为实现准确快速得到去除噪声后α射线谱,采用了一套可以让探测器处于不同真空条件下的探测设备.将实验研究获得的α能谱中能表征能谱信息的特征参数如峰值、不同高度的左右半宽度和左右边界,在遗传算法中经选择、交叉操作进行权值和阈值的优化后,作为输入层数据对BP神经网络进行训练.经过反复训练最终得到去噪后的输出结果.实验结果表明:对于α能谱的去噪,遗传算法优化后的BP神经网络比未被优化的效果要好.%In order to approach an accurate and immediate alpha spectrometry with noise subtraction, a set up with different vacuum conditions was used. Several parameters obtained from experimental alpha spectroscopy and indicating properties of spectroscopy, e.g. peak value, half - peak width and boundaries,were optimized in terms of weight and threshold by selecting and cross-operating in genetic algorithm prior to be trained in BP nerve network as input data. Experimental results show that the optimized BP nerve network is better than the non-optimized one in terms of alpha spectroscopy noise subtraction.
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