量子BP神经网络的自适应振动信号压缩及应用

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振动与冲击 ›› 2014, Vol. 33 ›› Issue (19) : 35-39.

论文

量子BP神经网络的自适应振动信号压缩及应用

王怀光1，张培林1，李胜1，吴定海1，周云川2

作者信息 +

Self Adaptive for Vibration Signal Compression Based on Quantum BP Neural Network and Its Application

Wang Huaiguang1, Zhang Peilin1, Li Sheng1, Wu Dinghai1, Zhou Yunchuan2

Author information +

文章历史 +

摘要

针对一维振动信号的压缩特点，提出一种改进的量子BP神经网络（IQBPN）信号压缩方法。本文根据一维振动信号的方差，将信号分为四个部分：平滑区、半平滑区、半边界区和边界区，从而可以选择不同的压缩比来对不同的区域进行压缩，以保持信号的丰富细节，保障压缩的质量。同时，利用量子BP神经网络的计算并行性和算法加速能力，从而提高了神经网络的收敛速度，缩短了压缩时间，为在线实时传输提供了一种新方法。实验证明，与其他方法相比，本文算法在相同的压缩比时，可以提高信噪比，缩短运行时间。

Abstract

Aimed at compression characteristic of one-dimension vibration signals, a signal compression method based on improved quantum BP neural network is proposed. Based on variance of vibration signal, the signal had been divided into four areas: smooth area, half-smooth area, half-boundary area and boundary area. For different areas, different compression ratio was chosen to keep many details and compression quality. Meantime, using concurrent calculation and algorithm acceleration of quantum BP neural network, the convergence speed is improved and the compression time is shortened. The experimental results showed that, compared with current methods in the same compression ratio, the proposed method can improve SNR and shorten execution time.

导出引用

王怀光;张培林;李胜;吴定海;周云川. 量子BP神经网络的自适应振动信号压缩及应用[J]. 振动与冲击, 2014, 33(19): 35-39

Wang Huaiguang;Zhang Peilin;Li Sheng;Wu Dinghai;Zhou Yunchuan. Self Adaptive for Vibration Signal Compression Based on Quantum BP Neural Network and Its Application [J]. Journal of Vibration and Shock, 2014, 33(19): 35-39