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一定条件下PCNN动态行为的分析 被引量:6

Analysis of Dynamic Behaviors of PCNN under Some Conditions
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摘要 近年来,具有生物学背景的脉冲耦合神经网络(PulseCoupledNeuralNetwork,PCNN)引起越来越多研究者的注意,在图像处理领域得到广泛的应用,对其展开研究具有重要的理论意义及应用价值,但目前相关的研究还远不够深入。至今尚未发现公开发表的研究PCNN动态行为的论文,而对PCNN动态行为展开研究是进一步拓展其应用的基础。该文依次具体分析了由两个乃至多个神经元构成的PCNN在一定条件下的动态行为,得到以下结论:一定条件下,PCNN的脉冲发放形式是周期性振荡的,其动态行为也是周期性振荡的。同时给出了每个振荡周期内各个神经元只点火一次的条件。 For the last several years,more and more researchers pay attention to PCNN —Pulse Coupled Neural Network based on biological experimental results,which h as been widely applied in image processing.Doing research on PCNN has the impor tant significance and value in theories and applications.Today the research of PCNN is still at the begin-ning and more work need to do further.So far no pu blished paper of researches on dynamical behavior has been found.This research work should be done and it is the base of expanding applications of PCNN.In thi s paper,under some conditions,dynamical behaviors of PCNN composed of two ne urons and many neurons are analyzed in detail respectively and authors obtain th e conclusion that under these conditions,the dynamical behaviors of PCNN are pe riodical.Mean-while,the condition guaranteeing that each neuron only fires on ce during each periodicity is given.
出处 《计算机工程与应用》 CSCD 北大核心 2004年第19期6-8,103,共4页 Computer Engineering and Applications
基金 国家自然科学基金资助项目(编号:60171036 30370392) 中国博士后科学基金资助项目(编号:2003034282)
关键词 PCNN 动态行为 神经元 PCNN,dynamical behaviors,neuro n
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参考文献8

  • 1R Eckhorn,H J Reitboeck,M Arndt et al.Feature linking via synchronization among distributed assemblies:Simulation of results from cat cortex[J].Neural Computation,1990;2(3):293~307
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  • 3J L Johnson,M L Padgett.PCNN models and applications[J].IEEE Trans on Neural Networks,1999;10(3):480~498
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  • 5H S Ranganath,G Kuntimad.Object detection using pulse coupled neural networks[J].IEEE Trans on Neural Networks,1999;10(3):615~620
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二级参考文献18

  • 1R Eckhorn,H J Reitboeck,M Arndt et al.Feature linking via synchronization among distributed assemblies :Simulation of results from cat cortex[J].Neural Computation, 1990;2(3) :293-307.
  • 2J L Johnson,M L PadgettPCNN models and applications[J]JEEE Transon Neural Networks. 1999:10(3) :480-498.
  • 3G Kuntimad,H S Ranganath.Perfect image segmentation using pulse coupled neural networks[J].IEEE Trans on Neural Networks,1999;10 (3) :591-598.
  • 4H S Ranganath,G Kuntimad.Object detection using pulse coupled neural networks[J].IEEE Trans on Neural Networks, 1999; 10(3 ) : 615-620.
  • 5H Jcaufield,J M Kinser.Finding shortest path in the shortest time using PCNN's[J].IEEE Trans on Neural Networks,1999;10(3):604--610.
  • 6J L John,D Ritter.Observation of periodic waves in a pulse-coupled neural network[J].Opt Lett, 1993,18( 15 ) : 1253-1255.
  • 7R.P. Broussard, S. K. Rogers, M. E. Oxley, et al., Physiologically motivated image fusion for object detection using a pulse coupled neural network, IEEE Trans. on Neural Networks, 1999,10(3), 554-563.
  • 8X. Liu, D. L. Wang, Range image segmentation using a relaxation oscillator networks, IEEE Trans. on Neural Networks, 1999, 10(3), 564-573.
  • 9J.M. Kinser, Foveation by a pulse-coupled neural network, IEEE Trans. on Neural Networks,1999, 10(3), 621 625.
  • 10J.L. Johnson, M. L. Padgett, PCNN models and applications, IEEE Trans. on Neural Networks,1999, 10(3), 480-498.

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