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检索条件"主题词=Back-propagation Algorithm"

共 209 条记录，以下是151-160 订阅

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Development of automatic control system for laser welding using neural network technology

Development of automatic control system for laser welding us...

引用

UKACC International Conference on Control 98

作者： Gavrilov, AI Bauman Moscow State Tech Univ Moscow Russia

ISBN: (纸本)085296708X

Neural network technology applications in automatic control system for laser welding are considered, fundamental concepts of neural network are outlined. Attention is drowning to the design of adaptive control of laser welding technological process. Structural scheme of automatic control system is presented together with control algorithm.

关键词： intelligent control system adaptive control neural network back-propagation algorithm laser welding

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A Computation Modification for Multi-layered Neural Network Using Extended Kalman Filter 18

A Computation Modification for Multi-layered Neural Network ...

引用

10th International Conference on Computer Modeling and Simulation (ICCMS)

作者： Kim, Kyungsup Kim, Hui-Joon Won, Yu-Jae Chungnam Natl Univ Dept Comp Engn Daejeon South Korea

ISBN: (纸本)9781450363396

A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We need to develop an efficient algorithm for deep neural network. The Kalman filter concept can be applied to parameter estimation of neural network to improve computation performance. The algorithms based extended Kalman filter has a serious drawback in its computational complexity. We discuss how a fast algorithm should be developed for reduction in computation time.

关键词： Multi-layered neural network extended Kalman filter Machine learning back-propagation algorithm optimization

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Optical learning neural network with fuzzy controlling

Optical learning neural network with fuzzy controlling

引用

Conference on Optics in Computing 2000

作者： Mori, M Yagai, Y Lin, X Yatagai, T Electrotech Lab Div Optoelect Tsukuba Ibaraki 3058568 Japan

ISBN: (纸本)0819437328

We constructed a learning optical neural network with variable learning coefficient by fuzzy controlling. The system performs learning with two-dimensional optical means for handling images without scanning and pixeling. By the fuzzy controlling;theory, the learning coefficient in back-propagation algorithm is adjusted based on the training error and training time. The effectiveness of the system confirmed by the learning experiments of the recognition of three human faces.

关键词： neural networks optical learning system fuzzy controlling back-propagation algorithm

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Two regimes of computer hyphenation - a comparison

Two regimes of computer hyphenation - a comparison

引用

International Joint Conference on Neural Networks (IJCNN 01)

作者： Kristensen, T Langmyhr, D Bergen Univ Coll Dept Comp Sci NO-5020 Bergen Norway

This paper presents two different regimes to automatically hyphenate Norwegian text. One method is based on a back-propagation neural network while the other uses the TEX algorithm. The two approaches are described an... 详细信息

ISBN: (纸本)0780370449

关键词： computer hyphenation TEX hyphenation algorithm back-propagation algorithm

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Parallel Character Reconstruction Expending Compute Unified Device Architecture

引用

2nd International Conference on Advances in Computing and Informaiton Technology (ACITY 2012)

作者： Pal, Anita Srivastava, Kamal Kumar Kumar, Atul Shri Ramswaroop Mem Coll Engn & Management Dept Comp Sci & Engn Lucknow UP India

ISBN: (纸本)9783642315510

Neural networks, or the artificial neural networks to be more precise, represents a technology that is rooted in many disciplines: neuroscience, mathematics, statistics, physics, computer science and engineering. Neural network finds applications in such fields as modeling, time series analysis, pattern recognition signal processing and control by virtue of an important property: the ability to learn from input data with or without a teacher An a biological system, learning involves adjustments to the synaptic connections between neurons same for artificial neural networks (ANNs) works too that has made it applicable to valid applications. Neural Network architecture has the ability to learn for the things and then later on classify the things. Neural Network for Character Recognition is based over Multi layered Architecture having back-propagation algorithm. First Network is been trained for the alphanumeric handwritten characters and then testing the network with the trained or untrained handwritten characters. We achieved a greater computation enhancement by using modified back- propagation algorithm having an added momentum term, which lowers the training time and speeds the system. The time is more reduced with its parallel implementation using CUDA.

