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基于神經(jīng)網(wǎng)絡(luò)的混沌信號源的設(shè)計及同步

張家樹,  肖先賜

張家樹, 肖先賜. 基于神經(jīng)網(wǎng)絡(luò)的混沌信號源的設(shè)計及同步[J]. 電子與信息學(xué)報, 2002, 24(1): 37-44.
引用本文: 張家樹, 肖先賜. 基于神經(jīng)網(wǎng)絡(luò)的混沌信號源的設(shè)計及同步[J]. 電子與信息學(xué)報, 2002, 24(1): 37-44.
Zharig Jiashu, Xiao Xianci. Chaotic signal source design and synchronization based on neural networks[J]. Journal of Electronics & Information Technology, 2002, 24(1): 37-44.
Citation: Zharig Jiashu, Xiao Xianci. Chaotic signal source design and synchronization based on neural networks[J]. Journal of Electronics & Information Technology, 2002, 24(1): 37-44.

基于神經(jīng)網(wǎng)絡(luò)的混沌信號源的設(shè)計及同步

Chaotic signal source design and synchronization based on neural networks

  • 摘要: 該文應(yīng)用具有全局最優(yōu)的BP改進算法和神經(jīng)網(wǎng)絡(luò)的強大學(xué)習(xí)能力、逼近任意非線性能力和權(quán)值調(diào)整的靈活性來優(yōu)化混沌信號源的設(shè)計,采用非線性負反饋實現(xiàn)了神經(jīng)網(wǎng)絡(luò)混沌信號源之間的同步。計算機仿真結(jié)果表明:由于該模型充分利用了逼近任意非線性能力和網(wǎng)絡(luò)權(quán)值調(diào)整的靈活性,比單一混沌映射能產(chǎn)生更多的、具有良好相關(guān)性能的混沌信號,且易于同步。
    Abstract: Chaotic signal source design based on neural networks trained by modified back propagation algorithm and synchronization by nonlinear feedback method have been proposed in this paper. This neural network source can very easily generate many chaotic signals by changing synaptic weights of neural networks. Nonlinear feedback term is used to synchronize chaotic signal sources designed by neural networks. Experimental results show that these chaotic sources can generate much more signal than other chaotic sources designed by the single chaotic map, and are very easily synchronized by the same nonlinear feedback method.
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  • G. Heidari-Batem, C. D. McGillem, A chaotic direct-sequence spread-spectrum communication system, IEEE Trans. On Communication, 1994,42(2/3/4), 1524-1527.[2]D.S. Morrantes, D. M. Rodrigues, Chaotic sequence for multiple access, Electron. Lett., 1998,34(3), 225-227.[3]Y.C. Lai, E. Billt, C. Grebogi, Communicating with chaos using two-dimensional symbolic dynamics, Phys. Lett. A, 1999, 255(1), 75-81.[4]L.M. Frey, Chaotic digital encoding, An approach to secure communication, IEEE Trans. on Circuit Syst.(Ⅱ), 1993, 40(10), 660-666.[5]L.M. Pecora, T. L. Carrol, Synchronization in chaotic systems, Phys. Rev. Lett., 1990, 64(8),821-824.[6]U. Feldman, M. Hasler, W. Schwarz, Communication by chaotic signals, The inverse system approach, Int. J. Circiut Theory and Application, 1996, 24(6), 551-579.[7]S. Haykin, X. B. Li, Detection of signals in chaos, Proc. IEEE, 1995, 83(1), 95-122.[8]K.M. Short, Steps toward unmaking secure communications, Int. J. Bifurcation and Chaos.1994, 4(4), 959-977.[9]K.M. Short, Signal extraction from chaotic communication, Int. J. Bifurcation and Chaos, 1997,7(7), 1579-1697.[10]K. Hornik, M. Stinchcombe, H. White, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks, 1990, 3(10), 535-549.[11]A. M. Albano, A. Passamante, T. Hediger, M. E. Farrell, Using neural nets to look for chaos.Physica D, 1992, 58(1), 1-9.[12]荊濤,徐勇,揚懷江,宋建中,一種基于神經(jīng)網(wǎng)絡(luò)的混沌序列產(chǎn)生方法,通信學(xué)報,1999,20(6),77-81.[13]張家樹,肖先賜,混沌時間序列的自適應(yīng)高階非線性濾波預(yù)測,物理學(xué)報,2000 , 49(7)1221-1226.[14]張家樹,肖先賜,混沌時間序列的Volterra自適應(yīng)濾波預(yù)測,物理學(xué)報,2000,49(3),403-408[15]朱偉勇,王興元,二維Logistic映射中的陣發(fā)混沌與分形,東北大學(xué)學(xué)報(自然科學(xué)版),1998.19(5),509-512.[16]Zhang Jia-Shu, Xiao Xian-Ci, Fast evolving multi-layer perceptrons for noisy chaot ic time series modeling and predict ions, Chinese Physics, 2000, 9(6), 408-413.
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  • 文章訪問數(shù):  1998
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出版歷程
  • 收稿日期:  1999-12-23
  • 修回日期:  2000-08-24
  • 刊出日期:  2002-01-19

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