and dilation parameters of the wavelets and adjusting the shape of membership functions. Simulation examples are presented to validate the effectiveness of FMRANN.
第三方网上支付企业核心竞争力评价%Third-Party Online Payment Core Competence Evaluation Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
王拉娣; 史亚伟
2012-01-01
Through the online payment industry environment, industry chain and the analysis of major enterprises, built with third-party online payment core competence evaluation index system including with 14 evaluation index. BP neural network model is designed to select a sample of six training companies, three companies to test, and the use of BP neural network model quantitatively identify third-party online payment the strength of the core competence of enterprises. Studies have shown that: compared with the traditional linear model, BP evaluation mode is more dynamic and self-learning nature, the error evaluation of the results of small, high precision, fully reflects real situation of the third-party online payment enterprise's core competence, for third-party online payment to build the core compete-tiveness of enterprises to provide a benchmark, while the third-party online payment company for quantitative evaluation of core competencies has opened up a new way.%通过对网上支付行业环境、产业链和主要企业的分析,构建了具有14个评价指标的第三方网上支付企业核心竞争力评价指标体系.设计了BP神经网络模型,选择了6家样本企业进行训练、3家企业进行测试,并运用BP神经网络模型定量识别第三方网上支付企业核心竞争力强弱.研究表明:BP评价模型与传统的线性评价模型相比,具有更高的动态性和自学习性,评价结果误差小,精度高,能充分反映第三方网上支付企业核心竞争力的真实状况,为第三方网上支付企业核心竞争力的打造提供了基准,同时对第三方网上支付企业核心竞争力进行定量评价开辟了一条新途径.
Institute of Scientific and Technical Information of China (English)
张载龙; 茹亮
2013-01-01
With the improvement of people's living standards,the safety of food and medicine is becoming the focus of attention. Temper-ature monitoring is the key factor to ensure the material safety and reduction of economic losses in the logistics transportation. Especially for dairy products,plasma,vaccines and other temperature-sensitive items,more stringent is required. Currently,the refrigerated trucks that lack of a better way in intelligent control can not be achieved for the effective temperature monitoring. Using BP neural network to predict the change in temperature of the items can achieve good control effect. In this paper,a novel method of BP learning algorithm to improve the convergence rate in BP neural network is proposed. The method is used to predict the cold chain temperature. Matlab simula-tion shows that the algorithm has a fast convergence rate theoretically.%随着人们生活水平的提高，食品和医药安全逐渐成为社会关注的焦点。温度监控是保证物流运输中物品安全、减少经济损耗的关键。尤其是乳制品、血浆、疫苗等温度敏感性物品对运输环境中的温度要求更严格。当前冷藏车温度监控在智能控制方面缺乏较好的方法，无法达到对于温度敏感性物品的有效监测。通过BP神经网络对物品的温度变化进行预测可以达到很好的监控效果。针对BP神经网络中存在的收敛速度慢的问题，文中提出了一种自适应的学习速率的新方法，并将其应用于冷藏车温度预测中，通过Matlab仿真表明该算法具有很好的预测效果。
Institute of Scientific and Technical Information of China (English)
崔东文
2013-01-01
分析BP神经网络应用于水质评价中存在的问题和目前水质评价中的不足，基于地表水环境质量分级标准和L-M算法原理，提出LM-BP神经网络水质综合评价通用模型。利用随机内插方法在地表水环境质量分级标准阈值间生成训练样本和检验样本，采用顺序和随机两种方法选取训练样本和检验样本进行随机模拟；利用平均相对误差、最大相对误差等统计指标评价LM-BP模型性能，并构建传统BP 、RBF模型作为对比模型；以某水质评价实例进行模型验证，并与灰色关联分析法、模糊综合评判法和TOPSIS法评价结果进行比较。结果表明：LM-BP通用模型具有评价精度高、泛化能力强、收敛速度快、算法稳定和通用性能好等优点，可应用于任意水质评价。在实际应用中仅需对通用模型的评价因子、输入维数和隐含层神经元数进行删减即可满足评价要求。%Existing problems and shortcomings in water quality evaluation using the BP neural network were analyzed. Based on surface water environmental quality grading standards and the principle of the L-M algorithm, a general model of the LM-BP neural network was developed for comprehensive assessment of water quality. First, the random interpolation method was used to generate training and testing samples at the surface water environmental quality grading standard threshold, and the order and random methods were used to select training and testing samples for random simulation. Then, statistical indices such as the average relative error and the maximum relative error were used to evaluate the performance of the LM-BP model, and the traditional BP and RBF models were constructed as the contrast models. Finally, the model was applied to water quality evaluation in a case study and compared with the gray correlation analysis method, fuzzy comprehensive evaluation method, and TOPSIS method. The results show that the
Rule Extraction:Using Neural Networks or for Neural Networks?
Institute of Scientific and Technical Information of China (English)
Zhi-Hua Zhou
2004-01-01
In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.
Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian － River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple－variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
Multilayered feed forward neural network based on particle swarm optimizer algorithm
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
BP is a commonly used neural network training method, which has some disadvantages, such as local minima,sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer (PSO), guaranteed convergence particle swarm optimizer (GCPSO), an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.
Applications of Neural Networks in Spinning Prediction
Institute of Scientific and Technical Information of China (English)
程文红; 陆凯
2003-01-01
The neural network spinning prediction model (BP and RBF Networks) trained by data from the mill can predict yarn qualities and spinning performance. The input parameters of the model are as follows: yarn count, diameter, hauteur, bundle strength, spinning draft, spinning speed, traveler number and twist.And the output parameters are: yarn evenness, thin places, tenacity and elongation, ends-down.Predicting results match the testing data well.
Introduction to Artificial Neural Networks
DEFF Research Database (Denmark)
Larsen, Jan
1999-01-01
The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....
Neural network fault diagnosis method optimization with rough set and genetic algorithms
Institute of Scientific and Technical Information of China (English)
SUN Hong-yan; XIE Zhi-jiang; OUYANG Qi
2006-01-01
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
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 branching and, in doing so, simulates observed scaling laws as pervasive to neural and behavioral activity. These scaling laws are related to neural and cognitive functions, in that critical branching is shown to yield spiking activity with maximal memory and encoding capacities when analyzed using reservoir computing techniques. The model is also shown to account for findings of pervasive 1/f scaling in speech and cued response behaviors that are difficult to explain by isolable causes. Issues and questions raised by the model and its results are discussed from the perspectives of physics, neuroscience, computer and information sciences, and psychological and cognitive sciences.
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. .
Compressing Convolutional Neural Networks
Chen, Wenlin; Wilson, James T.; Tyree, Stephen; Weinberger, Kilian Q.; Chen, Yixin
2015-01-01
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected laye...
Institute of Scientific and Technical Information of China (English)
刘垠杰; 黄强; 程玉强; 吴建军
2012-01-01
将云模型与BP（backpropagation）神经网络以串联方式有机结合，首先利用云变换方法进行网络的结构辨识和云模型的特征提取，同时通过在输入层引入单位延时环节描述发动机工作过程动态特性，研究提出了基于动态云BP网络的液体火箭发动机故障诊断方法.结合实际试车数据的验证结果表明，该方法能够准确识别发动机已有的3种故障模式，通过在试车数据中添加0期望、0.2标准差的随机噪声的方法来模拟环境噪声和测试过程中产生的随机噪声，根据持续性原则，方法仍能够正确进行故障检测与分类.方法单步运行时长为1.124x10-4,完全能够满足实时性要求.%A fault diagnosis method for liquid-propellant rocket engines was proposed based on the dynamic cloud-BP(back propagation) neural network in the way of the integration of cloud model and BP neural network.The Cloud transform method was used to identify the network configuration and to extract the cloud features.And a unit time-delay was also introduced into the input layer to describe the dynamic characteristics of the engine.Results with test data show that the method can isolate the existed 3 fault modes precisely.A 0 expectation,0.2 standard deviation noise was used to simulate the entironmental noise and stochastic noise,and the method can still detect and classify the fault accurately acount to lasting-rule.The method can run in real-time with the single processing time being 1.124×10-4 s.
Institute of Scientific and Technical Information of China (English)
闫岩; 孙彩堂; 周逢道; 刘长胜
2016-01-01
This paper is about a way to detect landmines based on BP neural network, put it more specifically,landmines are detected via landmine response curves acquired by electromagnetic detection. First, it tested the recognition effect of BP neural network upon four common curves namely sine wave, square wave, saw tooth wave and trapezoidal wave; second, simulation experiments are carried out to see how these curves are affected by changing the network parameters such as the number of hidden layer nodes and learning algorithms as well as by adding a certain proportion of noise in normal curves. Experimental results show that the recognition rate of all normal curves is 100% and that of the signals with noise less than 10% is also high. This technology has been applied to detect landmines and produced good results.%提出一种基于BP神经网络的地雷识别方法，利用电磁探测方法测得的地雷响应曲线对地雷进行识别。首先分析BP神经网络对4类常见曲线(正弦波、方波、锯齿波、梯形波)的识别效果，通过改变隐含层节点数、学习算法等网络参数以及对正常曲线加入一定比例的噪声，仿真分析它们对曲线识别的影响。实验结果表明：该方法对正常曲线的识别率几乎均达到100%，对于噪声约10%的信号也具有较高的识别能力。将该技术应用于地雷的识别中，取得比较好的识别效果。
Institute of Scientific and Technical Information of China (English)
尚琳; 刘国华; 张锐; 李国通
2013-01-01
针对Sage-Husa自适应滤波方法存在的窗函数开窗大小选择问题,提出一种基于BP神经网络学习估计系统协方差矩阵的自适应Kalman滤波算法.该算法以Kalman滤波预测残差向量作为网络输入,通过网络分段离线学习确定预测残差向量与预测残差协方差矩阵间的非线性关系,自适应地估计Kalman滤波系统协方差矩阵.将其应用到自主定轨系统,仿真结果表明利用本文算法自主定轨60天星座平均URE误差小于1.9米,且能够快速跟踪到系统噪声的突变,较Kalman滤波方法和Sage-Husa自适应滤波方法具有更好的性能.%In this paper,an adaptive covariance matrix estimation algorithm based-on BP neural network learning is proposed to solve the window size selection problem in Sage-Husa adaptive filtering way.The innovation vector derived from the Kalman filter (KF) is employed as the input to the BP neural network and the nonlinear function between the innovation vector and the innovation covariance matrix can be determined through learning of the network.The covariance matrix estimation algorithm proposed in this paper is applied to the autonomous orbit determination system.The simulation results show that the mean constellation URE of autonomous orbit determination will be within 1.9 meters in 60 days and it has better performance than the Sage-Husa adaptive filtering in the estimation of the system covariance matrix of the autonomous orbit determination algorithm.
基于BP网络的混凝土耗能器骨架曲线拟合%Based on BP Neural Network of Concrete Energy Dissipator Skeleton Curve Fitting
Institute of Scientific and Technical Information of China (English)
王文娟; 陈继光
2012-01-01
结合5种混凝土延性柱耗能器在低周期反复荷载作用下的试验数据研究,利用神经网络的工作原理,通过建立神经网络的输入层、隐含层、输出层,确定输入单元、输出单元和隐含层节点数,从而建立了BP神经网络的模型,并根据已有的部分试验数据数据.对网络进行训练,对各种混凝土延性柱耗能器骨架曲线进行了预测拟合,实现混凝土延性柱耗能器骨架曲线的数字化,使其成为具有分析和判断的拟合曲线功能,完整的描绘混凝土延性柱耗能器的骨架曲线,为后续混凝土延性柱耗能器性能研究的仿真模拟提供了可靠的数据模型.结果表明,这种方法是可行的.%Combined with 5 kinds of concrete ductility column energy dissipator at low cycle load test data research, the working principle of neural network, and by establishing a neural network's input layer, hidden and output layer, determine inputs unit, output unit and hidden node number, and to establish the BP neural network model, and part of the test data according to the existing data. Networks are trained to of all kinds of concrete ductility column energy dissipator skeleton curve fitting, forecast the realization concrete ductility column energy dissipator skeleton curve digital, make it become with analysis and judgment of the fitting curve function, complete description of concrete column energy dissipator ductility of skeleton curves, for the subsequent concrete ductility column energy dissipator performance simulation study provides the reliable data model. The results show that the method is feasible.
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.
Institute of Scientific and Technical Information of China (English)
代秋芬; 张元华; 胡帅显; 王磊
2012-01-01
通过对Q195钢丝在不同温度、时间下的退火处理,测试了退火前后的抗拉强度.采用BP神经网络建立了Q195钢丝连续退火后抗拉强度与初始抗拉强度、钢丝直径、保温时间和退火温度之间的预测模型,对钢丝连续退火后的抗拉强度进行预测.结果表明:BP网络预测最大相对误差为3.49％.该预测模型对于Q195钢丝连续退火抗拉强度的预测是有效的、可行的.%Q195 steel wire was annealed at different temperatures and times, and the tensile strength was tested before and after annealing. A predicting model based on BP neural network was constructed. The mapping relationship between tensile strength of Q195 steel wire continuous annealed and initial tensile strength, diameter, holding time, annealing temperature was established. The tensile strength of continuous annealed steel wire is predicted. The results show that the neurl network training error is less than 3.49%. The model is valid, and feasible to predict the tensile strength of Q195steel wire after continuous annealing.
Catalytic Oxidized Reaction of Paraffin Wax Based on BP Neural Network%基于BP神经网络的石蜡催化氧化反应的研究
Institute of Scientific and Technical Information of China (English)
黄玮; 丛玉凤; 郭大鹏
2012-01-01
The oxidized wax was prepared by catalytic oxidized reaction of paraffin wax which used BP neural network to build mathematical model of acid value and saponification value influenced by the amount of reactive catalyst and accessory ingredient, airflow rate, reaction temperature and time, and utilized the model of neutral network to calculate the technology condition of preparing oxidized wax through catalyzing and oxidizing paraffin wax. Consequently, optimum technology conditions were gained in order to achieve the objective of reducing experimental number of times.%在石蜡催化氧化反应制备氧化蜡的研究中,利用BP神经网络建立反应催化剂用量、助剂用量、空气流量、反应温度和反应时间对酸值和皂化值影响的数学模型,并利用该神经网络模型对石蜡催化氧化制备氧化蜡的工艺条件进行预测,从而获得最优工艺条件,达到缩短实验次数的目的.
Institute of Scientific and Technical Information of China (English)
范千; 许承权; 方绪华
2011-01-01
A novel model based on empirical mode decomposition ( EMD) and neural network for dam deformation prediction is presented in the paper. Firstly, considering that EMD has an advantage to do adaptive decomposition according to characteristics of the signal itself, deformation time series is decomposed into a series of intrinsic mode functions (IMF) in different scale space. Then, according to the change regulation of each IMF, they are forecasted by appropriate LM - BP neural networks. Finally, these forecasting results of each IMF are combined to obtain final forecasting result. The calculation result of a practical example shows that this model has higher forecasting precision and better adaptability.%提出一种基于经验模式分解(EMD)与LM-BP神经网络相结合的模型进行大坝变形预报的方法.先利用EMD具有根据信号本身特征进行自适应分解的功能将变形时间序列分解为一系列不同尺度的固有模式分量IMF,再根据各个IMF的变化规律采用相匹配的LM-BP模型进行预报,最后对各分量的预报值进行叠加得到最终的变形预报结果.实例分析表明,该方法具有较高的预测精度和较强的适应能力.
Triangulation and PSO-BP Neural Network Used in Star Pattern Recognition%三角形剖分以及PSO-BP神经网络在星图识别中的应用
Institute of Scientific and Technical Information of China (English)
张少迪; 王延杰; 孙宏海
2011-01-01
In order to realize accurate measurement of aircraft's current attitude, how to improve real time and robustness of star pattern recognition is the key of star sensor. The algorithms for star pattern abstraction, training sample set creation and network training improvement are proposed. First, a method of triangulation based on the character of star image is designed to combine all the stars of current field of view, which is used to extract star pattern and create complete training samples. The character of star pattern extracted has the advantages of translation and rotation invariance. Then BP Neural Network serves to recognize the star pattern with the weight matrix instead of navigation library. It is very fast to acquire current star information when the network has finished training. Particle Swarm Optimization (PSO) serves to train BP Neural Network, which helps BP network converge to the most optimum value. The experimental results show that the success rate of accurate recognition is 100％.%为了实现星敏感器对航天器当前姿态的准确测量,如何提高星图识别算法的实时性和鲁棒性成为星敏感器的关键技术.对星图识别过程中应用的模式提取、训练样本集的建立以及神经网络训练方式的改进等算法进行研究.首先,设计一种基于星图特征的三角形剖分方法,将视场内的恒星以三角形的方式组合起来,提取星图模式,建立完备的训练样本集,使星图特征具有平移和旋转不变性.然后,采用BP神经网络识别星图特征,以权值矩阵代替导航星库,一旦网络训练完成,可以很快获得当前星图信息,实现星敏感器星图识别算法的实时性和鲁棒性;为了优化BP神经网络改进其自身缺点,采用PSO(粒子群算法)训练BP神经网络,获取使BP神经网络趋近全局最优的初始权值和阈值,使其加快收敛至全局最优.由实验结果表明,该星图识别算法识别率达100%.
Gupta, S; Gupta, Sanjay
2002-01-01
This paper initiates the study of quantum computing within the constraints of using a polylogarithmic ($O(\\log^k n), k\\geq 1$) number of qubits and a polylogarithmic number of computation steps. The current research in the literature has focussed on using a polynomial number of qubits. A new mathematical model of computation called \\emph{Quantum Neural Networks (QNNs)} is defined, building on Deutsch's model of quantum computational network. The model introduces a nonlinear and irreversible gate, similar to the speculative operator defined by Abrams and Lloyd. The precise dynamics of this operator are defined and while giving examples in which nonlinear Schr\\"{o}dinger's equations are applied, we speculate on its possible implementation. The many practical problems associated with the current model of quantum computing are alleviated in the new model. It is shown that QNNs of logarithmic size and constant depth have the same computational power as threshold circuits, which are used for modeling neural network...
Institute of Scientific and Technical Information of China (English)
金志凤; 符国槐; 黄海静; 潘永地; 杨再强; 李仁忠
2011-01-01
利用2009年12月-2010年5月塑料大棚内外观测的气象数据,构建了基于BP神经网络的杨梅生产大棚内的最高、最低气温预测模型,根据逐时转化系数计算出棚内相应的逐时气温,达到逐时预报大棚内气温的目的.通过模拟回代和对独立试验数据的验证,基于BP神经网络模型对大棚内日最低气温、日最高气温和逐时气温预测值与实际值的回归估计标准误差(RMSE)分别为0.8℃、1.4C和0.7℃,精度明显高于同时利用逐步回归法建立的模型.该模型所需参数少,实用性强,模拟精度高,可为设施杨梅气象服务和环境调控提供依据.%The minimum and maximum temperature prediction model inside greenhouse planted Myica rubra was established based on BP neural network, by using meteorological data both inside and outside the greenhouse from December 2009 to June 2010 in Wenzhou of Zhejiang province. Using the independent experimental data and simulation back generations to verify the model,the results indicated that the root mean square error( RMSE) between the predicted value and measured value based on 1: 1 line for the minimum and maximum and hourly inside air temperature were 0. 8℃ ,1.4℃ and 0. 7℃ .respectively. The precision of BP neural network model was higher than that of the stepwise regression model obviously. The model,with few parameters,could predict the greenhouse temperature more accurately .which could provide scientific basis for facility meteorological service and environment regulation of greenhouse Myr-ica rubra cultivation.
基于BP神经网络的驾驶精神疲劳识别方法%Recognition method of driving mental fatigue based on BP neural network
Institute of Scientific and Technical Information of China (English)
郭孜政; 谭永刚; 马国忠; 潘毅润; 陈崇双
2014-01-01
To recognize driving mental fatigue efficiently, this study constructs a recognition method based on ECG. The method proposes hierarchy partition of state of driving mental fatigue by using driving behavior performance as objective evaluation indexes. Meanwhile, taking 6 indexes of HRV as fatigue recognition characterization factors and BP artificial neural network model, this paper establishes the recognition model for state of driving mental fatigue. Finally, according to examples, the mental fatigue is divided into two classifications. Collecting 4 hours continual driving behavior performance and ECG data from 10 drivers to test the model, the result shows that the average recognition accuracy rate is between 71%and 80%, and the average accuracy rate is 73%. The combination of BP neural network model and HRV indexes could recognize fatigue effectively.%为了对驾驶精神疲劳予以有效识别，基于行为绩效结合心电信号指标构建了一种驾驶精神疲劳识别方法．以驾驶行为绩效为客观测评指标，给出了驾驶精神疲劳状态的分级划分方法．在此基础上，以心率变异性的6项指标作为疲劳识别特征因子，采用BP神经网络模型，建立了驾驶精神疲劳状态分类器．最后结合实例，依据驾驶行为绩效，将疲劳状态划分为2级，采用10名驾驶员连续4 h的驾驶行为绩效（反应时）、心电数据，对模型、方法予以测算．结果表明，10名驾驶员平均正确识别率在71％～80％之间，且其平均正确识别率为73％．BP神经网络模型与心率变异性指标相结合可有效的识别疲劳．
Institute of Scientific and Technical Information of China (English)
周长林; 王振义; 刘统; 钊守国; 梁臻鹤
2016-01-01
The performances of low dropout linear regulator (LDO) can be lowered to different degrees under the electromagnetic interference, which may affect the whole system's electromagnetic compatibility.In order to solve this problem, we put forward a modeling method based on BP neural network, and used the genetic algorithm to optimize the initial weights and threshold matrix network.Meanwhile, we used the direct power injection method to design the circuit board for electromagnetic interference injection experiments of LDO within the frequency ranging from 100 MHz to 1 GHz and the power ranging from-15 dBmW to 25 dBmW.Taking the output of the LDO as training data, we compared the different structures of the BP neural network prediction performance, and then selected the appropriate network structure.Moreover, we established a model of the LDO electromagnetic interference damage to predict the effects of electromagnetic interference on LDO output data and conducted electromagnetic susceptibility, and made the experimental verification.Finally, we used the model to predict the LDO conducted electromagnetic susceptibility, and compared the predicted data and experimental data of the model.The results show that the maximum relative error between simulation model's output and the LDO test output is less than 8％, and the maximum relative prediction error between the simulation data of electromagnetic susceptibility of this model and the experiment data is less than 9％ in the frequency range of 100 MHz to 1 GHz.%低压差线性稳压器(LDO)在电磁干扰影响下会发生不同程度的性能受损,进而影响到整个系统的电磁兼容性能.为解决这一问题,提出了一种基于误差反向传播(BP)神经网络的建模方法,并使用遗传算法优化网络初始权值与阈值矩阵.采用直接功率注入法设计电路板,在100 MHz～1 GHz频率范围、-15～25 dBmW功率范围内对LDO进行电磁干扰注入实验;采样LDO的输出作为训练数
BP人工神经网络在鱼糜挤压制品生产中的应用%Application of BP artificial neural network in extruded surimi product
Institute of Scientific and Technical Information of China (English)
张建友; 王嘉文; 吕飞; 丁玉庭
2012-01-01
采用反向传播（BP）人工神经网络和响应面法（RSM）模拟操作工艺参数（鱼糜含量、螺杆转速、III区加热温度）对双螺杆挤压机生产的鱼糜挤压制品的品质属性（持水性、膨润度、硬度和弹性）的影响,并比较了BP人工神经网络和RSM所建立的操作工艺参数与产品属性间关系模型的预测误差。试验结果表明,经训练的BP人工神经网络的模拟值和实际值的均方差（MSE）及和方差（SSE）均比RSM低,在模拟产品属性上具有更好的拟合度和准确性,采用此法确定的鱼糜挤压制品最佳工艺参数为：鱼糜含量45.70%,螺杆转速170r/min,III区温度106.2℃。%A back propagation （BP） artificial neural network （ANN） model was developed to predict the properties of extruded surimi products produced by a twin screw extruder. A BP-ANN model was established in MATLAB to simulate the relationships between running parameters of contents of surimi, screw speed and heating temperature of barrel III with the properties of surimi products such as water holding capability （WHC）, swelling degree （SD）, hardness （H） and springiness （S） during extrusion process. Using the experimental data from a quadratic general rotary unitized design, the neural network was trained and then validated with a validation subset. Besides, the method of response surface method （RSM） was also used to analyze and predict these properties. By comparing with mean squared error （MSE） and sum squared error （SSE） of BP-ANN and RSM models, it was showed that BP-ANN was more accurate than RSM in predicting the relationship between the responses and the running parameters. The BP-ANN was then used to search for a combination of running parameters resulting in maximal WHC, SD, S and minimal H. As a result, the optimal running parameters for content of surimi, screw speed and heating temperature was 45.70%, 170 r/min and 106.2 %, respectively.
Directory of Open Access Journals (Sweden)
Kapil Nahar
2012-12-01
Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems.Ann’s, like people, learn by example.
Directory of Open Access Journals (Sweden)
Kapil Nahar
2012-12-01
Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.
Neural networks for triggering
Energy Technology Data Exchange (ETDEWEB)
Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))
1990-01-01
Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.
BP神经网络的身管寿命预测方法%Study on Prediction Method of Barrel Life based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
陈国利; 韩海波; 于东鹏
2008-01-01
作为影响火炮寿命的炮膛烧蚀和磨损,其作用过程是一个相当复杂的综合过程.到目前为止,还不能建立起一个较好的数学模型来计算火炮内膛烧蚀磨损量.针对这种情况,在实测数据的基础上,提出了采用BP(Back Propagation)神经网络的方法,利用其优越的非线性逼近能力和泛化能力来计算炮膛烧蚀磨损量,并根据最大烧蚀磨损量进行身管寿命预测.
Application of BP Neural Network in Packaging Cost Prediction of Chinese Liquor%BP神经网络在白酒包装成本预测中的应用
Institute of Scientific and Technical Information of China (English)
苏杰; 丁毅; 李国志
2011-01-01
The packaging cost of Chinese liquor differs from one to another in the market today. In order to estimate it, a BP neural network model was established for the packaging cost prediction of Chinese liquor based on MATLAB. According to the market research data, this paper established a prediction model in MAT LAB , and made use of procedures of MATLAB to realize training, simulation and verification of the model.This process can provide referent for packaging cost of Chinese liquor.%目前市场上白酒包装成本不一,为了估算白酒的包装成本,本文基于MATLAB构建白酒包装成本预测的BP神经网络模型,依据市场调查数据,在MATLAB中确立预测模型,从而实现预测模型的训练、仿真及验证,为白酒包装成本的确定提供决策方案.
Institute of Scientific and Technical Information of China (English)
李长荣; 赵浩文; 谢祥; 尹青
2011-01-01
BOF steelmaking is a very complex physical chemistry process; it is hard to achieve the target value of end-point by manual control. Multiple reblowing operations were usually necessary to taping off. Based on analyzing the influence major factors of phosphorus end-point in converter, the dominative factors of prediction model of endpoint for Conrerter smelting were fixed. A prediction model of end-point phosphorus content for BOF process is established based on Levenberg-Marquardt(LM) algorithm of BP neural network. The results show that the phosphorus content of end-point hitting rates could be reached 90％ if the accuracy of target error were ±0. 002％.%转炉炼钢过程是一个非常复杂的物理化学变化过程,人工控制很难一次达到终点目标值,通常需要经过多次补吹才能出钢.通过研究影响转炉冶炼终点磷含量的主要因素,确定了影响转炉终点磷含量的参数,建立了基于Levenberg-Marquardt(LM)算法BP神经网络转炉终点磷含量的预报模型.结果表明:在预报误差目标精度为土0.002%内,命中率达到了90%.
Institute of Scientific and Technical Information of China (English)
明道洋; 孙宗芹
2016-01-01
Although CAI (Computer-Aided Instruction) English teaching has made great progress,the traditional CAI English teaching focus less on students'individual differences.This paper introduces the ICAI MOOC teaching system,which can automatically push learning contents to students based on their learning performance.By using the BP neural network algorithm,the paper emphasizes the principle,the training steps and the heuristic rules of the algorithm.Furthermore,the ICAI MOOC teaching system has been developed and tested.The result shows that the software can meet the design requirements and has good stability.%英语CAI教学取得了很大进展，但传统英语CAI教学对学生个体差异关注不够。本文提出了一种根据学生学习效果自动推送个性化教学内容的ICAI慕课系统，分析了ICAI慕课系统的基本需求和整体结构；采用BP神经网络算法，重点阐述了该算法的原理、训练步骤和启发式规则；并对系统进行了开发与测试。结果显示：该软件能满足设计需求且具有较好的稳定性。
Institute of Scientific and Technical Information of China (English)
王学文; 魏彦凤; 单其帅; 王玉芬; 孙毅
2013-01-01
In order to solve the problem of poor result which is caused by the traditional forecasting methods on the real estate customer satisfaction, first, this paper established a customer satisfaction index system based on ACSI, combining with the industry characteristics of real estate. Second, a real estate satisfaction evaluation model was built based on BP neural network. Last, sample data would be learnt and practiced by MATLAB and a more efficient and practical methods would be provided for situation forecasting of real estate.%针对传统预测方法对房地产业顾客满意度预测效果差的问题，首先结合房地产业的行业特点，在ACSI的基础上建立顾客满意度指标体系。其次，构建基于BP神经网络的房地产业顾客满意度测评模型。最后，利用MATLAB软件对样本数据进行学习和训练，为房地产业顾客满意度测评提供一种更为有效和实用的方法。
基于BP神经网络的汽车起重机工作幅度计算%Computation of truck cranes＇ working range based on BP neural network
Institute of Scientific and Technical Information of China (English)
黄皓轩; 杨贯中
2012-01-01
汽车起重机由于其结构的特殊性和工作环境的复杂性,对工作幅度有比较精确的要求。在工作时,吊臂往往会发生形变。为了得出精确的工作幅度,计算相应的形变量是很有必要的。本文通过建立BP神经网络,利用实测数据,得出了较精确的形变量。为工作幅度的精确计算提供了保障。%The particularity of a truck crane＇s structure and complexity of its working environment demand high accuracy of working range.As an indispensable factor,deformation of the crane jib,occurring when a truck crane is in operation,influences the computation of working range.Thus,the value of deformation is required to be calculated precisely to meet the demand.In this paper,comparatively accurate values of deformation are acquired through putting measurements into BP neural network,guaranteeing accurate calculation of working range.
Institute of Scientific and Technical Information of China (English)
贾应炜
2014-01-01
At present, the cooperation in management platform data is much and miscelaneous, quality in daily use not to sum up the work of school enterprise cooperation effectively through data analysis; therefore, considering the advantages and disadvantages of the existing cooperation between college and enterprise management platform, try to use the characteristic of BP neural network, the accurate analysis and evaluation of the data, make some exploration to perfect cooperation between colege and enterprise management platform.%目前校企合作管理平台中的数据多而杂，在日常使用中不能有效通过数据分析来总结校企合作工作的质量；因此，本文在综合考虑现有校企合作管理平台中的优缺点，尝试利用BP神经网络特点，准确分析与评价各项数据，为校企合作管理平台的完善做出一些探索。
Institute of Scientific and Technical Information of China (English)
吴雪莲; 都洪基
2014-01-01
为了减少发电量随机性对电力系统的影响，需要对发电功率预测进行研究。通过分析影响光伏发电功率的因素，基于BP神经网络理论，在Matlab软件中建立预测模型，实现了对输出功率的短期预测，并给出了基于马尔科夫链的改进预测精度的方法。%In order to reduce impact of randomness generation capacity on power system, there is a need to study generation power forecast. Via analysis of factors of impact on photovoltaic generation power, this paper established the forecast model in Matlab based on BP neural network theory, realizing the short term forecast of output power and giving the method of improved forecast ac-curacy based on Markov chain.
Institute of Scientific and Technical Information of China (English)
徐海成
2011-01-01
In the change-condition calculation of turbo-charger set, the problem which refers to repeat characteristic calculating of compressor according to input data have made the whole calculating process complicated and low efficiency.Applying BP neural network to working out the functional relation in the characteristic map of compressor, and then introduces it into Excel for the change-condition characteristic calculating of turbo-charger set.A given example showed that the efficiency and universality of the Excel for characteristic calculating of compressor is remarkable, and that can be expanded to the other place where the characteristic calculating of compressor involved.%在涡轮增压机组变工况热力计算中,因为涉及到需要依据输入数据反复计算压气机特性参数的问题,使得整个计算过程复杂繁琐、效率低下.应用BP神经网络求出压气机特性曲线的函数关系,并将其引入到Excel中,可以实现机组的变工况热力计算.实例表明,该计算表格的查值效率较高、通用性较好,可推广至其它涉及到压气机特性计算的地方.