关键词： CUDA GPU back-propagation algorithm

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Through-the-Wall Imaging Using Wi-Fi Signals 12

Through-the-Wall Imaging Using Wi-Fi Signals

引用

12th International Symposium on Antennas, propagation and Electromagnetic Theory (ISAPE)

作者： Zhong, Wei He, W. Kai Wang, Longgang Li, Lianlin Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538673027

In this work, we investigate, for the first time, the high-resolution three-dimensional (3D) through-the-wall imaging using the everyday wireless communication signals, i.e., Wi-Fi signals, in an indoor environment. We also provide physical insights into such imaging technique, which has been derived from the well-known Huygens principle. Experimentally, the data acquisition has been realized in a synthetic aperture way, and the classical back-propagation algorithm is employed to form the high-resolution imaging. Moreover, we use IEEE 802.11n protocol wireless router working at 2.4GHz, the bandwidth is 20MHz, in our experiments. It can be faithfully expected that such imaging technique can open an exciting new door for various practical applications including safety screening and others.

关键词： through-the-wall Wi-Fi signals back-propagation algorithm

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A Distributed MIMO Relay Scheme Inspired by backpropagation algorithm

A Distributed MIMO Relay Scheme Inspired by Backpropagation ...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Wang, Rui Jiang, Yi Zhang, Wei Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9781728181042

This paper studies a distributed scheme for a multi-input multi-output (MIMO) relay network, where the transmit nodes are subject to the nonlinear instantaneous power constraints. We introduce a novel perspective of regarding a relay network as a so-termed quasi-neural network by drawing its striking analogies with a (four-layer) artificial neural network (ANN). We propose a nonlinear amplify-and-forward (NAF) scheme inspired by the back-propagation (BP) algorithm, namely the NAF-BP, to optimize the transceivers to maximize the output signal-to-interference-plus-noise ratio (SINR) of the data streams. The NAF-BP algorithm can be implemented in a distributed manner with no channel state information (CSI) and no data exchange between the relay nodes. The NAF-BP can also coordinate the distributed relay nodes to form a virtual array to suppress interferences from unknown directions. Extensive simulations verify the effectiveness of the proposed scheme.

关键词： relay network quasi-neural network interference suppression back-propagation algorithm nonlinear amplify-and-forward

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Knowledge Based Approach for Diagnosis of Breast Cancer

Knowledge Based Approach for Diagnosis of Breast Cancer

引用

IEEE International Advance Computing Conference

作者： Shukla, Anupam Tiwari, Ritu Kaur, Prabhdeep ABV Indian Inst Informat Technol & Management Gwa Dept Informat Commun & Technol Gwalior India

ISBN: (纸本)9781424429271

This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like Artificial Neural Networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the back propagation algorithm (BPA), the Radial Basis Function (RBF) Networks and the Learning Vector Quantization (LVQ) Networks;and also by Adaptive Neuro Fuzzy Inference System (ANFIS). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The simulation results show that this Knowledge Based Approach can be effectively used for early detection of Breast Cancer to help oncologists to enhance the survival rates significantly.

关键词： Knowledge Based System Breast cancer Diagnosis Neural Networks Neuro-Fuzzy Systems back-propagation algorithm Radial Basis Function Networks Learning Vector Quantization Networks Comparison

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New Artificial Intelligence based Tire Size Identification for Fast and Safe Inflating Cycle

New Artificial Intelligence based Tire Size Identification f...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Kahandawa, Gayan Choudhury, T. A. Ibrahim, M. Yousef Dzitac, Pavel Mazid, Abdul Md Federat Univ Australia Sch Engn & Informat Technol Gippsland Vic Australia Deakin Univ Sch Engn Geelong Vic 3217 Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia

ISBN: (纸本)9781479978007

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

关键词： artificial neural network back-propagation algorithm sensors tire safety microcontroller applications

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THE POSITIVE AND RANDOM IMPULSE NOISE REDUCTION USING ANN AND GAUSSIAN RECURSIVE FILTER

THE POSITIVE AND RANDOM IMPULSE NOISE REDUCTION USING ANN AN...

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World Automation Congress (WAC) on Emerging Technologies for a New Paradigm in System of Systems Engineering

作者： Bari, Mehrab Ghanat Bari, Fatemeh Ghanat Zhang, Jianqiu Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX USA IAU Dept Elect & Comp Engn Orumiyeh Iran

ISBN: (纸本)9781889335490

In this paper, a new method of filtering is introduced which can be adopted to neural networks, which can be applied to improve the corrupted images by salt-pepper noises. In the first step, neural networks are used to identify the location of the noises in image and in the next step;the identified noisy pixel will be reduced by using Gaussian recursive filter. This method is called the Neural Network Gaussian (NNG) filter. Using neural networks to recognize the location of salt-pepper noises prevents incorrect recognition of noise and increase the quality of noise reduction process. Moreover, by using Gaussian recursive filters against the typical median filter, which used only to omit trivial noises, the algorithm performance will also be improved significantly.

关键词： Image processing Multilayer neural network back-propagation algorithm Gaussian recursive filter

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