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检索条件"主题词=Back Propagation algorithm"

共 277 条记录，以下是261-270 订阅

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Handwriting Thai Signature Recognition System based on Multilayer Perceptron and Radial Basis Network

Handwriting Thai Signature Recognition System based on Multi...

引用

Int’l Conference on Advanced Computational Technologies & Creative Media (ICACTCM’2014, Pattaya)

作者： Naruemol Chumuang Mahasak Ketcham the Department of Information Technology Faculty of Information Technology King Mongkut’s University of Technology North Bangkok Bangkok Department of Information Technology Management Faculty of Information TechnologyKing Mongkut’s University of Technology North Bangkok Bangkok

In this paper,we proposed to develop Handwriting Thai Signature Recognition System（HTSRS） based on multilayer perceptron and radial basis *** order to achieve this objective,a combination of back-propagation and generalized regression neural networks is *** first stage is implemented by using two parallel back propagation and generalized regression neural network in order to reduce training time and make a system *** second stage is implemented by using the generalized regression neural network for final *** system is composed of two principal phases:signature feature extraction and *** the extraction step,global and grid features of all signature images are first *** this features are feed into the first and second stage neural network classifier for *** second group is for testing and unknown *** experimental results show that the accuracy of the proposed system performs rate is 90.00%.

关键词： Signature Recognition Neural Network Multilayer Perceptron back propagation algorithm Generalized Regression Network

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Neural Network Solution of the Circular Loop Antenna Radiation Problem

Neural Network Solution of the Circular Loop Antenna Radiati...

引用

Telecommunications Forum

作者： Theodoros N. Kapetanakis Ioannis O. Vardiambasis George S. Liodakis Andreas Maras Broadband Communications and Electromagnetic Applications Lab Department of Electronics Branch of Chania Technological Educational Institute (TEI) of Crete Romanou 3 Chalepa Chania Greece Department of Telecommunications Science & Technology University of Peloponnese Terma Karaiskaki 22100 Tripolis Arcadia Greece

ISBN: (纸本)9781467329835

The circular cylindrical antenna is a simple, inexpensive, versatile, and very popular antenna type, which has received much attention due its wide range of applications. The exact values of the radiated near and far electromagnetic fields have recently been analytically evaluated, in terms of complex series involving Legendre functions of the second kind and half-integral order. The inverse problem of determining the loop antenna parameters (radius and current) causing specific field levels at one or more points of interest is even more complex. In order to find the solution, avoiding the associated lengthy and time-demanding mathematical analysis, we apply artificial neural network modeling. The proposed models consist of a feed-forward back-propagation and a radial basis neural network trained with theoretical data. The results obtained are found to be in perfect agreement with the exact theoretical data.

关键词： Antenna radiation back propagation algorithm circular loop antenna inverse radiation problem neural networks Legendre functions radial basis function

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ROBUST NEURAL-FUZZY METHOD FOR FUNCTION APPROXIMATION

ROBUST NEURAL-FUZZY METHOD FOR FUNCTION APPROXIMATION

引用

2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： HORNG-LIN SHIEH PO-LUN CHANG Dept.of Electrical Engineering St.John's University Taipei Taiwan Dept.of Electrical Engineering Lunghwa University of Science and Technology and National Taiwan Uni

The back propagation (BP) algorithm for function approximation is multi-layer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter (FDS) is used to partition the nonlinear system's domain into several piecewise linear subspaces to be represented by neural networks. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.

关键词： back propagation algorithm fuzzy neural networks fuzzy clustering robust noises and outliers

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Lithology Determination by Artificial Intelligence Method

Lithology Determination by Artificial Intelligence Method

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The Second International Conference on Information and Management Sciences

作者： Jinmei Wang~1, Jinghui Li~2 1 Daqing Exploration and Development Research Institute, Daqing Oilfield Corporation Ltd., Daqing, Heilongjiang, China 2 Daqing Petroleum Institute, Daqing, Heilongjiang 163318, China

This paper proposes an artificial neural network (ANN) method to calculate reservoir parameters. By improving the algorithm of BP neural network, convergence speed is enhanced and better result can be *** applications... 详细信息

关键词： neural network reservoir evaluation back propagation algorithm

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Investigation of Single and Dual Step Shot Peening Effects on Mechanical and Metallurgical Properties of 18CrNiMo7-6 Steel Using Artificial Neural Network

Investigation of Single and Dual Step Shot Peening Effects o...

引用

SCIEI 2015 Lisbon Conference

作者： E.Maleki K.Sherafatnia Sharif University of Technology-International Campus Sharif University of Technology

Shot peening is a process of cold working a part that increase its resistance to metal fatigue and some forms of stress *** peening causes plastic deformation in the surface of the peened part and leads some changes in mechanical and metallurgical properties of *** intelligence(AI) systems such as artificial neural networks(ANNs) have found many applications to predict and optimize the engineering problems in the last few *** present study effects of SP on mechanical and metallurgical properties of 18 CrN i Mo7-6 are investigated by *** has been developed based on back propagation error *** order to train the network data of experimental tests results were *** tests were concluding different SP types: single step SP and dual step SP with different SP *** of the ANN is accomplished using experimental data not used during networks *** from the surface and Almen intensity are considered as input parameters and residual stress,remnant austenite content,Cauchy breath,domain size and microhardness are regarded as output parameters of the *** comparison of obtained results of ANN's response and experimental values indicates that the networks are tuned well and the ANN can be used to predict the SP effects on mechanical and metallurgical properties of materials.

