基于神經(jīng)網(wǎng)絡(luò)混沌擴(kuò)頻序列的研究
Study of Chaotic Spread-Spectrum Sequences Based on Neural Networks
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摘要: 應(yīng)用神經(jīng)網(wǎng)絡(luò)的強(qiáng)大學(xué)習(xí)能力和具有全局最優(yōu)的BP改進(jìn)算法,提出了通過(guò)訓(xùn)練學(xué)習(xí)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(lái)產(chǎn)生混沌序列,該模型序列更換容易并且數(shù) 量巨大。實(shí)驗(yàn)與分析結(jié)果表明該模型產(chǎn)生的混沌擴(kuò)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(kuò)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.
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