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中文摘要：
针对M-Z干涉仪型光纤分布式扰动传感系统输出信号短时频率随外界扰动变化的特征,提出了基于短时频率-时间特性的模式识别算法。采用提取短时过电平率来描述传感信号的短时平均频率-时间特性,并将提取出来的时频特性分段后建立相应的特征元素模型,通过动态规划算法（DTW）筛选出最优特征元素模型,将信号所有最优模型的参数作为信号特征输入到人工神经网络（ANN）进行学习和判决,降低了ANN的训练难度以及对时间的敏感性,提高了系统的环境适应能力。实验结果表明：该方法可以有效区分瞬时作用、长时作用、径向作用和不规则作用等多种不同扰动事件,平均识别速度在0.26 s之内,平均识别准确度在97%以上。
英文摘要：
A frequency-time based pattern recognition method was presented for recognizing different disturbance modes in fiber distributed disturbance sensing system based on M-Z interferometer by using the frequency of output interferometer signal in relation to the external disturbance signal. The frequency- time characteristic was measured by using the rate which the output signals crossed the preset average level. Then frequency-time characteristic was segmented and the corresponding feature element model could be set up. The optimum models were selected by using dynamic time warping （DTW） algorithm, and then they were sent to the artificial neural network （ANN） to carry out training and judging. This method could effectively reduce the difficulty of training and judging the signal and time sensitivity of the ANN, and improve the adaptability for the environment. Experimental results illustrate that this method can effectively distinguish different disturbance events such as short-term, long-term, radial and irregular event. The average recognition speed is less than 0.26 s and average accuracy is great than 97%.
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