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首页> 外文期刊>Journal of Computers >Signal Denoise Method Based on Fractal Dimension, the Higher Order Statistics and Local Tangent Space Arrangement
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Signal Denoise Method Based on Fractal Dimension, the Higher Order Statistics and Local Tangent Space Arrangement

机译：基于分形尺寸的信号等方法，高阶统计和局部切线布置

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—In denoise method for nonlinear time series based on principle manifold learning, reduction targets are chosen at random, using linear method of singular value decomposition solving local tangent space coordinate, these caused efficiency and effect of denoise lower. To solve this problem, a new denoise method based on based on the fractal dimension, higher order statistics and local tangent space arrangement is proposed. The intrinsic dimension is estimated as dimension of reduction targets by fractal geometry method, the data outside intrinsic dimension space will be regarded as noise signal to be eliminated . At the same time, making use of restraining characteristic to colored noise of high-order cumulan, covariance matrix is constructed with the fourth-order cumulant function instead of second-order moment function covariance matrix ,local tangent space alignment algorithm based on fourth-order cumulan is also proposed. Noise reduction experiments on lorenz signal and fan’s vibrating signal show that method proposed in this paper has better denoise effect.
机译：-IN基于原理歧管学习的非线性时间序列的去噪方法，使用奇异值分解的线性方法选择局部切线坐标的线性方法，这些导致效率和效果较低。为了解决这个问题，提出了一种基于分形维度，高阶统计和局部切线布置的新的去噪方法。估计内在尺寸作为减少目标的尺寸通过分形几何方法，内在尺寸空间之外的数据将被视为噪声信号被消除。同时，利用抑制特征到高阶模块的彩色噪声，协方差矩阵由四阶累积函数而不是二阶力矩函数协方差矩阵，局部切线空间对准算法基于四阶Cumulan也被提出。 Lorenz信号和风扇振动信号的降噪实验表明，本文提出的方法具有更好的效果。

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