Institute of Scientific and Technical Information of China (English)
任强; 谢伟东
2012-01-01
Shock absorber is an important part of automotive suspension,it will direct influence the safety and comfort of a vehicle. Indicator diagram of shock absorber plays an important role in identifying whether it is qualified. At present, shape identification of the indicator diagram of shock absorber depends heavily on experience. The paper trained BP neural networks wilh MATLAB to realize automatic identification the defective products of shock absorber. The study has tremendous market value. [Ch,1 fig.2 tab.9 ref. ]%减振器是汽车悬架的重要组成部分,其性能直接影响整车的安全性和舒适性,减振器示功图是判断减振器是否合格的重要依据.目前,减振器示功图的类型识别都依赖人的经验.文章通过在MATLAB中训练BP神经网络,实现了减振器缺陷产品的自动识别,该研究具有巨大的市场价值.
Institute of Scientific and Technical Information of China (English)
盛译萱
2016-01-01
Driver-in-loop Simulation Platform is used to simulate actual road condition and carry on the simulation driving experi-ment. Driver's behavior data is collected in real time and transmits to computer to analyze. The basic idea is to use BP neural net-work algorithm which could classify the data from platform into different driving states and Discrete Fourier transform( DFT) method which could analyze driver's behavior through of steering angle and accelerator pedal to verify the amplitude-frequency characteristic of different driving behavior is related to driver's operation ability and safety of vehicle.%运用驾驶员在环仿真平台可以模拟实际道路环境，进行模拟驾驶试验，实时采集行为数据并将数据传输到计算机上分析。本文主要运用BP神经网络算法对在环仿真平台采集的数据进行驾驶状态分类，并根据离散傅里叶变换（ DFT）方法分析方向盘转角和油门踏板开度等数据从而对驾驶员行为进行分析。
Institute of Scientific and Technical Information of China (English)
彭源; 莫玉龙
2003-01-01
Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using theback projection method.
Institute of Scientific and Technical Information of China (English)
曲仕茹; 杨红红
2013-01-01
To improve the recognition rate and speed of the multi-target detection and tracking in the complex background, a tracking method based on neural network Kalman filter with correction mean square error estimation was proposed. Multi-target detection and tracking of the video sequence were achieved. In this method, first of all, the background was extracted accurately through the inter-frame difference method and multi -target detection was achieved combined with background subtraction method,the detection results were optimized utilizing morphological filtering. Then, Kalman_BP neural network filter was used to predict the position of the moving target. The estimation error of the Kalman filter caused by model changing and noise was mainly reduced with BP neural network, which made the predictive results more accurate. Finally, the fast matching of target was achievid via labeling different targets. Target chain was established by using the characteristics that little change of same goal centroid position and the boundary rectangle between the adjacent frames, which brought about the multi-target tracking. Simulation results show that the algorithm can not only track different scenarios targets, but also count the number of targets and display target trajectory rapidly and stably. Compared with the particle filter and other metheds, tracking is more smooth, thus the reliability of the tracking is improved.%针对在复杂环境下多目标检测与跟踪实时性差和准确率低的问题，提出了一种基于神经网络修正均方误差估计的卡尔曼滤波跟踪方法，实现视频序列的多目标跟踪。在该方法中，首先通过帧间差分法准确提取出背景，并结合背景消减法实现多目标的检测，应用形态学滤波对检测结果进行优化；然后利用Kalman_BP神经网络预测滤波器对运动目标的位置进行预测。BP神经网络的引入，主要是降低由于模型变化以及噪声等引起的Kalman滤
Performance of Neural Networks Methods In Intrusion Detection
Energy Technology Data Exchange (ETDEWEB)
Dao, V N; Vemuri, R
2001-07-09
By accurately profiling the users via their unique attributes, it is possible to view the intrusion detection problem as a classification of authorized users and intruders. This paper demonstrates that artificial neural network (ANN) techniques can be used to solve this classification problem. Furthermore, the paper compares the performance of three neural networks methods in classifying authorized users and intruders using synthetically generated data. The three methods are the gradient descent back propagation (BP) with momentum, the conjugate gradient BP, and the quasi-Newton BP.
Institute of Scientific and Technical Information of China (English)
刘建国; 安振涛; 张倩; 赵志宁
2014-01-01
通过气体采样分析，确定了弹药库房需要重点检测的有害气体种类，在此基础上构建了基于传感器阵列与粒子群优化 BP网络的气体检测系统；针对线性惯性权重调整的粒子群优化 BP神经网络算法的不足，提出了一种新的非线性惯性权重调整方法，既保证了算法在运行前期具有较快的搜索速度和全局搜索能力，又保证了后期具有较高的搜索精度；为了克服算法在运行后期的局部收敛，在算法运行过程中引入速度变异算子，使算法摆脱了易陷入局部最优点的束缚；最后，通过实验对气体检测系统的性能进行了检验。结果表明，该气体检测系统速度快、精度高，能够较好地实现对弹药库房有害气体的检测。%Through the analysis of the gas sample,the species of harmful gas was defined in the ammunition warehouse.The gas analysis system composed of sensor array and BP neural network was established.An advanced BP algorithm based on improved particle swarm opti-mization with non-linear adjustment of inertia weight was proposed by analyzing the shortcomings of BP algorithm using linear adjustment of inertia weight.The algorithm can not only maintain the characteristic of fast speed and global searching in the early convergence phase,but also keep a high precision in the later convergence phase.In order to escape from the local minimum basin of attraction of the later phase,a mutation operator of velocity is added to the Particle Swarm Optimization(PSO)algorithm.In the end,the performance of the algorithm was tested by the experiment.The experimental result indicates that the system has a rapid detection speed and a high precision.It is an effective method for harmful gas detection.
企业市场营销策略组合的BP神经网络%BP neural network model for market sale strategy combination
Institute of Scientific and Technical Information of China (English)
曾旗; 刘明明; 徐君
2008-01-01
针对企业如何采取有效的市场营销组合策略来提高顾客忠诚度的问题,提出了基于BP(back-propagation)神经网络算法的企业市场营销组合策略分析方法;建立了基于4Ps理论的营销组合策略影响因素函数;通过对不同的营销组合策略影响因素进行加权、量化、各层之间权值的调整和迭代运算,最终构建了满足预定误差要求的BP神经网络模型;通过对十种手机品牌顾客忠诚度的实际调查值与网络模拟值相比较,得出了BP神经网络算法具有良好的模拟性,在此基础上证明了该方法在企业进行市场营销组合策略选择时具有良好的预见性和实用性.
Institute of Scientific and Technical Information of China (English)
姚洪磊; 张彦
2014-01-01
杂的互联网售票系统网络，实验数据与实际系统风险评估值基本吻合。%Railway internet ticketing system had replaced the conventional ticket transaction method which was playing an important part in railway transportation production. As a result of the Internet-based character, railway internet ticketing system was facing several levels of security risks and threats such as overt aggressions and virus infections. Once the system was break down, a great negative impact would be brought to the society. Based on the threats referred, scientific methods and tools need to be used to analyze the threats vulnerability of the system; consequences caused by the security incidents should also be evaluated once the accidents occurred. Protection countermeasures and corrective measures against threats should be proposed to control and mitigate information security risks which should bring the threats to an acceptable level. Artificial neural networks (ANN) has intelligent character such as autonomously access knowledge which can better deal with uncertainty and nonlinear problems, and it had been wildly applied in information security risk assessment in many industries. Compared with other ANN, the BP neural network had a good nonlinear mapping ability including self-learning and adaptive capacities. First, using the 3-layer neural network can approximate any nonlinear arbitrary precision continuous functions, making it suitable for solving complex problems. Second, the output can be automatically extracted "Reasonable Rules" between output data during the training process, and the learning content can adaptively memory the rules on the weights in the network. As a result, an evaluation mode was proposed by using artificial neural network based on BP model in view of safety menace of railway internet ticketing system, the major safety menaces of internet ticketing system were used as the training samples; an experiment was conducted by using the trained
Institute of Scientific and Technical Information of China (English)
周复旦; 赵长胜; 高卫东
2011-01-01
随着我国各项建设对永资源的需求越来越大,导致由地下水开采而引起的沉降问题成为当前研究的热点课题.本文对某矿区采水地面沉降进行了模型设计,通过对部分实测数据的训练,优选出该模型的网络结构和网络参数,并且用Matlab软件编程实现对其他监测点的计算和预测.通过研究表明本文所建立的BP神经网络模型能较准确反映采水地面沉降的规律,同时也能较准确地预测地下水开采引起的地面沉降.%With the growing water demand in China domestic construction, subsidence caused by groundwater over-exploitation has become a topic of current research. This article made model design in light of water area' s subsidence. By training some monitoring points' measured data, it selected the network structure and parameters of this model. By using the models, it calculated and predicted other monitoring points with the help of Matlab software. Research results indicated that the BP neural network model could accurately reflect the spatial law of water area' s subsidence and predict the subsidence caused by underground mining precisely.
Muskmelon Disease Forecasting Based on Optimized BP Neural Network%基于优化神经网络的温室厚皮甜瓜病害预测
Institute of Scientific and Technical Information of China (English)
王福顺; 孙小华
2012-01-01
在温室环境中,厚皮甜瓜较易感染一些病害,而传统的病害预测模型收敛速度慢,易在局部局限在极小值,为准确预测温室厚皮甜瓜病害,在BP神经网络的基础上进行优化,引入了遗传算法,在全局最优解的附近进行局部搜索,以遗传算法的全局搜索能力克服了传统神经网络的局部极小值问题与收敛速度缺陷.经以Matlab对试验数据进行仿真分析,证实引入遗传优化算法进行温室厚皮甜瓜病害预测误差显著减小,取得了较理想的拟合结果.%In the environment of greenhouse,muskmelon is often infected by diseases. The traditional neural network algorithm converges slowly,easy to limited to the minimum in the local convergence. In this paper,genetic algorithm was led into BP network to overcome the defects by its global search ability. The Matlab simulation analysis of test data confirmed that the introduction of genetic optimization algorithm significantly reduced the prediction errors of greenhouse muskmelon disease,and obtained better fitting results.
Institute of Scientific and Technical Information of China (English)
曹勇; 李殿生; 朱景川; 刘勇; 来忠红
2011-01-01
对不同时效处理的3J33B马氏体时效钢进行硬度测试,获得了时效工艺（温度、时间）、硬度参数数据。利用BP人工神经网络建立起其关系网络模型。结果表明,所建立的网络可以很好地反映出材料的时效工艺-时效硬度之间的关系,网络模型可以用来预测不同时效条件下3J33B马氏体时效钢的时效硬度,并且利用粒子群优化,对3J33B马氏体时效钢的时效工艺进行优化,对实际生产具有有效的指导作用。%Parameters of processing（aging temperature,time） and aged hardness of maraging steel were obtained through mechanical properties examination,and their relationship network model was built by BP artificial neural network.The results show that the built model can reflect the relationships between the processing and the aged hardness very well and is certainly accurate.It can be used for predicting the properties of 3J33B steel under different aging process.Meanwhile,the optimized aging temperature and time can be obtained with particle swarm optimization.The model can serve as a guide for the aging treatment of maraging steel.
MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Ge Guangying; Chen Lili; Xu Jianjian
2005-01-01
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.
Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network
Institute of Scientific and Technical Information of China (English)
WU Xiao-guang; SHI Zhong-kun
2006-01-01
The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.
Trimaran Resistance Artificial Neural Network
2011-01-01
11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to
A Study on Turbo-rotor Multi-fault Diagnosis Based on a Neural Network
Institute of Scientific and Technical Information of China (English)
SUN Shou-qun; ZHAO San-xing; ZHANG Wei; CHANG Xin-long
2003-01-01
The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine. As it has many excellent qualities, the neural network might be used to solve the problems of multi-fault diagnosis of a turbo-rotor system. First, the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing. Then, the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propagation) neural network. According to the BP neural network problems, the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated. The results indicated that the parallel BP neural network method could solve the turbo-rotor multi-fault diagnosis problems.
[Artificial neural networks in Neurosciences].
Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María
2011-11-01
This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.
Institute of Scientific and Technical Information of China (English)
李红连
2011-01-01
It is difficult to do dead reckon ( DR) position' zero update' for vehicle GPS/DR integrated navigation system in case of GPS signal blockage,a new DR position algorithm is put forward based on the back-propagation (BP) neural network. Firstly, DR position data is dc-noised with stationary wavelet transformation (SWT) model square root soft-threshold de-noising algorithm,and the vehicle precise position data is acquired with extended Kalman filter(EKF) data fusion algorithm based on SWT during the presence of GPS signal. The algorithm compares the two position data at different SWT decomposition level, reconstructs the SWT coefficients and the DR position error data is acquired. A BP network model to mimic DR position error property is trained with back-propagation algorithm. To improve generalization of the BP network , the back-propagation algorithm is improved by Bayesian regularization (BR) theory in this paper. During CPS outages, DR position error data is predicted with the model, and the algorithm provides precise position for the vehicle GPS/DR integrated navigation system through DR position error data updating DR position data in complex running trajectory. The simulation results show that the algorithm is available for the vehicle GPS/DR integrated navigation system.%针对车辆GPS/DR组合导航系统在GPS信号被遮挡时无法完成DR"零点更新"的问题,提出了基于BP神经网络的DR位置误差预测模型来解决该问题.在GPS有效时,该算法采用基于平稳小波变换的扩展卡尔曼滤波器对GPS/DR信号进行数据融合得到车辆实时的精确位置,与经平稳小波变换软阈值模平方去噪法处理的DR位置数据进行平稳小波多尺度比较获得DR位置误差;然后用BP神经网络建证DR位置误差预测模型,为了提高所用网络的泛化能力.采用了贝叶斯正则化规则训练网络.在GPS失效时,利用已建立的预测模型预测DR位置误差来修复DR位置数据,实现车辆行
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Three-layer Adaptive Back-Propagation Neural Networks(TABPNN) are employed for the demodulation of spread spectrum signals in a multiple-access environment. A configuration employing three-layer adaptive Back-propagation neural networks is put forward for the demodulation of spread-spectrum signals in asynchronous Gaussian channels. The theoretical arguments and practical performance based on the neural networks are analyzed. The results show that whether the resistance to the multiple access interference or the robust to near-far effects, the proposed detector significantly outperforms not only the conventional detector but also the BP neural networks detector and is comparable to the optimum detector.
Directory of Open Access Journals (Sweden)
J. Reyes-Reyes
2000-01-01
Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.
Leg Amputees Pattern Recognition with BP Neural Network%BP神经网络大腿截肢者运动模式识别
Institute of Scientific and Technical Information of China (English)
刘磊; 杨鹏; 刘作军; 耿艳利
2014-01-01
在假肢运动优化控制的研究中，针对动力型假肢控制方面存在的运动模式识别准确性差的问题，搭建人体下肢运动信息系统获取下肢髋关节角速度信号和加速度信号。建立基于BP神经网络的大腿截肢者运动识别模型。研究了建模过程中输入输出数据预处理、网络结构设计、训练模式选择等问题。改进模型能有效识别平地行走、上楼、下楼、上坡和下坡5种运动模式，正确识别率达到了90.4%，已具备一定的实用性。%Lower limb amputation significantly affects the quality of the leg amputee's daily life. Recent advance-ments in electromechanical actuators have propelled the recent development of powered artificial legs. Accurately rec-ognizing the leg amputee's locomotion intent is required in order to realize the smooth and seamless control of prosthet-ic legs. The approach infers amputee's intents of upslope, downgrade, stairs ascent, stairs descent or level-ground walking without the need for instrumentation of the sound-side leg. Specifically, the intent recognizer utilizes the fea-tures extracted from accelerometer and gyroscope. The preprocessing of input and output data, design of network structure, training mode selection and other aspects were analyzed. This paper demonstrates via experiments the ef-fectiveness of the approach.
