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A blind identify algorithm for weak signals in chaotic noise

机译:混沌噪声中弱信号的盲识别算法

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For a kind of weak harmonic wave signal in chaotic noise, this paper proposes a new blind identify algorithm which based on generalized regression neural network (GRNN) to estimate parameters of chaotic system, and then apply reiteration blind deconvolution method to remove remains noise in the system, and extract weak signal from chaotic background. From 2597 observation points, we obtain mean deviation as 0.0112, and peak value signal-noise ratio as −44.77dB. Simulation result shows efficiency of the algorithm.
机译:针对一种混沌噪声中的弱谐波信号,提出了一种基于广义回归神经网络(GRNN)的混沌识别算法,用于估计混沌系统的参数,然后采用迭代盲反褶积方法去除噪声。系统,并从混沌背景中提取微弱的信号。从2597个观察点获得的平均偏差为0.0112,峰值信噪比为-44.77dB。仿真结果表明了算法的有效性。

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