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IEEE 16th International Conference on Industrial Engineering and Engineering Management

作者： Huang Feng-wen Jiang Ai-ping Shanghai Urban Management Coll Res Ctr Finance Shanghai Peoples R China Shanghai Univ Sydney Inst Language & Commerce Shanghai 200041 Peoples R China

ISBN: (纸本)9781424436705

bp algorithm of neural network don't obtain global minimum sometimes[2-5], furthermore, it is possible to create many local minimum so that the optimum solution can't be found. In order to solve this question, one parameter filled function method[1] is presented which can calculate value fast. We combine it with modified BFGS (Broyden-Davidon-Fletcher- Powell) to get a new algorithm for global optimization of wavelet neural network. The algorithm obtain the first local minimum by BFGS, then filled function method is used to obtain another smaller local minimum, this process is repeated for some times so that the network structure and weight value are optimized till global minimum is found. This method is used to train Shanghai stock index, then better network performance is obtained.

关键词： bp algorithm Filled function Optimization Wavelet neural network

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Vehicle Types Recognition Based on Neural Network

Vehicle Types Recognition Based on Neural Network

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International Conference on Computational Intelligence and Natural Computing

作者： Zhang, Xin-bo Jiang, Li ZheJiang Gongshang Univ Coll Informat & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780769536453

As a key technology of intelligent transportation system, higher identification accuracy, robustness and real-time are needed in vehicle recognition. Therefore, in view of the features of vehicle types, this paper proposes a bp neural network car types classifier method based on fuzzy C-means clustering. First, on the basis of the pretreatment of the images of the vehicle,we abstract the features of car types from images and classes the massive dataes by fuzzy C- means clustering algorithm. Then, design the bp neural networks to train and test the classified data. Finally it is carried on compressive judgment by the computer. Experiments prove the validity of the classifier. It can recognize the highway vehicle types rapidly.

关键词： character abstraction fuzzy c-means clustering bp algorithm

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A New Practical Method on Hydrological Calculation

A New Practical Method on Hydrological Calculation

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6th International Symposium on Neural Networks

作者： Feng, Lihua Zhang, Xingcai Zhejiang Normal Univ Dept Geog Jinhua 321004 Peoples R China

ISBN: (纸本)9783642015069

Artificial Neural Networks (ANN) deal with information through interactions among neurons (or nodes), approximating the mapping between in-puts and outputs based on non-linear functional composition. They have the advantages of self-learning, self-organizing, and self-adapting. It is practical to use ANN technology to carry out hydrologic calculations. To this end, this note has fundamentally set up a system Of calculation and analysis based on ANN technology, given an example of application with good results. It shows that ANN technology is a relatively effective way of solving problems in hydrologic calculation.

关键词： Artificial neural networks bp algorithm Hydrologic calculation

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Accelerating bp-Based Iterative Low-Density Parity-Check Decoding by Modified Vertical and Horizontal Processes

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Journal of Southwest Jiaotong University(English Edition) 2009年第4期17卷 275-282页

作者：陈婧文仰枫帆罗琳 THO Le-Ngoc College of Information Science and Technology Nanjing University of Aeronautics and Astronautics College of Information Science and Engineering Southeast University Department of Electrical and Computer Engineering McGill UniversityMontreal

Two modified bp algorithms related to vertical and horizontal processes are proposed to accelerate iterative low-density parity- check （LDPC） decoding over an additive white Gaussian noise （AWGN） channel, where the newly updated extrinsic information is immediately used in the current decoding round. Theoretical analysis and simulation results demonstrate that both the modified approaches provide significant performance improvements over the traditional bp algorithm with almost no additional decoding complexity. The proposed algorithm with modified horizontal process offers even better performance than another algorithm with the modified horizontal process. The two modified bp algorithms are very promising in practical communications since both can achieve an excellent trade-off between the performance and decoding complexity.

关键词： LDPC codes Iterative decoding bp algorithm Extrinsic information Horizontal process Vertical process

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A Study of Data Fusion Based on Combining Rough Set with bp Neural Network

A Study of Data Fusion Based on Combining Rough Set with BP ...

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2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Wei Gao Nan Jiang Jingxin Wen Hai Zhao Institute of Information and Technology Northeastern University Shenyang Institute of Chemical Techn Shenyang Institute of Chemical Technology Shenyang China Institute of Information and Technology Northeastern University Shenyang China

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and bp neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough set theory is first used to process the sample data, and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the improved algorithm is demonstrated by an example compared with the traditional neural network system.