关键词： step shot peening mechanical properties artificial neural network back propagation algorithm

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Research on the Forecasting of Inventory Risk Management of Spare Parts:A Neural Network Model

Research on the Forecasting of Inventory Risk Management of ...

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2011 13th IEEE Joint International Computer Science and Information Technology Conference(JICSIT 2011)

作者： Weipeng WANG School of Economics and Management Weifang University Weifang, China

This paper proposes a neural network-based classification approach to inventory risk level of spare parts. Firstly a fuzzy evaluation of spare parts is made in terms of their availability of suppliers,importance,prodictability of failure , specificity and lead time. Then a multilayer feed forward neural network model is established. The back propagation (BP) algorithm for training a neural network is used to decide the weights to connections in the model. Choosing a sample of historical data of 100 spare parts and undertaking a BP training stimulation,the model is used to predict the inventory risk levels of 60 spare parts for a well- logging service firm. The forecasting reliability reaches 84%.

关键词： management of spare parts neural network back propagation algorithm

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Off-line Odia Handwritten Numeral Recognition Using Neural Network: A Comparative Analysis

Off-line Odia Handwritten Numeral Recognition Using Neural N...

引用

International Conference on Computing, Communication and Automation

作者： Abhisek Sethv Prashanta Kumar Patra Dept. Computer Science and Engineering College of Engineering and Technology Bhubaneswar India

ISBN: (纸本)9781509016679

Character recognition is one of the most interesting and challenging research areas in the field of image processing. The recognition rate of handwritten character is still limited due to the presence of large variety of shape, scale and format in hand written characters. A sophisticated handwritten character recognition system demands a better feature extraction technique that would take care of such variety of handwriting. The work proposed in this paper is an attempt to develop recognizer for Odia handwritten numeral digits based on Binarization and discrete cosine transform (DCT) scheme. The recognizer puts emphasis on exploiting the inherit characteristics of Odia numeral images. The system first employs the techniques like thinning, foreground and background noise removal, cropping and size normalization etc., to preprocess the character images. Binarization and DCT technique is employed separately to extract the features from the images. Subsequently, these feature vectors are sent to the neural network classifier. Extensive simulations show that the results are very promising over a standard dataset and the recognition rate for Binarization and DCT are 80.2% and 90%, respectively.

关键词： Character Recognition (CR) Binarization Discrete cosine transform (DCT) Feature Extraction back propagation algorithm Neural Network

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Application of artificial neural networks coupled with an orthogonal design to simulation for powder mixed EDM

Application of artificial neural networks coupled with an or...

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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Fuling Zhao Yuangang Wang Hui Wang Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)0780393953

Electric discharge machining (EDM) is one of the most accurate manufacturing processes available, and it is quite indispensable to aerospace industries. Powder mixed EDM (PMEDM) is a new process through which a better surface can be reached, nevertheless, PMEDM is a complicated and random process influenced by many parameters, and the intricacies of mechanism hinders its application. This paper presents a method that can be used to forecast the surface roughness in the PMEDM process with the application of artificial neural networks (ANNs). ANNs with back propagation (BP) algorithm coupled with an orthogonal design was applied to the modeling of the PMEDM system and simulation of experimental conditions. An orthogonal design was utilized to design the experimental protocol, in which peak current, pulse-on time, discharging area, servo voltage and no-load voltage were varied simultaneously. The results showed that the ANNs model reflected the complex relationship of PMEDM and had high predicting accuracy.

关键词： backpropagation electrical discharge machining neural nets production engineering computing surface roughness ANN BP algorithm PMEDM process artificial neural networks back propagation algorithm electric discharge machining orthogonal design powder mixed

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Study on the Experimental Method of Film Thickness Control for Robot Spraying in the Shipbuilding Industry

Study on the Experimental Method of Film Thickness Control f...

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The 2015 International Conference on Advances in Construction Machinery and Vehicle Engineering(ICACMVE’2015)

作者： Yang Liansheng Li Aiping Liu Xuemei Qi Guodong Luo Xi School of Mechanical Engineering Tongji University Shanghai Waigaoqiao Shipbuilding Co.Ltd.

The experimental methods of the painting film thickness distribution sprayed by painting robot in shipbuilding industry are studied in this ***,the factors influencing the film distribution is theoretically analyzed and the experimental procedure is designed according to the orthogonal experiment design(OED) *** paper uses the least square method and back propagation(BP) algorithm to fit the data Levenberg Marquardt algorithm(LM-BP) and Bayesian regularization algorithm(BR-BP) are used to optimize the training function By comparing the mathematics models through different methods,the results show that the LM-BP is the most suitable main algorithm for coating application film thickness distribution control model when industrial robots used in shipbuilding spraying.

关键词： orthogonal experiment design painting film thickness distribution least square method back propagation algorithm

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Intelligent Fault Diagnosis System Research on AeroEngine

Intelligent Fault Diagnosis System Research on AeroEngine

引用

2011 International Symposium on Computer Science and Society

作者： Peishu Qu Wenhui Dong Zhiguo Sang Yong Sheng Department of Physics Dezhou University Department of Physics Dezhou University Department of assets management Dezhou University

This paper, we take neural network technology into the field of test of aircraft engine endoscopic. introduced general framework of the diagnostic expert system of aircraft engines based on neural network and giving an improved reasoning method, established the BP network model, take the common faults of B747-200F's CFM56 engine, for example, take a simulation test of fault diagnosis, and compared with the actual fault data, proved that the system can intelligently determine fault type, Which can further help quickly and accurately locate and solve the fault for aircraft maintenance personnel,and improving the work efficiency, so it has very greater practical value.

关键词： aeroengine Expert system video probe inspection artificial neural network back propagation algorithm

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