基于神經(jīng)網(wǎng)絡(luò)混沌擴頻序列的研究
Study of Chaotic Spread-Spectrum Sequences Based on Neural Networks
-
摘要: 應(yīng)用神經(jīng)網(wǎng)絡(luò)的強大學習能力和具有全局最優(yōu)的BP改進算法,提出了通過訓練學習建立的具 有混沌性態(tài)的優(yōu)化神經(jīng)網(wǎng)絡(luò)模型;利用網(wǎng)絡(luò)權(quán)值調(diào)整的靈活性來產(chǎn)生混沌序列,該模型序列更換容易并且數(shù) 量巨大。實驗與分析結(jié)果表明該模型產(chǎn)生的混沌擴頻序列具有良好的相關(guān)特性、平衡特性以及理想的線性復(fù) 雜度,是最優(yōu)加密密鑰及擴頻碼的優(yōu)選碼型之一。Abstract: The chaos generation neural network based on the excellent learning ability and synaptic weight database are built to generate many chaotic spread-spectrum sequences trained by the modified back-propagation algorithm with various discrete chaotic time series. The chaotic sequences are very easily generated by changing weights of neural network model, and their number is large. The computer simulation results show that the output chaotic sequences have good correlation property, balance property and linear complexity, therefore they are good candidates for the optimal encrypting code and the spread spectrum code.
-
王亥,胡健棟.改進型Logistic-Map混沌擴頻序列[J].通信學報,1997,18(8):19-23.[2]易繼鍇,侯媛彬.智能控制技術(shù)[M].北京:北京工業(yè)大學出版社,1999:102-108.[3]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].電子與信息學報.2002,24(1):37-44瀏覽[4]He D, He C, Jiang L G, et al.. A chaotic map with infinite collapses[A]. IEEE 2000 ten con Proc.,Kuala Lumpur, Malaysia, Sept. 2000, Ⅲ: 95-99.[5]Rosenstein M T, Collins J J, Luca C J. A practical method for calculating largest Lyapunov exponents from small data sets[J].Physica D.1993, 65(1/2):117-134[6]Massey J L. Shift-register synthesis and BCH decoding[J].IEEE Trans. on Info. Theory.1969,15(1):122-127 -
計量
- 文章訪問數(shù): 2274
- HTML全文瀏覽量: 69
- PDF下載量: 666
- 被引次數(shù): 0
下載:
