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复数Hopfield神经网络在路牌识别中的应用 被引量：2

Road Sign Recognition Using Complex-Valued Hopfield Neural Network
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摘要 本文介绍了多态神经元模型和复数Hopfield神经网络,给出了网络权值确定的两种方法:复数Hebbian学习规则和内积法构造厄米矩阵。另外借助于二维离散傅立叶变换给出了灰度图相位变换和反变换的方法,实现了复数Hopfield神经网络对灰度图的存储。在此基础上借助于网络的联想记忆特性,将其应用于交通路牌的识别中。 A multistate neuron and complex valued network are introduced.Tow ways are given to determine its weight mastics：complex valued Hebbian rules and hermitian mastics inner product.By using the tool of 2-dimensional Fourier Transform,image phase transform and its inverse transform,the storage of gray scale image is realized.Take advantage of the associative property of the network,it is used in road sign recognition.
作者 杨杰 王直杰 董宗祥
机构地区 东华大学信息科学与技术学院
出处 《微计算机信息》 2010年第22期161-163,共3页 Control & Automation
关键词 复数Hopfield神经网络 多态神经元 灰度图 相位变换 路标识别 Complex-Valued Hopfield Neural Network（CVHNN） Multistate Neuron Gray Scale Image Storage Image Phase Transform Road Sign Recognition
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献6

  • 1J.J.Hopfield: Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci., USA, 79, pp.2554-2558, 1982.
  • 2H. Aoki, Y. Kosugi: An Image Storage System Using Complex- Valued Associative Memories. 15th International Conference on Pattern Recognition Barcelona 2000 Vol. 2, pp.626-629 Sept. 2000.
  • 3A. Hirose: Proposal of fully complex-valued neural networks, Proc. of IJCN'92 Baltimore, U.S.A., vol.IV, pp.152-157, 1992.
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  • 5Stanislaw Jankowski, Andrzej Lozowski, and Jacek M. Zurada: Complex-Valued Multistate Neural Associative Memory, IEEE Trans. On Neural Networks, Vol.7, No. 6, Nov.1996.
  • 6马晶,雷勇,涂国强,杨健熙,穆巍.基于DSP的图像处理在车牌识别中的应用[J].微计算机信息,2009,25(29):133-135. 被引量：7

二级参考文献4

  • 1Shyang-lih Chang, Li-Shien Chen, Yun-Chung Chung. Automatic license plate recognition. IEEE Intelligent Transportation System Vol. 5, Issue 1, March 2004: 42-53.
  • 2Rahman C.A, Badawy W, Radmanesh A. A real time vehicle's license plate recognition system [J]. IEEE Conference on Advanced Video and Signal based Surveillance, July 2003: 163-166.
  • 3J. Rev, TMS320C6713B Floating-point Digital Signal Processors [M]. Texas Instruments Incorporated,2005.2.
  • 4夏培容,石中锁,董平.汽车牌照自动识别系统[J].微计算机信息,2003,19(3):35-36. 被引量：10

共引文献6

同被引文献31

  • 1LAMPINEN J, VEHTATI A. Bayesian approach for neural networks: review and case studies[J]. Neural Networks, 2001, 14(3): 257-274.
  • 2ZHANG Jiye, SUDA Y, IWASA T. Absolutely exponential stability of a class of neural networks with unbounded delays [ J ]. Neural Networks, 2004, 17(3) : 391-397.
  • 3SHAO Jinliang, HUANG Tingzhu, ZHOU Sheng. Some improved criteria for global robust exponential stability of neural networks with time-varying delays [ J ]. Communications in Nonlinear Science and Numerical Simulation, 2010, 15 (12) : 3782-3794.
  • 4ZHANG Huaguang, WANG Zhanshan, LIU Derong. Global asymptotic stability of recurrent neural networks with multiple time-varying delays [ J]. IEEE Trans- actions on Neural Networks, 2008, 19(5) : 855-873.
  • 5LIN Da, WANG Xingyuan. Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters [ J]. Neurocomputing, 2011, 74( 12/13): 2241-2249.
  • 6HUANG Yujiao, ZHANG Huaguang, WANG Zhanshan. Dynamical stability analysis of multiple equilibrium points in time-varying delayed recurrent neural networks with discontinuous activation functions[ J]. Neurocomputing, 2012, 91 ( 1 ) : 21-28.
  • 7BAO Gang, ZENG Zhigang. Analysis and design of associative memories based on recurrent neural network with discontinuous activation functions[ J]. Neurocomputing, 2012, 77(1) : 101-107.
  • 8ZHU Song, LOU Weiwei, SHEN Yi. Robustness analysis for connection weight matrices of global exponential stability of stochastic recurrent neural networks[ J]. Neural Networks, 2013, 38(1) : 17-22.
  • 9ENSARI T, ARIK S. New results for robust stability of dynamical neural networks with discrete time delays[Jl. Expert Systems with Applications, 2010, 37 (8) : 5925-5930.
  • 10SREE H R V, MURTHY G R. Global dynamics of a class of complex valued neural networks[J]. International Journal Neural Systems, 2008, 18 (2) : 165-171.

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微计算机信息
2010年 第22期

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