Long-term Runoff Forecasting with B-P Neural Network Model%B-P神经网络在径流长期预测中的应用
Institute of Scientific and Technical Information of China (English)
蓝永超; 康尔泗; 徐中民; 陈仁升; 张济世
2001-01-01
Artificial neural network (ANN), as a computing model possessing high-nonlinear mapping ability, has been widely applied in lots of field, for example mode recognizing, automatal control and so on. In the numerical values forecast, this computing method can be used for predicting by means of studying sample data and it need not predefining samples model. The forecasting for runoff variation trend is one of the important matters in water resource research field, and is at the holding position in long-term programming about water resource utilization. At present, the statistical models combining with experience indexes are mainly used for establishing predicting equation in long-term forecasting for runoff. These models basically belong to linear models, whose forecasting effect is not very satisfactory for forecasting largely fluctuating runoff change process. Back-Propagation model in ANN, B-P model for short, composed by nonlinear transform cell possess better nonlinear mapping ability, its structure is simple and its capability is favorable. So B-P neural network net is used for predicting runoff variation trends. The Longyangxia Key Water Control System is located on the upper Yellow River in the northeastern Qinghai-Tibet Plateau, 1 688 km down from the source of the Yellow River. As the first of the stairstep power station along the Longyangxia Gorge to Qingtongxia Gorge river section, its reservoir can hold 24.7 billion m3 of water has been playing an very important role in providing power, storing flood and resisting ice running and irrigating, etc. in the northwestern China. The upper Yellow River basin above Longyangxia Gorge is located in the northeastern Qinghai-Tibet Plateau, between 95°50′～102°52′E, 32°20′～ 36°30′N, with a water collection area of 13.14×104 km2. The Tangnag Hydrometric Station, upstream about 110 km, is the monitoring station of runoff into the Longyangxia Reservoir. Runoff has been observing since 1956 and there
Institute of Scientific and Technical Information of China (English)
段巍; 王璋奇; 万书亭
2009-01-01
The reliability reverse-solution-seeking design of steam turbine blades aims at determining the design parameters of blades with unknown probability to meet a given reliability requirement.In the light of the blade function being a random variable implicit function,a reliability reverse-solution-seeking design method was presented based on finite element method,BP neural network and decomposition techniques.It combined the finite element method with BP neural network to establish an approximate analytic expression showing the relationship between the performance function and the random input variables.By employing the decomposition techniques,the overall optimization problem involving the solution-seeking of random design parameters was decomposed into a main problem and sub-problems.By way of the sub-problems,the standard optimization toolbox was used directly to obtain the reliability indexes,and the decomposition and iterative techniques were employed to seek solutions to the main problem,thus obtaining the sensitivity of the random design parameters and target reliability indexes to various random variables.With the equal and straight blades of a steam turbine on a test rig serving as an example,the concrete application process of the method was expounded.The method features a simple mathematical expression and can be directly used in standard optimization programs.It successfully solved the reliability reverse-solution-seeking design problem of blades under an implicit function,thus enjoying a relatively good application value for engineering projects.%汽轮机叶片可靠性反求设计旨在确定叶片未知概率设计参数以满足给定的可靠度要求.针对叶片功能函数为随机变量隐性函数的情况,提出了基于有限元、BP神经网络和分解技术的可靠性反求设计方法,该方法将有限元和BP神经网络相结合以构造功能函数与随机输入变量之间的近似解析表达式,运用分解技术,将求解随机
Institute of Scientific and Technical Information of China (English)
王金浩; 李小娟; 孙永华; 李文彬
2016-01-01
During the voyage , the fishing vessel is in a potential threat because of its own structure or the influence of sea surface wind and waves . In order to study the risk of fishing vessels in the marine environment , based on the BP neural network algorithm , the fishing boats early warning model which is composed of 6 early warning indicators :fishing vessel tonnage , engine power , material , fishing vessels age , sea breeze level , wave level , were evaluated and then the sea operations risk level for fishing vessels were finally determined .400 fishing vessel accident cases were selected to develop the risk early warning model and the model was verified through classification of multiple levels for the training samples .The results of early warning and the actual results of statistical calculation showed , the correct rate remained at 79 .76%-83 .62%, in which when the training sample number was 0 .75 times as the number of test samples , the accuracy of the model is highest .In conclusion , the assessment results of fishing vessel risk early warning model based on BP neural network was basically consistent with the actual condition of accident , which could provide guarantee for safe navigation .%渔船在海上航行时由于船体自身结构或者海面风浪等不利因素的影响，时常处于潜在的威胁当中。为了研究渔船在海洋环境中可能会遭受的风险，采用基于BP神经网络算法，对渔船吨位、发动机功率、渔船材质、渔船船龄以及渔船所处海面风等级、海面浪等级等6个预警指标要素构成的渔船预警模型进行评估，最终确定渔船在海上航行时的风险等级。在构建风险预警模型中使用了400个渔船事故案例，将训练样本按照数量划分为多个级别进行验证。预警模型结果与实际值比较显示，模型的正确率为79．76％～83．62％，其中在训练样本数为测试样本数的0．75倍时，模型精度最高。研究表明
Institute of Scientific and Technical Information of China (English)
李进贤; 莫文宾; 唐金兰
2011-01-01
固体火箭发动机中,药柱的结构完整性直接关系到发动机的结构完整性和可靠性,而推进剂的力学性能对保持药柱结构完整性起着重要作用,也是决定推进剂寿命的重要指标.为了预估固体推进剂的力学性能,提高系统的可靠性,将遗传算法和神经网络相结合,建立了预估固体推进剂力学性能的遗传神经网络(GA-BP)模型.利用模型预测了某固体推进剂在不同温度、湿度和时间下的抗拉强度、延伸率、弹性模量变化情况,并与试验结果进行了比较.结果表明,模型预估精度高,泛化能力强,仿真计算与试验在结果上有很好的一致性.从而为固体火箭发动机的结构完整性研究提供可靠依据.%Solid propellant grain structural integrity influences the structural integrity and reliability of solid rocket motor (SRM). Mechanical property of solid propellant plays an important role in grain structural integrity, which is critical criterion of solid propellant life. In order to predict mechanical property of solid propellant, a new mechanical property prediction model for solid propollant was established by means of combination of 8enetic algorithm with neural network (GA-BP). Using above model, the mechanical proporty of a solid propellant in conditions of different ternperature, humidity and time was predicted and compared with experiment results. The comparison results show high precision of the model and strong ability of generalization and with good consistency between prediction of model and experiment. The investigation provides reliable assistance for structural integrity research of SRM.
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...
Institute of Scientific and Technical Information of China (English)
韩震; 赵宁
2012-01-01
Using the LM-BP neural network and choosing the sea surface temperature, longitude, latitude and depth obtained from Argo data in 2007 as input parameters, the seawater temperature model of the Northwest Pacific Ocean was built. Using the root-mean-square error( RMSE) and the Pearson' s correlation coefficient (R) as test indices, the model was evaluated by the data in the period 2008 ~ 2009. The results were that the RMSE was 0.714 0 ℃ and R was 0.996 8 in 2008. The RMSE was 0.761 5 ℃ and R was 0.996 5 in 2009. lt shown this seawater temperature model was.%以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型.将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验.检验结果为:2008年均方根误差为0.7140℃,Pearson相关性系数为0.9968;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965.表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的.
Institute of Scientific and Technical Information of China (English)
李正学; 吴微; 高维东
2003-01-01
Some widely-used technical indexes of stock analysis are introduced as input of BP neural networks for the prediction of ups and downs of stock market, and better accuracy of prediction is achieved. A jump training strategy and three varying training ratio methods are used to accelerate the training iteration. An online prediction strategy is applied to monitor the training iteration procedure. The ratio of central distances of prediction examples is defined, in order to locate the un-stable prediction examples.%本文使用股市分析中常用的一些技术指标构造BP网络的输入样本向量,在此基础上,对沪市股指的涨跌进行了预测.数值实验结果表明,该方法能够提高网络预测的正确率.使用跳跃学习及三种变学习率、批方式的学习算法对BP网络进行了训练,节省了预测时间.运用"在线预测"的方法对预测过程进行了跟踪.针对预测样本在预测性能及预测结果方面存在的差异,引入预测样本中心距离比的概念对其进行简单的划分,得到一些富有启发性的结果.
Institute of Scientific and Technical Information of China (English)
向平; 张蒙; 张智; 张南
2012-01-01
At present there are scant studies on the impact factors of hourly water demand in the water demand prediction. This work investigated the main impact factors for the water consumption in different hours through the analysis on the correlation between the different impact factors and the hourly water consumption. The period-divided prediction model was established on the basis of three divided periods of one day. And BP neural network was used to predict. Precision index was indicated with MAPE value. Case analysis results show that for the established period-divided water consumption prediction model, MAPE values are all within 5%, which indicates a high prediction accuracy, and the water supply system optimization scheduling requirements can be met, providing a simple and feasible approach and method for the urban hourly water consumption prediction.%针对目前时用水量预测模型中对时用水量影响因素分析研究较少的问题,通过分析各种时用水量影响因素与时用水量之间的相关性,筛选出时用水量的主要影响因子;通过分类将1d划分为3个时段,建立分时段用水量模型.采用BP神经网络预测,精度指标采用平均绝对百分比误差(MAPE)表示.实例分析结果表明:模型预测MAPE均在5％以内,预测精度较高,满足供水系统优化调度的要求,为城市时用水量预测提供一种简单可行的思路和方法.
Institute of Scientific and Technical Information of China (English)
曾祥燕; 赵良忠; 蒋盛岩
2013-01-01
为提高槐花加工过程中芦丁的得率,以槐花为原料提取芦丁,采用正交试验设计收集试验数据,利用BP神经网络的自学习能力,通过仿真和评估,优化其提取工艺参数.结果表明:BP神经网络与正交实验方法相结合,能更好的利用已知信息,最佳仿真提取条件为:pH 9.50,乙醇体积分数60％,时间1.45 h,料液比1∶21,运用该提取条件用于实际研究中,测得实际得率为13.51％,试验结果优于正交试验,获得较为理想的目标值.%BP Neural Network (BPNN) combined with orthogonal array design was applied to optimize the extraction technology of rutin from Sophora japonica. The orthogonal testing data was used by applying BPNN. The experimental result showed that BPNN helper to increase the productivity of rutin. The optimized extraction conditions of rutin from Sophora japonica were as follows:pH 9.50,60% as the alcohol concentration, 1.45 h as the extraction time,and 1: 21 as the material/liquid ratio. The experiment was accomplished under optimized extraction conditions. The yield of rutin was 13.51%. In conclusion,BPNN provided a good technical basis for industrialization of the production of rutin.
Institute of Scientific and Technical Information of China (English)
鲍珍珍; 朱沛
2013-01-01
According to the research results of the performance evaluation,combining the current situation of our nation’s third-party logistic companies with the chosen indicators,this paper aims to evaluate and analyze the comprehensive strength of the third-party logistic companies based on the BP neural network and AHP.Then we selected relevant data from several logistic companies and used them to have the evaluation done,which proves that our method is pragmatic and scientific and is able to provide important reference for the selection of the third-party logistic companies.%文中根据绩效评价体系的研究成果，结合我国第三方物流企业的现状，选取了适当的评价指标，并采用基于BP神经网络和层次分析法的评价方法，对第三方物流企业的综合实力进行评价分析。然后，收集了某几家物流企业的相关数据，对具体物流企业的综合实力进行评价，说明文中的评价方法具有实用性和科学性，对第三方物流企业的选择提供重要的参考。
Institute of Scientific and Technical Information of China (English)
熊晶; 吴云; 林剑
2013-01-01
On the basis of the F2 layer peak electron density ( NmF2) from University Corporation for Atmospheric Research (UCAR) , we constructed a Back Propogation(BP) artificial neural network (ANN) in order to detect pre-earthquake anomalies for the first time. The ANN provides NmF2 model value with five parameters: DOY, local time(LT) , longitude ( LON ) , latitude ( LAT) and solar activity index of F10. 7 (FLUX). We compare the model value with observations during the Wenchuan earthquake. It is found that NmF2 around the forthcoming epicenter decreased remarkably in the afternoon period of day 6 -4 before the earthquake, but enhanced day 3 -2 before the earthquake.%基于UCAR公布的电离层F2层最大电子密度数据NmF2,利用人工神经网络技术,构建局部地区NmF2模型.以年积日DOY、当地时LT、经度LON、纬度LAT和F10.7太阳活动指数FLUX为网络输入,以NmF2为网络输出,提供磁平静期NmF2模型值作为参考背景,通过模型值与观测值的比较,发现2008年5月12日汶川7.9级地震前震中附近上空NmF2在震前第6～4天(6-8日)减小约30％,震前第3～2天(9-10日)明显增大约40％.
Institute of Scientific and Technical Information of China (English)
李君波; 刘旺开
2014-01-01
针对高压热动力试验台测控系统需要控制的参数多，控制任务要求复杂，控制精度要求高，且各参数间存在耦合的情况，提出了基于 BP＋RBF神经网络 PID的智能控制的方法；应用智能控制的方法解决传统的 PID控制无法解决的问题；实际应用表明基于 BP神经网络整定的 PID控制器具有较好的自学习和自适应性，能保证控制精度等要求，控制效果比较令人满意。%As there are many parameters to be controlled in the Highpressure Thermodynamics Test Unit,the control task is demanding, and the requirement of the control precision is high.And there is a complicated coupling between parameters.In view of these difficulties,a method of intelligent control based on BP + RBF neural network and PID is Proposed.The intelligent control method is used to solve the problem that can not be solved by the traditional PID control method.In practical application the system has met the specifications require-ments perfectly,and achieved excellent controlling effect,though the mathematical model of the control object is complicated.
Institute of Scientific and Technical Information of China (English)
任向辉; 王运兵; 崔建新; 李嘉; 李松伟
2009-01-01
The sticky plates of three colors were made out by water-soluble adhesive prepared with carboxymethylcellulose sodium and glycerine as main raw materials, and the field experiment of trapping thrips was carried out in one place of a vegetable garden by different adhesives. Finally the data of this experiment as the training samples and the forecasting samples were analyzed by using BP neural network model. The results showed that the attrahent added had the most weight to the number of entrapped Thrips alliorum on the field, and the forecast results of model were consistent with the actual results perfectly. Generally, the bigger blue sticky plates had the best effect of trapping Thrips alliorum. The predictive value of samples by using this model was accordant with the observed value.%利用羧甲基纤维素钠和甘油为主要原料配制的水溶性胶黏着剂制作3种颜色粘板,在露天菜地的同一处地点进行田间蓟马诱捕测试.将测试结果作为BP神经网络模型的训练样本与预测样本进行建模分析,结果表明,黏合剂中诱虫物质的添加因素对葱蓟马田间诱捕量的影响权重最大,蓝色大型粘板诱捕蓟马效果最好,模型对预测样本的预测值与实际观测值基本吻合.
A Trust Evaluation Model for C2C E-Commerce Based on BP Neural Network%基于BP神经网络的C2C电子商务信任度评价模型
Institute of Scientific and Technical Information of China (English)
胡伟雄; 姜政军
2012-01-01
从卖家、网站、外部环境、网上信任等方面构建信任度评价指标体系；将影响网上信任的因素作为输入，将信任度综合得分作为输出，然后，运用BP神经网络技术，从买家的角度，构建一个C2C电子商务信任度评价模型。从实验来看，训练样本和检验样本的平均误差率和标准差均较低，模型的稳定性较好。因此，以此构建的C2C电子商务信任模型有很重要的价值，可以对信任度进行较为准确有效的评估。%First, the authors build the indicators according to the seller, the website and the external environment. Meanwhile, the factors affecting online trust are treated as input, and the trust composite score are treated as the output value. Then, the authors ap- plied BP neural network technology to build a trust evaluation model for C2C e-commerce from the perspective of the buyers. According to the experiment, the standard deviation and the average error rate of the training samples and test samples are low. Also, the stability of the model is well. Therefore, this C2C e-commerce trust model has very important value and can be used to assess trust accurately and effectively.
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Chinese word sense disambiguation based on neural networks
Institute of Scientific and Technical Information of China (English)
LIU Ting; LU Zhi-mao; LANG Jun; LI Sheng
2005-01-01
The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to ( - M, + N). The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of M and N affect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90. 31% ,and 89. 62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.
Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks
Directory of Open Access Journals (Sweden)
Reza K. Moghadas
2008-01-01
Full Text Available Efficient neural networks models are trained to predict the maximum deflection of two-way on two-way grids with variable geometrical parameters (span and height as well as cross-sectional areas of the element groups. Backpropagation (BP and Radial Basis Function (RBF neural networks are employed for the mentioned purpose. The inputs of the neural networks are the length of the spans, L, the height, h and cross-sectional areas of the all groups, A and the outputs are maximum deflections of the corresponding double layer grids, respectively. The numerical results indicate that the RBF neural network is better than BP in terms of training time and performance generality.
Development of HT-BP nueral network system for the identification of well test interpretation model
Energy Technology Data Exchange (ETDEWEB)
Sung, W.; Hanyang, U.; Yoo, I. [and others
1995-12-31
The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquart method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.
