基于多级小波神经网络的毒品爆炸物能量色散识别

Identification of Drugs and Explosives in Energy Spectrum Based on Multiple Wavelet Neural Networks

利用小波神经网络自适应学习分类的优点,提出将多个小波神经网络并联使用,改进小波网络结构,在每个小波特征空间中确定小波神经元个数和初始化合适的小波基,用多级小波神经网络对毒品爆炸物的X光能量色谱的进行了识别分类。实验表明,用多级小波神经网络可以实现对不同种类毒品爆炸物的识别和鉴定,为X光能量色散技术用于毒品爆炸物的检测和识别提供了一种有效的方法。

Taking advantage of adaptive learning classification of wavelet neural networks, this paper proposes multiple wavelet neural networks used in parallel to improve the wavelet network structure. It also determines the number of wavelet neuron and the appropriate initialization wavelet in each wavelet feature space. The X-ray energy spectrum of drugs and explosives are identified by multiple wavelet neural network. Experiments show that the identification of different types of drugs and explosives can be achieved by multiple wavelet neural network, which provides an effective method for the technology of X-ray energy dispersive used in detection and identification of drugs and explosives.