关键词： rough set neural network bp algorithm data fusion attribute reduction

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Forecasting Sedimentation of constructions Based on bp Network

Forecasting Sedimentation of constructions Based on BP Netwo...

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2009 International Conference on Environmental Science and Information Application Technology

作者： JIANG Ting-chen School of Geodesy&Geomatics Engineering Huaihai Institute of Technology Lianyungang,China

If sedimentation of constructions exceeds the prescribed limits,it would give rise to huge losses for community and people,so it is significant to establish the effective and practical deformation forcasting model for the safe operation and economic *** the unique non-linear,non-convexity,non-locality,non-steadiness,adaptability and powerful ability of calulation and information process,bp(Back Propagation)neural network can adapt to the complicated and changeable dynamic characteristics of buildings,that has broad application foreground in deformation *** this paper,on the basis of deformation observation data,a basic algorithm about establishing bp neural network model in sedimentation prediction is *** the same time,analysis of examples are gived so that the application of neural network for deforamtion forcasting is studied comprehensively and systemically,the results show that neural network is very effective for Sedimentation prediction and can serve society and people in the future.

关键词： bp Neural networks sedimentation Forecast bp algorithm

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Prediction model of arc furnace based on improved bp neural network

Prediction model of arc furnace based on improved BP neural ...

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2009 International Conference on Environmental Science and Information Application Technology

作者： Zhao Hui and Xiaobo Wang Tianjin Key Laboratory for Control Theory and Application in Complicated System Tianjin University of Technology Tianjin,P.R.China Xiaotao Wang Electric and information college Xihua University Chengdu,Sichuan,P.R.China

It’s very important to control the electrode current of arc *** paper firstly discuss intelligent control method of arc furnace based on neural network,then the three-phase current prediction model of arc furnace has been built and it is analyzed and simulated by matlab *** result shows that the electrode is effective controlled and the effect by using improved bp method is satisfactory.

关键词： neural network arc furnace prediction model bp algorithm

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Neural network based method for compensating model error

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Journal of Southeast University(English Edition) 2009年第3期25卷 400-403页

作者：胡伍生孙璐东南大学交通学院南京210096 美国华盛顿天主教大学土木工程学院华盛顿20064

Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a bp neural network （called the H-bp algorithm） for compensating function model errors is put forward. The function model is assumed as y =f（x1, x2,… ,xn）, and the special structure of the H-bp algorithm is determined as （ n ＋ 1） ×p × 1, where （n ＋ 1） is the element number of the input layer, and the elements are xl, x2,…, xn and y＇（ y＇ is the value calculated by the function model）; p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y＇（y0 is the known value of the sample）. The calculation steps of the H-bp algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-bp algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors.

关键词： model error neural network bp algorithm compen- sating

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Software Design for the Fault Diagnosis System of ATC Based on Neural Networks

Software Design for the Fault Diagnosis System of ATC Based ...

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2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

作者： Liansheng Liu Jianfang Zhang Basic Experiment Center Civil Aviation University of China Tianjin 300300 Basic Experiment Center Civil Aviation University of China Tianjin 300300 Engineering Technique Tr

Because of the limitation of the expert system which is based on the rule, this text proposes introducing the neural network technology into the fault diagnosis system. Then we recommend the frame and the principle of the expert system. And at last, we complete the simulation experiment which indicates the rationality of this desin..

关键词： Fault Diagnosis Ezpert System Neural Network bp algorithm

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Research on Fault Diagnosis of Fork Lift Truck Hydraulic System Based on Artificial Neural Network

Research on Fault Diagnosis of Fork Lift Truck Hydraulic Sys...

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2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机械自动化国际会议)

作者： Heqing LI Qing TAN School of Automobile & Mechanical Engineering Changsha University of Science and Technology Changsha School of Mechanical and Electrical Engineering Center South University Changsha China

The structure and algorithm of bp neural net were described, the realization process of the fault diagnosis of hydraufic system based on bp neural net was discussed. According to the experiment and test of fault of fork lift truck hydraulic system, the bp net has better learning function, high net convergence rate and high stability of learning and memory. The diagnosis results indicate that the presented diagnosis method has high reliability and can attain the expected results, which can be applied to fault diagnosis of hydraulic system.

关键词： bp algorithm Neural network hydraulic system fault diagnosis

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