Institute of Scientific and Technical Information of China (English)
陈忠
2012-01-01
风速预测对于风力发电并网调度至关重要.基于BP神经网络建立了风速预测模型,并从BP算法及遗传算法自身特点出发,针对BP网络结构确定困难、收敛速度慢等问题,提出创建多种群遗传算法,实现对BP神经网络的结构和权值初始值的同步优化.通过具体算例表明,经优化后的BP算法的收敛步数和计算时间明显减少,预测精度更高,网络整体性能有了显著提高.%Wind speed forecastion is very important to dispatch wind power grid connected. A wind speed prediction model based on BP neural network is established; and from the characteristics of BP algorithm and genetic algorithm , considered network structures hard to certain and slow convergence speed ,a multi-population genetic algorithm was proposed to optimize the structure and the initial weights synchronously of BP networks, Through practical examples shows that the convergence steps and computing time of optimized BP algorithm has significantly decreased ,and a better prediction accuracy. The overall performance of the network has been remarkably improved.
Institute of Scientific and Technical Information of China (English)
李永亮; 张怀清; 林辉
2012-01-01
利用便携式ASD野外光谱辐射仪对杉木冠层叶片光谱进行测定,同时以分光光度法对叶片叶绿素含量进行提取.样本经均值处理、平滑处理和微分处理后,进行红边参数提取.对11个红边参数以PCA方法进行降维,将得到的前7个主成分得分作为网络输入参数,叶绿素含量作为网络输出参数,以遗传算法(GA)优化网络初始权值阈值,建立隐含层神经元数分别为4,6,8,10,12和14的6种单隐层BP神经网络模型.以R2,RMSE和相对误差作为模型精度检验标准,结果表明:6种模型预测精度均可达到92.0％以上,其中隐含层神经元数为10时,预测精度最高,可达97.372％.说明此种模型可对杉木冠层叶片叶绿素含量进行高精度估算.%High-precision estimation model of arbor canopy chlorophyll content is important to forestry and ecology. The spectral reflectance of canopy was measured by ASD FieldSpec and the chlorophyll content was measured by spectrophotometry at the same time. The sample data were pretreated by the methods of mean, smoothing and derivative, and then the red edge parameters of samples were extracted from the pretreated spectra data. The eleven red edge parameters were analyzed with principal component analysis ( PCA). The anterior 7 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer which had four, six, eight, ten, twelve or fourteen neurons, while the chlorophyll content was used as the output variables of BP-ANN, and then the three layers BP-ANN discrimination model was built. Weight value and threshold value of this model were optimized by using genetic algorithm. The fitness between the predicted value and the measured value was tested by the determination coefficient, the lowest root mean-square error and the average relative error. The results show that the precisions of six models are all above 92. 0% and the
Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo
2003-01-01
In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).
Logic Mining Using Neural Networks
Sathasivam, Saratha
2008-01-01
Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Data mining methods are important in the management of complex systems. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Neural networks have been successfully applied in wide range of supervised and unsupervised learning applications. Neural network methods are not commonly used for data mining tasks, because they often produce incomprehensible models, and require long training times. One way in which the collective properties of a neural network may be used to implement a computational task is by way of the concept of energy minimization. The Hopfield network is well-known example of such an approach. The Hopfield network is useful as content addressable memory or an analog computer for s...
Institute of Scientific and Technical Information of China (English)
邹修国; 丁为民; 刘德营; 赵三琴
2013-01-01
. The experimental result showed that the Krawtchouk moment has the highest recognition rate. It can be used for the extraction of rice planthopper feature values in the real-time system. This study focused on the search of invariant moments to extract good feature values, but the use of a BP neural network classification resulted in a recognition rate of sogatella furcifera and nilaparvata lugens that was not very high. The identification of sogatella furcifera and nilaparvata lugens was worse than that of the small brown planthoppers. It meant that recognition of two kinds of planthoppers based on a BP neural network needs further study.%针对稻飞虱远程实时识别采集图像质量不高的问题，研究了基于不变矩提取形状特征值对稻飞虱进行分类。采用自行设计的拍摄装置采集稻飞虱图像，进行灰度化后用大津法二值化，再用数学形态学滤波；对二值图像采用Hu矩、改进Hu矩、Zernike矩和Krawtchouk矩4种不变矩分别提取特征值，再用BP神经网络进行训练和测试，以此检测4种矩的提取效果。试验用Matlab2008验证算法，对白背飞虱、褐飞虱和灰飞虱共300个样本进行了训练和测试，结果表明Krawtchouk矩提取稻飞虱图像形状特征值的识别率最高，总体达到了91.7%。该文可为大田中现场识别稻飞虱提供参考。
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
Institute of Scientific and Technical Information of China (English)
吴建昱; 何小荣
2002-01-01
Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.
Institute of Scientific and Technical Information of China (English)
魏明桦; 郑金贵
2014-01-01
An improved BP neural network model is proposed to improve the precision of the prediction of agricultural products. Firstly, the factors of price fluctuation of agricultural products are gotten through the qualitative analysis and then use the MIV method to choose the strong influent factors as the input nodes of a neural network. Find the optimal structure of BP network through the improved learning algorithm, and then use the improved model to realize the agricultural high precision simulation of the product price.%为了提高农产品价格预测精度，提出一种改进的 BP 神经网络模型。先通过定性分析得到影响农产品价格波动的因子，然后采用MIV方法选择强影响力的因子作为神经网络输入节点。并采用改进的算法进行学习，寻找最优的BP网络结构。利用改进后的模型，实现了农产品价格的高精度仿真。
A neural network method to evaluate consolidation coefficient
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Many methods to calculate the consolidation coefficient of soil depend on judgment of testing curves of consolidation,and the calculation result is influenced by artificial factors. In this work, based on the main principle of back propagation neural network, a neural network model to determine the consolidation coefficient is established. The essence of the method is to simulate a serial of compression ratio and time factor curves because the neural network is able to process the nonlinear problems. It is demonstrated that this BP model has high precision and fast convergence. Such method avoids artificial influence factor successfully and is adapted to computer processing.
FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM
Institute of Scientific and Technical Information of China (English)
Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang
2003-01-01
The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.
Artificial Neural Network Analysis System
2007-11-02
Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis
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.
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification
Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun
2016-12-01
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.
Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun
2016-12-01
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.
Institute of Scientific and Technical Information of China (English)
陈鑫; 翁卫卫; 吴敏; 曹卫华
2013-01-01
Efficient calculation and prediction of the coke consumption of the sintering process are pivotal to optimize the sintering process to reduce the coke consumption. A reasonable coke consumption indicator is defined and its calculation mode is developed. Then, the factors affecting the consumption of coke are determined through the integrated use of the mechanism analysis and gray correlation analysis method. At last, a CPSO-BPNN predictive model is developed, in which the particle swarm optimization combined with chaotic local search is used to optimize the initial connection weights and translate scaling factors of the BP neural network model. The simulation result demonstrates that CPSO-BPNN provides an effective way to predict the coke consumption, which serves as a basis to optimize sintering process and reduce the coke consumption.%有效地计算和预测烧结碳耗，是有针对性地优化烧结生产以降低烧结碳耗的关键前提。文章首先提出了烧结过程碳耗指标--综合焦比并给出合理可行的烧结过程碳耗指标计算模型；再次，结合机理进行分析和灰色关联度分析方法，确定了影响碳耗的主要因素；最后，建立了基于混沌局部搜索粒子群算法的烧结碳耗BP神经网络预测模型(CPSO-BPNN)，用带混沌局部搜索的粒子群算法对烧结碳耗BP神经网络预测模型的初始网络权值、阈值进行寻优，以克服BP算法参数寻优时陷入局部极小的缺点。仿真结果表明，CPSO-BPNN可有效地对烧结碳耗进行预测，为优化烧结生产过程，降低烧结碳耗奠定了基础。
Institute of Scientific and Technical Information of China (English)
戚大伟; 牟洪波
2013-01-01
X-ray was adopted as a measure method for wood nondestructive testing.Wood defects were identified by testing X-ray transmitted intensity through the wood.The detected defects were conducted by image processing.Wood defect images were first converted into grayscale images,and then into binary images.With the threshold values determined by some known experience,the wood defects were separated from the background and the clear wood defects edge was extracted.A group of parameters describing shape features were obtained by extending Hu invariant moments.Those parameters not only have translation invariance,scaling invariance and rotation invariance,but also have lower computational complexity.The feature parameters were input into BP (back propagation) neural network after preprocessing,and then the wood defects were recognized.The experimental results show that the recognition ratio is above 86％,indicating that this method is successful for detection and classification of wood defects.This study offers a new method for automatic detection of wood defects.%采用X射线作为检测手段,对木材进行无损检测,通过检测透过木材的射线强度来断定检测木材是否存在缺陷.对得到的木材缺陷进行图像处理,将木材缺陷图像转化为灰度图像,再把灰度图像转换为二值图像,根据经验选择相应的阈值,提取出清晰的木材缺陷边缘,把木材缺陷部位从背景中分离出来,完成木材缺陷图像分割.对Hu提出的区域不变矩进行扩展,得到一组新的描述形状特征的参数,这些参数具有平移、缩放和旋转不变性,并且具有较低的计算复杂性.将这些特征参数预处理后输入BP神经网络,对木材缺陷进行检测,检测准确率达到86％以上,试验结果表明此方法的可行性,为实现木材缺陷的自动检测提供了新的途径.
Institute of Scientific and Technical Information of China (English)
林婵; 王起峰; 朱良山
2013-01-01
湿球温度是电力工程中常用的气象设计参数,而目前气象站安装的地面气象自动观测设备中无湿球温度观测工具,且已有的湿球温度计算方法存在不足.为了满足工程设计需要,分析了湿球温度与干球温度、相对湿度、大气压强及平均风速等4个气象参数的非线性关系,建立了基于LM-BP神经网络的湿球温度计算模型,并将其应用于潍坊气象站湿球温度计算中.结果表明,该模型计算精度较高,且较为合理地反映了湿球温度与干球温度等影响因子之间复杂的非线性关系.%The wet-bulb temperature is a common weather parameter in power engineering design, but at present most ground automatic weather observation equipments installed in the weather station do not include the wet-bulb temperature observation equipment. The common wet-bulb temperature calculation methods have shortcomings. In order to meet the needs in engineering design, this paper analyzes the non-linear relationship between wet-bulb temperature and four meteorological parameters, which include dry-bulb temperature, relative humidity, atmospheric pressure and wind speed. And then it established a wet-bulb temperature calculation model based on Levenberg-Marquardt BP neural network. Finally, this model is applied to calculate wet-bulb temperature in Weifang weather station. The results show that the proposed model has high precision and it can well reflect the non-linear relationship between wet-bulb temperature and dry-bulb temperature.
Institute of Scientific and Technical Information of China (English)
蒋海燕; 施小斌; 杨小秋; 石红才
2012-01-01
In order to obtain thermal conductivity of rocks at the depth where no core is available, we build ua prediction model for thermal conductivity based on BP neural networks with sonic, density, neutron porosity, resistivity, gamma ray as input. The prediction model needs short estimating time without any more lithological composition data, therefore it has more and wider applications than the empirical formula only influenced by one or several physical parameters. The test results from the test samples and 1144A, 1146A, 1148A well logs show that the error given by our model is less than the maximum permissible error of thermal conductivity measurement under laboratory conditions. This model provides a new way for obtaining thermal conductivity of rocks at the depth where has no core but has the related geophysical well logs.%为了获取无岩心深度段的岩石热导率,建立基于BP神经网络的热导率预测模型.根据声波、密度、中子、电阻率、自然伽马等5种测井响应预测岩石热导率,其模型计算所需时间较短,不需要岩性组分资料,比只考虑1种或其中几种物理参数影响的经验公式适用范围更广.对检验样本以及位于南海的1144A井、1146A井、1148A井等3口大洋科学钻探ODP(Ocean Drilling Program)钻孔的热导率预测结果表明,模型预测的热导率误差低于实验室岩石热导率测试的最大允许误差.该热导率预测模型为获取没有岩心的上述5种测井响应的深度段的岩石热导率提供了一种新途径.
基于纹理特征与BP神经网络的运动车辆识别%Motor Vehicle Identification Based on Texture Feature and BP Neural Network
Institute of Scientific and Technical Information of China (English)
张秀林; 王浩全; 刘玉; 安然
2013-01-01
在Gabor小波滤波器组与图像卷积值作为特征向量达到很高识别率的基础上,提出了一种特征值加权的Gabor小波纹理特征的提取方法.首先Gabor小波函数与纹理图像做卷积,然后加权处理尺度各不相同和方向各不相同的的卷积值,最后将均值和方差看作它们的特征向量,该方法使特征维数有所降低,并利用BP神经网络进行训练和仿真,实现运动车辆纹理图像的自动分类,达到运动图像的识别.实验结果表明此算法有效降低了图像的识别错误,增强了稳健性,对质量差的图像能够有效识别.%On the basis of the Gabor wavelet filter group and the image convolution values as the feature vector can achieve a high recognition rate,a feature-weighted method of extracting texture is proposed.Firstly,Gabor wavelet function and texture image deconvolution.Then,the convolution values are extracted in different scales and different directions.After making the weighting process,taking its mean and variance as the characteristic vector,which greatly reduces the feature dimension.Finally,BP neural network is used to making training and simulation,in order to achieving the automatic classification of texture images of moving vehicles and the identification of moving images.The experimental results show that this algorithm can effectively reduce the recognition error of the image and enhance the robustness.To the poor quality images,it can make the effective recognition.
Neural networks and statistical learning
Du, Ke-Lin
2014-01-01
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...
Neural Networks in Control Applications
DEFF Research Database (Denmark)
Sørensen, O.
examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models....... - Control concepts including parameter estimation - Control concepts including inverse modelling - Control concepts including optimal control For each of the three groups, different control concepts and specific training methods are detailed described.Further, all control concepts are tested on the same......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...
The holographic neural network: Performance comparison with other neural networks
Klepko, Robert
1991-10-01
The artificial neural network shows promise for use in recognition of high resolution radar images of ships. The holographic neural network (HNN) promises a very large data storage capacity and excellent generalization capability, both of which can be achieved with only a few learning trials, unlike most neural networks which require on the order of thousands of learning trials. The HNN is specially designed for pattern association storage, and mathematically realizes the storage and retrieval mechanisms of holograms. The pattern recognition capability of the HNN was studied, and its performance was compared with five other commonly used neural networks: the Adaline, Hamming, bidirectional associative memory, recirculation, and back propagation networks. The patterns used for testing represented artificial high resolution radar images of ships, and appear as a two dimensional topology of peaks with various amplitudes. The performance comparisons showed that the HNN does not perform as well as the other neural networks when using the same test data. However, modification of the data to make it appear more Gaussian distributed, improved the performance of the network. The HNN performs best if the data is completely Gaussian distributed.
What are artificial neural networks?
DEFF Research Database (Denmark)
Krogh, Anders
2008-01-01
Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...
Neural Network Communications Signal Processing
1994-08-01
Technical Information Report for the Neural Network Communications Signal Processing Program, CDRL A003, 31 March 1993. Software Development Plan for...track changing jamming conditions to provide the decoder with the best log- likelihood ratio metrics at a given time. As part of our development plan we...Artificial Neural Networks (ICANN-91) Volume 2, June 24-28, 1991, pp. 1677-1680. Kohonen, Teuvo, Raivio, Kimmo, Simula, Oli, Venta , 011i, Henriksson
Robotic velocity generation using neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The fast-paced nature of robotic soccer necessitates real-time sensing coupled with quick decision making and behaving. The robot must have high response-rate, exact motion ability, and must robust enough to confront interfere during drastic match. But during the match, we find that the robot usually do not act exactly as the commands from host computer. In this paper, we analyze the reason and present a method that uses BP neural network to output robotic velocity directly instead of conventional path-plan strategy, to reduce the error between actual motion and ideal plan.
Institute of Scientific and Technical Information of China (English)
陈秀丽; 赵爱娟; 卫世乾
2014-01-01
由于 MQ3、MQ4、TGS813、TGS26204个金属氧化物半导体组成的气体传感器阵列对酒精气体及有机物交叉敏感，据此建立实时数据采集系统，并提出稳态特征值和动态特征值的提取方法，进而结合 BP神经网络识别方法，通过所建立的电子鼻系统对3种不同品牌白酒进行了分类识别实验.结果表明：电子鼻系统对不同品牌白酒的识别率稳态特征时达90.0%，动态特征时达83.3%.%Based on the sensors array made up of MQ3、MQ4、TGS813、TGS2620 four metal oxide semiconductor sensors which are cross sensitive to alcohol gas and organic,the real-time data acquisition was established in this pa-per,and the method of the steady and dynamic characteristic value extraction was proposed. Combined with BP neu-ral network recognition,three kinds of liquor were conducted classification experiments by the electronic nose sys-tem. The results show that the recognition rate of electronic nose system is up to 90. 0% under the steady character-istic and 83. 3% under the dynamic characteristic for the different brands of liquor.
Institute of Scientific and Technical Information of China (English)
丁硕; 常晓恒; 巫庆辉; 杨友林; 胡庆功
2014-01-01
To study which type of network in Elman neural networks or standard BPNN is more effective for pattern classifi-cation,two classification models based on Elman neural network and standard BPNN are established respectively. The classifica-tion of two- dimensional vector pattern on a plane is taken as an example to train the two classification models and test their generalization abilities respectively. The simulation results show that Elman neural network has higher classification accuracy and faster convergence speed than BPNN under the conditions of the same quantity of the training samples and small or medium size network. And this makes Elman neural network more suitable for solving the problem of pattern classification.%为了研究Elman神经网络和标准BPNN中何种网络类型更适合于解决模式分类问题，分别构建了基于Elman神经网络的分类模型和基于标准BPNN的分类模型。以平面上二维向量模式的分类为例，对2种分类模型进行训练和泛化能力测试。仿真结果表明，在训练样本数量相等且中小规模网络的条件下，Elman网络模型比BP网络模型具有更高的分类精度，更快的收敛速度，更适合于解决模式分类问题。
Institute of Scientific and Technical Information of China (English)
崔日鲜; 刘亚东; 付金东
2015-01-01
The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP)based artificial neural networks (ANN)method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass.Cano-py cover (CC)and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic).Correlation analysis was carried out to identify the relationship between CC,10 color indices and winter wheat above ground biomass.Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass.The results showed that CC,and two color indices had a significant cor-relation with above ground biomass.CC revealed the highest correlation with winter wheat above ground biomass.Stepwise mul-tiple linear regression model constituting CC and color indices of NDI and b,and BP based ANN model with four variables (CC, g,b and NDI)for input was constructed to estimate winter wheat above ground biomass.The validation results indicate that the model using BP based ANN method has a better performance with higher R 2 (0.903)and lower RMSE (61.706)and RRMSE (18.876)in comparation with the stepwise regression model.%建立基于冬小麦冠层图像分析获取的冠层覆盖度和色彩指数的地上部生物量估算模型，以促进作物冠层图像分析技术和 BP 神经网络技术在冬小麦长势无损监测中的应用。六个施氮水平的田间试验条件下，在冬小麦拔节期，分四次采集冬小麦冠层图像，同步进行破坏性取样，测定冬小麦地上部生物量；分析了通过图像分析软件（利用微软 Visual Basic
Applying Neural Network in Evaporative Cooler Performance Prediction
Institute of Scientific and Technical Information of China (English)
QIANG Tian-wei; SHEN Heng-gen; HUANG Xiang; XUAN Yong-mei
2007-01-01
The back-propagation (BP) neural network is created to predict the performance of a direct evaporative cooling (DEC) air conditioner with GLASdek pads. The experiment data about the performance of the DEC air conditioner are obtained. Some experiment data are used to train the network until these data can approximate a function, then, simulate the network with the remanent data. The predicted result shows satisfying effects.
VLSI implementation of neural networks.
Wilamowski, B M; Binfet, J; Kaynak, M O
2000-06-01
Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.
Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model
Institute of Scientific and Technical Information of China (English)
2011-01-01
Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.
Institute of Scientific and Technical Information of China (English)
周建强; 李玉娜; 屈卫东; 兰增林
2015-01-01
基于风速历史数据统计法和基于地理信息与数值预报的物理方法都不能经济、有效、准确地对超短期风速做出预测。为了满足超短期风速预测的时效性和准确性，提出了基于风速历史数据和周边风速数据的风速时空信息BP神经网络超短期风速预测的思想，并研究了基于风速时空信息BP神经网络风速预测模型。建立基于MATLAB平台的BP神经网络预测程序，并实例验证了基于风速时空信息BP神经网络风速预测方法具有更高的精确度、时效性和经济性。%The ultra-short term wind speed forecast method based on historical wind speed statistics and geographical infor-mation physical and numerical prediction is not economical, effective and accurate. In order to meet requirements of time-liness and accuracy of the ultra-short-term wind forecasting, the paper proposes a new idea of BP neural network ultra-short term wind speed forecast based on historical and spatial wind speed data, and studies the BP neural network wind speed prediction model. The BP neural network prediction program is established on MATLAB platform,and it is verified through examples that this prediction method is of higher accuracy,timeliness and economy.
Complex-Valued Neural Networks
Hirose, Akira
2012-01-01
This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...
Institute of Scientific and Technical Information of China (English)
姚荣江; 杨劲松; 邹平; 刘广明
2009-01-01
Aiming at the complexity and spatial variability of the dynamic soil water and salinity in the saline region of the Lower Yellow River Delta, artificial neural network was introduced for modeling and prediction of soil water and salinity. Influence of the number of neurons in the hidden layer on training and forecasting was discussed for the three-layered network, and Back Propagation Neural Network (BPNN) models were established for modeling contents of water and salinity and their spatial distribution in the surface soil 0～20 cm in depth. Results indicate that the water and salinity in the surface soil was significantly correlated with soil bulk density and groundwater properties across the study area. For surface soil salinity, it is advisable to have the five variables, i.e. longitude and latitude of the site, soil bulk density, and depth and mineralization of groundwater cited as input vectors, while for soil moisture, the four variables, i.e. longitude, latitude, bulk density, and groundwater depth. An excessive number of neurons in the hidden layer would result in overfitting. Considering forecasting precision, the topological structure of the BP network was defined as 5∶ 8∶ 1 and 4∶ 6∶ 1 for salinity and moisture in the surface soil, respectively. Distribution maps of the observed surface soil water and salinity and their BPNN simulation displayed similarity in spatial pattern, and the BPNN effectively simulated contents of water and salinity and their spatial distribution in the surface soil with high accuracy. The findings of the study can serve as a theoretical basis for analyzing the occurrence, development and evolvement regularities of soil salinization in the Yellow River Delta, and provide a scientific basis for decision-making in regulating soil water and salt regulation and implementing scientific management of saline soils.%针对黄河下游三角洲盐渍区土壤水盐动态的复杂性和空间的变异性,将人工神经网络引
Institute of Scientific and Technical Information of China (English)
刘红胜; 卢慧清
2011-01-01
The paper establishes a risk evaluation index system for lean supply chain collaboration and evaluates the risks exposed to manufacturing enterprises in their lean supply chain collaboration using BP neural network.%建立了精益供应链协同风险评价指标体系,并运用BP神经网络方法进行协同风险评价,为制造企业精益供应链协同风险的评价提供理论指导.
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2016-07-14
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Institute of Scientific and Technical Information of China (English)
周祥; 何小荣; 陈丙珍
2004-01-01
Because of the powerful mapping ability, back propagation neural network (BP-NN) has been employed in computer-aided product design (CAPD) to establish the property prediction model. The backward problem in CAPD is to search for the appropriate structure or composition of the product with desired property, which is an optimization problem. In this paper, a global optimization method of using the α BB algorithm to solve the backward problem is presented. In particular, a convex lower bounding function is constructed for the objective function formulated with BP-NN model, and the calculation of the key parameter α is implemented by recurring to the interval Hessian matrix of the objective function. Two case studies involving the design of dopamine β-hydroxylase (DβH) inhibitors and linear low density polyethylene (LLDPE) nano composites are investigated using the proposed method.
Institute of Scientific and Technical Information of China (English)
李伟; 何鹏举; 杨恒; 陈明
2012-01-01
针对BP神经网络结构由于特征维数增多变得复杂,以及网络易陷入局部极值点,提出了粗糙集和改进遗传算法结合共同优化神经网络的方法.首先利用粗糙集对样本空间进行属性约简,降低特征维数,进而简化BP神经网络的结构;然后训练过程中先用改进的遗传算法全局搜索网络的权值和阀值,再使用BP算法局部搜索细化,避免网络过早收敛.试验分析证明优化后BP神经网络比传统BP网络的预测精度得到了极大提高,泛化能力得到了增强,说明了该方法的可行性、有效性.%Considering that the BP neural network became complex due to the increase of the sample dimension and it fell easily into local maximums or minimums, we combined genetic algorithm and rough set to optimize the BP neural network. Sections 1 through 3 explain our backpropagation algorithm mentioned in the title, which we believe is effective and whose core consists of; (1) rough set was applied to simplify the network by reducing the attribute dimension; (2) modified genetic algorithm was used to globally search the weights and bios and, further, the BP algorithm was to locally optimize them to avoid the network falling into the local extremes. Simulation results, presented in Fig. 1 and Table 2 in subsection 3. 4, and their analysis indicated preliminarily that prediction accuracy was increased greatly over that of the traditional BP neural network and that generalization was enhanced, thus showing that our backpropagation algorithm is indeed effective.
Institute of Scientific and Technical Information of China (English)
赵璇; 张强
2013-01-01
The construction industry is the important material production sectors. It plays a crucial role on the development of national economy and improvement of people's lives. The development of Chinese building industry has made great contributions to the growth of the national economy. Trends of the construction industry will directly influence on the GNP and the capital from the international market. The construction industry which is labor intensive provides a lot of employment opportunities, from this per-spective, the well-developed construction industry is very significant to social stability. Therefore, it is necessary and valuable to conduct a research on the development of the construction industry. In this paper we will use the Cobb-Douglas production func-tion and BP neural network to predict the development of Chinese building industry, besides, Matlab will also play an important role in modeling and function fitting. Based on the analysis, research and forecasting data of Chinese building industry in 2005-2011, we find out the value of Chinese building industry in 2012. This paper provides the basis and foundation for the simulation prediction of Chinese building industry.% 建筑业是我国国民经济的重要物质生产部门，它与发展国民经济、改善人民生活的质量有着密切的关系。我国建筑业的发展为国民经济高速增长做出了重要贡献，建筑业的发展趋势将直接影响到国民生产总值及国际市场对我国建筑业的资本投放。建筑业是劳动密集型行业，提供了大量的就业机会，从这个角度讲，建筑行业发展良好与否对社会稳定有十分重要的意义。因此，对建筑行业的发展进行研究是非常有必要，有价值的。本文将利用柯布-道格拉斯生产函数和BP神经网络对我国建筑业的发展趋势做预测研究，借助Matlab实现建模和函数拟合。通过对2005-2011年我国建筑行业的数据进行分析
Multigradient for Neural Networks for Equalizers
Directory of Open Access Journals (Sweden)
Chulhee Lee
2003-06-01
Full Text Available Recently, a new training algorithm, multigradient, has been published for neural networks and it is reported that the multigradient outperforms the backpropagation when neural networks are used as a classifier. When neural networks are used as an equalizer in communications, they can be viewed as a classifier. In this paper, we apply the multigradient algorithm to train the neural networks that are used as equalizers. Experiments show that the neural networks trained using the multigradient noticeably outperforms the neural networks trained by the backpropagation.
Institute of Scientific and Technical Information of China (English)
LI Zuoyong; PENG Lihong
2004-01-01
This paper analyses the intrinsic relationship between the BP network learning ability and generalization ability and other influencing factors when the overfit occurs, and introduces the multiple correlation coefficient to describe the complexity of samples; it follows the calculation uncertainty principle and the minimum principle of neural network structural design, provides an analogy of the general uncertainty relation in the information transfer process, and ascertains the uncertainty relation between the training relative error of the training sample set, which reflects the network learning ability,and the test relative error of the test sample set, which represents the network generalization ability; through the simulation of BP network overfit numerical modeling test with different types of functions, it is ascertained that the overfit parameter q in the relation generally has a span of 7×10-3 to 7 × 10-2; the uncertainty relation then helps to obtain the formula for calculating the number of hidden nodes of a network with good generalization ability under the condition that multiple correlation coefficient is used to describe sample complexity and the given approximation error requirement is satisfied;the rationality of this formula is verified; this paper also points out that applying the BP network to the training process of the given sample set is the best method for stopping training that improves the generalization ability.
Institute of Scientific and Technical Information of China (English)
喻小毛
2012-01-01
以水泥混凝土路面材料设计方案以及月降水量为输入，以路面摩擦系数表征路面抗滑性能为输出，利用BP神经网络分析水泥混凝土路面抗滑性能，研究表明，BP网络能考虑不同公路的实际差异，找到路面材料设计方案及月降水量与抗滑性能的最佳组合，为实际施工提供指导。%Taking cement concrete pavement material design scheme and monthly precipitation as the input, and taking anti-friction coefficient embodying anti-sliding property as the output, the paper analyzes the anti-sliding performance of cement concrete pavement by applying BP neu- ral network. Results show that BP network finds out the optimal combination of pavement material design scheme and monthly precipitation and anti-sliding performance by considering various highway conditions, which has provided guidance for actual construction.
Relations Between Wavelet Network and Feedforward Neural Network
Institute of Scientific and Technical Information of China (English)
刘志刚; 何正友; 钱清泉
2002-01-01
A comparison of construction forms and base functions is made between feedforward neural network and wavelet network. The relations between them are studied from the constructions of wavelet functions or dilation functions in wavelet network by different activation functions in feedforward neural network. It is concluded that some wavelet function is equal to the linear combination of several neurons in feedforward neural network.
Application of Artificial Neural Network in Active Vibration Control of Diesel Engine
Institute of Scientific and Technical Information of China (English)
SUN Cheng-shun; ZHANG Jian-wu
2005-01-01
Artificial Neural Network (ANN) is applied to diesel twostage vibration isolating system and an AVC (Active Vibration Control) system is developed. Both identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.
Research on Feasibilityof Top-Coal Caving Based on Neural Network Technique
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on the neural network technique, this paper proposes a BP neural network model which integratesgeological factors which affect top-coal caving in a comprehensive index. The index of top-coal caving may be usedto forecast the mining cost of working faces, which shows the model's potential prospect of applications.
License Plate Recognition Based on Transform Coding and Neural Network
Institute of Scientific and Technical Information of China (English)
李小平; 胡海生; 宋瀚涛; 朱建学; 丁俨
2003-01-01
A method of vehicle license plate recognition utilizing Karhunen-Loeve(K-L) transform is provided. The transform is used to extract features from a mass of image templates, to describe high-dimensional images with low-dimensional ones, and moreover, to implement data compression and play down complexity of the neural network. With the character to reduce eigenspace dimensionality of K-L transform and the ability to map data of BP network, the method does effectively in recognizing license plates.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Ocean wave forecasting using recurrent neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...
Application of neural networks in coastal engineering
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Neural Network for Sparse Reconstruction
Directory of Open Access Journals (Sweden)
Qingfa Li
2014-01-01
Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.
The Physics of Neural Networks
Gutfreund, Hanoch; Toulouse, Gerard
The following sections are included: * Introduction * Historical Perspective * Why Statistical Physics? * Purpose and Outline of the Paper * Basic Elements of Neural Network Models * The Biological Neuron * From the Biological to the Formal Neuron * The Formal Neuron * Network Architecture * Network Dynamics * Basic Functions of Neural Network Models * Associative Memory * Learning * Categorization * Generalization * Optimization * The Hopfield Model * Solution of the Model * The Merit of the Hopfield Model * Beyond the Standard Model * The Gardner Approach * A Microcanonical Formulation * The Case of Biased Patterns * A Canonical Formulation * Constraints on the Synaptic Weights * Learning with Errors * Learning with Noise * Hierarchically Correlated Data and Categorization * Hierarchical Data Structures * Storage of Hierarchical Data Structures * Categorization * Generalization * Learning a Classification Task * The Reference Perceptron Problem * The Contiguity Problem * Discussion - Issues of Relevance * The Notion of Attractors and Modes of Computation * The Nature of Attractors * Temporal versus Spatial Coding * Acknowledgements * References
Building a Chaotic Proved Neural Network
Bahi, Jacques M; Salomon, Michel
2011-01-01
Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to make these iterations behave chaotically, as defined by Devaney, and thus we obtain the first neural networks proven chaotic. Several neural networks with different architectures are trained to exhibit a chaotical behavior.
Meta-Learning Evolutionary Artificial Neural Networks
Abraham, Ajith
2004-01-01
In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the compara...
Neural networks and applications tutorial
Guyon, I.
1991-09-01
The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Aphasia Classification Using Neural Networks
DEFF Research Database (Denmark)
Axer, H.; Jantzen, Jan; Berks, G.
2000-01-01
A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...
Spin glasses and neural networks
Energy Technology Data Exchange (ETDEWEB)
Parga, N. (Comision Nacional de Energia Atomica, San Carlos de Bariloche (Argentina). Centro Atomico Bariloche; Universidad Nacional de Cuyo, San Carlos de Bariloche (Argentina). Inst. Balseiro)
1989-07-01
The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.).
Artificial neural networks in medicine
Energy Technology Data Exchange (ETDEWEB)
Keller, P.E.
1994-07-01
This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.
Move Ordering using Neural Networks
Kocsis, L.; Uiterwijk, J.; Van Den Herik, J.
2001-01-01
© Springer-Verlag Berlin Heidelberg 2001. The efficiency of alpha-beta search algorithms heavily depends on the order in which the moves are examined. This paper focuses on using neural networks to estimate the likelihood of a move being the best in a certain position. The moves considered more like
Neural Network based Consumption Forecasting
DEFF Research Database (Denmark)
Madsen, Per Printz
2016-01-01
This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...
Simplified LQG Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Competition Based Neural Networks for Assignment Problems
Institute of Scientific and Technical Information of China (English)
李涛; LuyuanFang
1991-01-01
Competition based neural networks have been used to solve the generalized assignment problem and the quadratic assignment problem.Both problems are very difficult and are ε approximation complete.The neural network approach has yielded highly competitive performance and good performance for the quadratic assignment problem.These neural networks are guaranteed to produce feasible solutions.
A Robust Digital Watermark Extracting Method Based on Neural Network
Institute of Scientific and Technical Information of China (English)
GUOLihua; YANGShutang; LIJianhua
2003-01-01
Since watermark removal software, such as StirMark, has succeeded in washing watermarks away for most of the known watermarking systems, it is necessary to improve the robustness of watermarking systems. A watermark extracting method based on the error Back propagation (BP) neural network is presented in this paper, which can efficiently improve the robustness of watermarking systems. Experiments show that even if the watermarking systems are attacked by the StirMark software, the extracting method based on neural network can still efficiently extract the whole watermark information.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Quantum computing in neural networks
Gralewicz, P
2004-01-01
According to the statistical interpretation of quantum theory, quantum computers form a distinguished class of probabilistic machines (PMs) by encoding n qubits in 2n pbits. This raises the possibility of a large-scale quantum computing using PMs, especially with neural networks which have the innate capability for probabilistic information processing. Restricting ourselves to a particular model, we construct and numerically examine the performance of neural circuits implementing universal quantum gates. A discussion on the physiological plausibility of proposed coding scheme is also provided.
Combination of Fault Tree and Neural Networks in Excavator Diagnosis
Directory of Open Access Journals (Sweden)
Li Guoping
2013-04-01
Full Text Available By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.
Grinding Parameter Intelligent Prediction Model Based on BP Neural Network%基于神经网络的磨削工艺参数智能预测模型
Institute of Scientific and Technical Information of China (English)
刘伟强; 杨建国
2013-01-01
磨削参数的合理选择对于磨削加工过程有着重要的影响,将人工智能运用到磨削工艺参数的选择过程中是现代发展的一个新趋势.在分析现有的智能算法后,提出了一种利用BP神经网络模型来确定磨削参数的方法.在该方法中综合考虑影响磨削加工的因素,把它们列为神经网络系统的输入参数,并对输入参数进行编码；同时也对输出参数(砂轮速度、工件速度、磨削深度、磨削进给速度)进行了归一化处理以适应神经网络的学习.采用循环算法比较得出隐层的最优神经元个数,从而最终建立了磨削参数智能预测模型,并利用Matlab进行仿真预测,仿真结果表明该预测模型准确率很高,能为磨削参数的选择提供可靠数据.%The reasonable selection of grinding parameters plays an important role in grinding process.Combine artificial intelligence with the selection of grinding process parameters is a new trend in the modern development.After analyzing the existing intelligent algorithm,put forward a new method that using artificial neural network model to determine the grinding parameters.Considerating the influence factors of grinding comprehensively,and listing them as neural network input parameters which are encoded.At the same time make the output parameters (wheel speed,workpiece speed,grinding depth,grinding feed rate) on the normalized in order to adapt to the neural network learning.Using cyclic algorithm for optimal number of neurons in the hidden layers,and eventually established the grinding parameters intelligent prediction model Using matlab to simulate it,the simulation results show that the prediction model has high accuracy,and can provide reliable data for the selection of grinding parameters.
Institute of Scientific and Technical Information of China (English)
张飞; 耿红琴
2014-01-01
清洁机器人的移动定位是个复杂的非线性定位的问题，精密机械结构与路径规划无法补偿定位不精确造成的移动误差，提出一种基于异质 RBF神经网络信息融合的清洁机器人定位技术，设计了智能机器人的控制系统、移动系统和感知系统，设计多个位姿传感器后，实时采集位置信息，在主控芯片中使用粒子群优化神经网络技术对多传感器的信息进行融合，计算清洁机器人的位置信息，解决了位置因素非线性强，定位误差大的问题，并且有效提高了神经网络的局部收敛能力；使用机器人多传感器的实验平台测试证明，这种方法下清洁机器人的移动中定位准确率较传统方法提高13％，具有很强的可靠性与实用性。%The orientation of the movement of the Clean robot is a complex nonlinear problem,structure and path planning can not com-pensate positioning precision machinery movement error caused by inaccurate,put forward a kind of cleaning robot localization based on het-erogeneous RBF neural network information fusion technology,intelligent robot control system is designed and perception system,moving system,design more bits after posture sensor,real-time collecting location information,in the main control chip,using particle swarm opti-mization neural network technology of multi-sensor information fusion,the calculation of the cleaning robot position information,to solve the nonlinear strong location factors,the problem of large positioning error,and effectively improve the local convergence ability of neural network.Using robot multi-sensor experimental platform to test proved that under this kind of method the clean mobile robot positioning accuracy increased by 13%than the traditional methods,have very strong reliability and practicability.
Institute of Scientific and Technical Information of China (English)
罗海艳; 杨勇; 王珏; 于海龙
2015-01-01
如何根据不同的用户行为,来为移动用户提供精准的个性化服务是目前移动应用服务开发技术发展的主流。为解决BP神经网络建模算法收敛速度慢及预测不准确问题,提出基于人工蜂群算法改进的BP神经网络算法。为测试改进后算法的准确性,采用Matlab编程进行试验仿真,通过黑盒子测试方法输出预测的用户行为和实际的用户行为。在18次预测中只有2次预测失败,预测成功率达80%以上。为了验证改进的BP神经网络算法的效率,采用初始总群数为1000,进行了收敛性测试。试验结果表明：基于人工蜂群算法改进的BP神经网络算法可以有效的提高移动用户行为分析的效率和准确性,对在使用移动用户行为分析模型构建过程中,准确定位用户上网需求,提升企业在营销中的竞争力具有非常重要的意义。%It is the mainstream of development technology for current mobile application service to provide accurate and personalized service for mobile users based on different usersˊ behavior. This paper proposes an algorithm to improve BP neural network based on artificial bee colony in order to solve BP neural network algorithm slow convergence and inaccurate prediction. To test the accuracy of the improved algorithm, this paper uses Matlab programming experiment simulation. This paper outputs prediction and the actual user behavior through the black box testing method. Only twice were failed in 18 times forecast and the success rate was more than 80%.In order to validate the efficiency of the improved BP neural network algorithm, this paper tests the convergence by initializing total group number 1000. Results showed that the improved BP neural network algorithm based on artificial colony algorithm could effectively improve the efficiency and accuracy of the mobile user behavior analysis. It is very important to locate userˊs demand to Internet accurately and promote the power of
Discontinuities in recurrent neural networks.
Gavaldá, R; Siegelmann, H T
1999-04-01
This article studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net function and a sigmoid-like continuous activation function. We introduce arithmetic networks as ARNN augmented with a few simple discontinuous (e.g., threshold or zero test) neurons. We argue that even with weights restricted to polynomial time computable reals, arithmetic networks are able to compute arbitrarily complex recursive functions. We identify many types of neural networks that are at least as powerful as arithmetic nets, some of which are not in fact discontinuous, but they boost other arithmetic operations in the net function (e.g., neurons that can use divisions and polynomial net functions inside sigmoid-like continuous activation functions). These arithmetic networks are equivalent to the Blum-Shub-Smale model, when the latter is restricted to a bounded number of registers. With respect to implementation on digital computers, we show that arithmetic networks with rational weights can be simulated with exponential precision, but even with polynomial-time computable real weights, arithmetic networks are not subject to any fixed precision bounds. This is in contrast with the ARNN that are known to demand precision that is linear in the computation time. When nontrivial periodic functions (e.g., fractional part, sine, tangent) are added to arithmetic networks, the resulting networks are computationally equivalent to a massively parallel machine. Thus, these highly discontinuous networks can solve the presumably intractable class of PSPACE-complete problems in polynomial time.
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Neural Networks Methodology and Applications
Dreyfus, Gérard
2005-01-01
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...
Fiber optic Adaline neural networks
Ghosh, Anjan K.; Trepka, Jim; Paparao, Palacharla
1993-02-01
Optoelectronic realization of adaptive filters and equalizers using fiber optic tapped delay lines and spatial light modulators has been discussed recently. We describe the design of a single layer fiber optic Adaline neural network which can be used as a bit pattern classifier. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in processing speed, parallelism, and interconnection. The new optical neural network described in this paper is designed for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the optically implemented Adaline in the presence of errors in the hardware, and we studied methods for improving the convergence rate of the Adaline.
Analog electronic neural network circuits
Energy Technology Data Exchange (ETDEWEB)
Graf, H.P.; Jackel, L.D. (AT and T Bell Labs., Holmdel, NJ (USA))
1989-07-01
The large interconnectivity and moderate precision required in neural network models present new opportunities for analog computing. This paper discusses analog circuits for a variety of problems such as pattern matching, optimization, and learning. Most of the circuits build so far are relatively small, exploratory designs. The most mature circuits are those for template matching. Chips performing this function are now being applied to pattern recognition problems.
Neural Networks for Speech Application.
1987-11-01
operation and neurocrience theories of how neurons process information in the brain. design. Early studies by McCulloch and Pitts dunng the forties led to...developed the commercially available Mark III and Mark IV neurocom- established by McCulloch and Pits. puters that model neural networks and run...ORGANIZERS Infonuiaonienes (1986) FOR Lashley, K. Brain Mehaius and Cblali (129)SPEECHOTECH McCullch. W and Pitts . W, ’A Logical Calculusof the
Process Neural Networks Theory and Applications
He, Xingui
2010-01-01
"Process Neural Networks - Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks, and enhancing the expression capability for practical problems, with broad applicability to solving problems relating to process in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are strictly proved. The application methods, network construction principles, and optimization alg
The LILARTI neural network system
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
1992-10-01
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
基于KPCA的BP神经网络齿轮泵故障诊断方法研究%Gear Pump Fault Diagnosis Method Based on KPCA and BP Neural Network
Institute of Scientific and Technical Information of China (English)
仝奇; 胡双演; 李钊; 叶霞; 张仲敏
2015-01-01
Aiming at the complex structures and time⁃consuming problem of neural network,this paper proposes a gear pump fault diagnosis method based on kernel principal component analysis (KPCA) and back propagation neural network (BPNN).Firstly,empiri⁃cal mode decomposition ( EMD) is used to break down the acquired gear pump vibration signal characteristic to form the original char⁃acteristic parameter set.Secondly,KPCA is used to extract nonlinear feature of the signal and reduce the sample dimensions.Finally,the results can be used as the input of BPNN to train the gear pump fault diagnosis model for diagnosis of the test samples.The experimental results show that the method can effectively realize clustering of gear pump samples,reduce the network complexity,cut down the net⁃work training time and times,and improve the accuracy of fault diagnosis.%针对神经网络结构复杂和训练时间长的问题，提出了一种基于核主元分析的反向传播神经网络齿轮泵故障诊断方法。使用经验模态分解对采集的齿轮泵振动信号进行特征分解形成原始特征参数集，利用核主元分析法提取信号的非线性特征，降低样本维数，并将结果作为神经网络的输入训练齿轮泵故障诊断模型，对测试样本进行诊断。实验结果表明，该方法对齿轮泵样本能够有效聚类，降低网络复杂度，减少网络训练时间和次数，并提高故障诊断的精度。
Institute of Scientific and Technical Information of China (English)
黄春晖; 温永杰
2014-01-01
在分析宽频带CMMB直放站高功率功放(HPA）特性的基础上，提出了一种可分离处理功放记忆效应和非线性的延时神经网络（ FIR-NLNNN ）模型。该模型以实数延时神经网络（ RVTDNN ）为基础，用Levenberg-Marquardt（LM）优化算法确定神经网络系数，在模型中新增参数 w0，给出了 LM 算法的修改公式。接着在预失真神经网络系统中引入Bayesian机理消除LM算法的过拟合现象，构建CMMB数字直放站的间接学习预失真器，拟合HPA的非线性和记忆效应。结果表明：RVTDNN和FIR-NLNNN 2种预失真器均能显著提高系统性能，降低邻信道功率比30 dB左右。在保持均方误差（MSE）小于10-6的情况下，FIR-NLNNN结构的网络参数比RVTDNN结构减少了近50%，迭代过程中的乘法和加法次数约降低75%。%Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations, a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier. The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network. Due to the new parameters w0 in the novel NN model, the modified formulas of LM algorithm were provided. Next,in order to eliminate the over-fitting of LM algorithm, the Bayesian regularization algorithm was applied to the predistortion system. Additionally, the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA. Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB. Moreover, with the mean square error less than 10-6, the coefficient of network for FIR-NLNNN is about half of that for RVTDNN. Similarly, the times of
Neural Network-Based Active Control for Offshore Platforms
Institute of Scientific and Technical Information of China (English)
周亚军; 赵德有
2003-01-01
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
Data Process of Diagnose Expert System based on Neural Network
Directory of Open Access Journals (Sweden)
Shupeng Zhao
2013-12-01
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
Practical neural network recipies in C++
Masters
2014-01-01
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum
Activated sludge process based on artificial neural network
Institute of Scientific and Technical Information of China (English)
张文艺; 蔡建安
2002-01-01
Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.
Application of Neural Network for Concrete Carbonation Depth Prediction
Luo, Daming; Niu, Ditao; Dong, Zhenping
2014-01-01
Concrete carbonation is one of the most significant causes of deterioration of reinforced concrete structures in atmospheric environment. However, current models based on the laboratory tests cannot predict carbonation depth accurately. In this paper, the BP neural network is optimized by the particle swarm optimization (PSO) algorithm to establish the model of the length of the partial carbonation zone for concrete. After simulation training, the improved model is applied to a concrete bridg...
Speed-Sensorless Control Using Elman Neural Network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed,with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.
Institute of Scientific and Technical Information of China (English)
甄新武; 乔均俭
2013-01-01
将BP人工神经网络(Artificiai neural network)技术与传统的正交试验方法相结合,提出一种新的试验分析和处理方法,利用神经网络特有的自学能力,对主要影响因素进行仿真优化,获得Bacillus velezensis Z-27菌株培养基组分,即玉米粉2.0％、豆饼粉1.5％、MnSO4· H2O 0.07％,起始pH为7.0、接种量为2.0％,应用优化得到的培养基组分进行验证试验,取得了较好的效果.%In this article,a new method of test analysis and data treatment which combined BP artificial neural network and traditional orthogonal experiment was proposed to obtain optimized medium composition of Bacillus velezensis Z-27 strains.By this method the main factors could be simulated and optimized with the help of specific learning capability of neural network.The results showed that the medium components of B.velezensis Z-27 strains was corn flour 2.0％,soybean powder 1.5％,MnSO4·H2O 0.07％ at initial pH 7.0,with inoculation quantity 2.0％.The optimized culture medium was then verified,and showed satisfactory effect.
Directory of Open Access Journals (Sweden)
CHEN, Z.
2013-11-01
Full Text Available A microphone clustering and back propagation (BP neural network based acoustic source localization method using distributed microphone arrays in an intelligent meeting room is proposed. In the proposed method, a novel clustering algorithm is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to select clusters which are small and close to the source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each selected cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization approaches confirm the validity of the proposed method.
Institute of Scientific and Technical Information of China (English)
杨国良; 钟雯; 黄晓韵; 梁思敏; 何慧慧; 陈家驹
2015-01-01
Based on layered elastic theory,the elastic modulus of asphalt course in asphalt pavement was predicted using BP artificial neural network.According to the types of pavement structure in common use,the database of surface deflections with their corresponding structural parameters of asphalt course based on layered elastic theory was established.The elastic modulus backcalculation model of asphalt course in asphalt pavement was developed using BP artificial neural network to predict.The predictive results of asphalt course elastic modulus backcalculation using theoretical deflection basin and measured deflection basin indicate that the elastic modulus backcalculation model of asphalt course in asphalt pavement is of good predictive accuracy and reliability.It would provide the references with the elastic modulus backcalculation model of asphalt course to accurately and quickly estimate the conditions of asphalt course in asphalt pavement.%基于层状弹性体系理论,建立BP人工神经网络反演沥青路面沥青面层弹性模量预测模型,利用BP人工神经网络预测沥青路面沥青面层弹性模量.理论弯沉盆和实测弯沉盆反演沥青面层弹性模量的结果表明,建立的BP人工神经网络反演沥青路面沥青面层弹性模量模型具有良好的预测精度和可靠性,为评价沥青路面的沥青面层性能状况提供了参考.
Institute of Scientific and Technical Information of China (English)
徐卫晓; 谭继文; 文妍
2014-01-01
针对单一传感器对滚动轴承故障信息的识别具有不确定性的缺陷，提出了基于BP神经网络与D⁃S证据理论的多传感器信息融合的方法。将BP神经网络的输出结果进行归一化处理作为各焦元的基本概率分配，轴承的5种故障类型作为系统的识别框架，根据Dempster合成法则进行决策级融合。试验结果表明，利用该方法对轴承的内圈磨损、外圈磨损、滚珠磨损等故障进行试验诊断，提高了故障诊断的准确率，验证了该方法的可行性。%Aimed at the defect of uncertainty of single sensor for the rolling bearing fault information recognition, the method of multi⁃sensor information fusion was proposed based on the BP neural network and the D⁃S evidence theory. Output results of BP neural network were normalized as the focal element of the basic probability assignment, five kinds of fault types of rolling bearing were identi⁃fied as a system framework, and decision level fusion was made according to Dempster combination rule. The test results show that using the method in experiments of fault diagnosis for bearing inner ring wear, outer ring wear and ball bearing wear has improved the accuracy of fault diagnosis, and verified its feasibility.
MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION
Directory of Open Access Journals (Sweden)
Artur Popko
2013-06-01
Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.
A Novel Training Algorithm of Genetic Neural Networks and Its Application to Classification
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Salience-Affected Neural Networks
Remmelzwaal, Leendert A; Ellis, George F R
2010-01-01
We present a simple neural network model which combines a locally-connected feedforward structure, as is traditionally used to model inter-neuron connectivity, with a layer of undifferentiated connections which model the diffuse projections from the human limbic system to the cortex. This new layer makes it possible to model global effects such as salience, at the same time as the local network processes task-specific or local information. This simple combination network displays interactions between salience and regular processing which correspond to known effects in the developing brain, such as enhanced learning as a result of heightened affect. The cortex biases neuronal responses to affect both learning and memory, through the use of diffuse projections from the limbic system to the cortex. Standard ANNs do not model this non-local flow of information represented by the ascending systems, which are a significant feature of the structure of the brain, and although they do allow associational learning with...
Dynamic Analysis of Structures Using Neural Networks
Directory of Open Access Journals (Sweden)
N. Ahmadi
2008-01-01
Full Text Available In the recent years, neural networks are considered as the best candidate for fast approximation with arbitrary accuracy in the time consuming problems. Dynamic analysis of structures against earthquake has the time consuming process. We employed two kinds of neural networks: Generalized Regression neural network (GR and Back-Propagation Wavenet neural network (BPW, for approximating of dynamic time history response of frame structures. GR is a traditional radial basis function neural network while BPW categorized as a wavelet neural network. In BPW, sigmoid activation functions of hidden layer neurons are substituted with wavelets and weights training are achieved using Scaled Conjugate Gradient (SCG algorithm. Comparison the results of BPW with those of GR in the dynamic analysis of eight story steel frame indicates that accuracy of the properly trained BPW was better than that of GR and therefore, BPW can be efficiently used for approximate dynamic analysis of structures.
Institute of Scientific and Technical Information of China (English)
郭华锋; 李菊丽; 孙涛
2014-01-01
In order to study effects of process parameters on kerf quality of fiber laser cutting , the relationship between process parameters and kerf quality was analyzed based on the test of laser cutting T 4003 stainless steel .The prediction model between the main process parameters , such as laser power , cutting speed , assistant gas pressure and kerf roughness was established based on error back propagation artificial neural network .The samples collected by the cutting test was network trained and the training model was inspected by the test samples .The results show that , kerf roughness increases while laser power increases and kerf roughness decreases while cutting speed and assist gas pressure increase .The neural network prediction model has high precision and the network training has good effect .The maximum relative error between the predictive values and the test sample value is 2.4%.After training, the prediction model has high inspection precision, the maximum relative error of the test sample is only 6.23%.The model can predict the laser cutting kerf roughness effectively and can provide the experiment basis for selecting and optimizing process parameters and improving laser cutting quality .%为了研究工艺参量对光纤激光切割切口质量的影响，进行了切割T4003不锈钢试验，分析了工艺参量与切口质量之间的关系。采用基于误差反向传播算法的人工神经网络，建立了激光功率、切割速率、辅助气体压力等工艺参量与切口粗糙度之间的预测模型。对切割试验采集的训练样本进行了网络训练，并利用测试样本对训练模型进行验证。结果表明，随着激光功率增加，切口粗糙度增大；随着切割速率和辅助气体压力增加，切口粗糙度减小。神经网络预测模型精度较高，网络训练效果良好，预测值与试验样本值间的最大相对误差为2．4％。训练后检验精度较高，检验样本最大
Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.
Institute of Scientific and Technical Information of China (English)
宋博
2012-01-01
With the increasingly heated market competition,catering enterprises must construct and cultivate their core competence to develop permanently.In order to analyze and evaluate the core competence of catering enterprises,this paper constructed the comprehensive evaluation index system by using the fault-tolerance characteristics of neural network theory,then achieved fuzzy comprehensive evaluation for the core competence of catering enterprises by using proper action function,data structure and processing various of non-numerical index.%本文构造了餐饮企业核心竞争力的综合评价指标体系,利用神经网络理论的容错特征,通过选取适当的作用函数和数据结构,处理各种非数值性指标,实现对餐饮企业核心竞争力的模糊综合评价。
Rule Extraction using Artificial Neural Networks
2010-01-01
Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can...
Feature Weight Tuning for Recursive Neural Networks
2014-01-01
This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful c...
Modelling Microwave Devices Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Andrius Katkevičius
2012-04-01
Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian
Fast Algorithms for Convolutional Neural Networks
Lavin, Andrew; Gray, Scott
2015-01-01
Deep convolutional neural networks take GPU days of compute time to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3x3 filters. We ...
Semantic Interpretation of An Artificial Neural Network
1995-12-01
ARTIFICIAL NEURAL NETWORK .7,’ THESIS Stanley Dale Kinderknecht Captain, USAF 770 DEAT7ET77,’H IR O C 7... ARTIFICIAL NEURAL NETWORK THESIS Stanley Dale Kinderknecht Captain, USAF AFIT/GCS/ENG/95D-07 Approved for public release; distribution unlimited The views...Government. AFIT/GCS/ENG/95D-07 SEMANTIC INTERPRETATION OF AN ARTIFICIAL NEURAL NETWORK THESIS Presented to the Faculty of the School of Engineering of
Forecasting Exchange Rate Using Neural Networks
Raksaseree, Sukhita
2009-01-01
The artificial neural network models become increasingly popular among researchers and investors since many studies have shown that it has superior performance over the traditional statistical model. This paper aims to investigate the neural network performance in forecasting foreign exchange rates based on backpropagation algorithm. The forecast of Thai Baht against seven currencies are conducted to observe the performance of the neural network models using the performance criteria for both ...
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Institute of Scientific and Technical Information of China (English)
宋丹丹; 任国臣; 张梦松; 苏人奇
2016-01-01
The photovoltaic power station has the characteristics that generating capacity is intermittent and easily impact large power networks. This paper analyzes the main factors influencing PV station, including illumination intensity weather patterns and temperature. And it is important to establish the forecasting model of generating capacity. The model uses Self-Organizing Feature Maps to classify the input samples, then the classification of sample uses LM learning algorithm training. By comparison between the predicted value and the real value, the model gets over shortcomings which are easy to fall into local minima. The accuracy and application of models are high.%针对并网型光伏电站发电量具有间歇性、易对大电网造成冲击等特点，分析影响光伏电站发电量的主要气象因素，包括辐照度、天气类型、温度，建立了一种基于 SOFM-LM-BP 神经网络发电量预测模型。该模型采用SOFM神经网络对输入样本进行分类，再将分类后的样本采用LM学习算法训练，从而得到光伏发电量的预测系统。通过预测值与真实值对比可知，该预测模型的预测精度较高，克服陷入局部极小值等缺点。
Institute of Scientific and Technical Information of China (English)
朱爱胜; 俞林; 许敏; 张天华
2015-01-01
Firstly, the paper collects the first hand data by the questionnaire survey method, and then studies the relationship between university students' entrepreneurial intentions and entrepreneurial behaviors by artificial neural network based on the preliminary research results. The results show that among the 11 dimensions which are influenced the contemporary university students' Entrepreneurial intentions and entrepreneurial behavior, the impact of entrepreneurship education, entrepreneurship, entrepreneurial intentions, institutional environment, endowments are largest, the impact of the expected benefits, market opportunities and behavi-oral attitudes are smaller than the above five dimensions, the impact of cognitive, subjective norm, perceived behavioral control are smallest. The predicted result of improved BP Neural Network algorithm through genetic algorithm optimization are compared with the traditional BP neural network, it is found that optimization algorithm has better prediction accuracy which is improved nearly 10 percen-tage points.%通过设计调研问卷进行实地调研, 收集第一手资料数据, 在前期成果的基础上, 通过遗传算法优化的人工神经网络技术计量研究高校学生的创业意愿与创业行为间的关系. 结果表明影响当代大学生创业意愿与创业行为的11个维度中创业教育、 创业能力、 创业意愿、 制度环境、 禀赋5个维度对其影响度较大, 预期收益、 市场机会和行为态度等维度对当代大学生创业意愿与创业行为的影响次之, 认知、 主观规范、 行为知觉控制3个维度对当代大学生创业意愿与创业行为的影响最小. 并在此基础上, 将遗传算法优化的神经网络的预测结果与传统神经网络进行比较, 发现遗传算法优化的神经网络的预测效果更佳, 预测精确度提升了近10个百分点.
Fuzzy neural network theory and application
Liu, Puyin
2004-01-01
This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he
Neural Networks for Rapid Design and Analysis
Sparks, Dean W., Jr.; Maghami, Peiman G.
1998-01-01
Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.
Neural networks for nuclear spectroscopy
Energy Technology Data Exchange (ETDEWEB)
Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States)] [and others
1995-12-31
In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.
Institute of Scientific and Technical Information of China (English)
WANG Hong-bing; XU An-jun; AI Li-xiang; TIAN Nai-yuan
2012-01-01
The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
Systolic implementation of neural networks
Energy Technology Data Exchange (ETDEWEB)
De Groot, A.J.; Parker, S.R.
1989-01-01
The backpropagation algorithm for error gradient calculations in multilayer, feed-forward neural networks is derived in matrix form involving inner and outer products. It is demonstrated that these calculations can be carried out efficiently using systolic processing techniques, particularly using the SPRINT, a 64-element systolic processor developed at Lawrence Livermore National Laboratory. This machine contains one million synapses, and forward-propagates 12 million connections per second, using 100 watts of power. When executing the algorithm, each SPRINT processor performs useful work 97% of the time. The theory and applications are confirmed by some nontrivial examples involving seismic signal recognition. 4 refs., 7 figs.
Magnitude Sensitive Competitive Neural Networks
Pelayo Campillos, Enrique; Buldain Pérez, David; Orrite Uruñuela, Carlos
2014-01-01
En esta Tesis se presentan un conjunto de redes neuronales llamadas Magnitude Sensitive Competitive Neural Networks (MSCNNs). Se trata de un conjunto de algoritmos de Competitive Learning que incluyen un término de magnitud como un factor de modulación de la distancia usada en la competición. Al igual que otros métodos competitivos, MSCNNs realizan la cuantización vectorial de los datos, pero el término de magnitud guía el entrenamiento de los centroides de modo que se representan con alto de...
The Laplacian spectrum of neural networks.
de Lange, Siemon C; de Reus, Marcel A; van den Heuvel, Martijn P
2014-01-13
The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these "conventional" graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.
Institute of Scientific and Technical Information of China (English)
LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai
2008-01-01
Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.
Neural Network Controlled Visual Saccades
Johnson, Jeffrey D.; Grogan, Timothy A.
1989-03-01
The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.
Neural networks with discontinuous/impact activations
Akhmet, Marat
2014-01-01
This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...
Video Traffic Prediction Using Neural Networks
Directory of Open Access Journals (Sweden)
Miloš Oravec
2008-10-01
Full Text Available In this paper, we consider video stream prediction for application in services likevideo-on-demand, videoconferencing, video broadcasting, etc. The aim is to predict thevideo stream for an efficient bandwidth allocation of the video signal. Efficient predictionof traffic generated by multimedia sources is an important part of traffic and congestioncontrol procedures at the network edges. As a tool for the prediction, we use neuralnetworks – multilayer perceptron (MLP, radial basis function networks (RBF networksand backpropagation through time (BPTT neural networks. At first, we briefly introducetheoretical background of neural networks, the prediction methods and the differencebetween them. We propose also video time-series processing using moving averages.Simulation results for each type of neural network together with final comparisons arepresented. For comparison purposes, also conventional (non-neural prediction isincluded. The purpose of our work is to construct suitable neural networks for variable bitrate video prediction and evaluate them. We use video traces from [1].
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-computing is a practical and advanced tool for solving large-scale underground rock engineering problems.
A novel compensation-based recurrent fuzzy neural network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
WU Bo; WU Ke; LU JianHong
2009-01-01
Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
Optimising the topology of complex neural networks
Jiang, Fei; Schoenauer, Marc
2007-01-01
In this paper, we study instances of complex neural networks, i.e. neural netwo rks with complex topologies. We use Self-Organizing Map neural networks whose n eighbourhood relationships are defined by a complex network, to classify handwr itten digits. We show that topology has a small impact on performance and robus tness to neuron failures, at least at long learning times. Performance may howe ver be increased (by almost 10%) by artificial evolution of the network topo logy. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution.
Artificial neural networks in neurosurgery.
Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali
2015-03-01
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.
Neural Networks for Emotion Classification
Sun, Yafei
2011-01-01
It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifier that can recognize six basic emotions with an average accuracy of 77% over the Cohn-Kanade database. The novelty of this work is that instead of empirically selecting the parameters of the neural network, i.e. the learning rate, activation function parameter, momentum number, the number of nodes in one layer, etc. we developed a strategy that can automatically select comparatively better combination of these parameters. We also introduce another way to perform back propagation. Instead of using the partial differential of the error function, we use optimal algorithm; namely Powell's direction set to minimize the error function. We were also interested in construction an authentic emotion databases. This...
A new formulation for feedforward neural networks.
Razavi, Saman; Tolson, Bryan A
2011-10-01
Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry. Based on this geometrical interpretation, a new set of variables describing neural networks is proposed as a more effective and geometrically interpretable alternative to the traditional set of network weights and biases. Then, this paper develops a new formulation for neural networks with respect to the newly defined variables; this reformulated neural network (ReNN) is equivalent to the common feedforward neural network but has a less complex error response surface. To demonstrate the learning ability of ReNN, in this paper, two training methods involving a derivative-based (a variation of backpropagation) and a derivative-free optimization algorithms are employed. Moreover, a new measure of regularization on the basis of the developed geometrical interpretation is proposed to evaluate and improve the generalization ability of neural networks. The value of the proposed geometrical interpretation, the ReNN approach, and the new regularization measure are demonstrated across multiple test problems. Results show that ReNN can be trained more effectively and efficiently compared to the common neural networks and the proposed regularization measure is an effective indicator of how a network would perform in terms of generalization.
Hindcasting cyclonic waves using neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Rao, S.; Chakravarty, N.V.
network attractive. A neural network (NN) is an information processing system modeled on the structure of the dynamic process. Its merit is the ability to deal with information whose interrelation is ambiguous or whose functional relation is not clear... the backpropagation networks with updated algorithms are used in this paper. A brief description about the working of a back propagation neural network and three updated algorithms is given below. Backpropagation learning: Backpropagation is the most widely used...
Drift chamber tracking with neural networks
Energy Technology Data Exchange (ETDEWEB)
Lindsey, C.S.; Denby, B.; Haggerty, H.
1992-10-01
We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed.
Coherence resonance in bursting neural networks.
Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J
2015-10-01
Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal-a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.