【摘要】 针对金属断口图像模式识别的特点,提出应用小波变换技术提取断口图像特征的方法,在此基础上,利用神经网络的基本原理设计了一种断口图像模式识别的非线性分类器·通过实验确定了分类器的网络结构,给出了相关参数选择的方法·对几种典型的金属断口图像进行了计算机实验研究·实验结果表明,其平均正确识别率达93 75%,单独以能量作为特征值,其平均正确识别率可达到95%·这说明采用非线性分类器进行断口模式识别比采用线性分类器能取得更高、更可靠的正确识别率·研究结果显示出,这种基于小波变换技术和神经网络原理的非线性模式识别方法能对纹理变化复杂、规律性不强的断口图像进行有效识别,具有更好的适应性·更多还原

【Abstract】 Aiming at the characters of pattern recognition of fracture surface images, a character pick-up method by wavelet transform is proposed. Then, based on BP neural network theory, a nonlinear classifier is designed specially for such a pattern recognition, and its network structure was determined by test to give the way to choose parameters involved. Experimental investigations were carried out on computer aiming at several typical metal fracture surface images. The results showed that the average rate of correct recognition achieves 93.75%, even 95% if using the energy as character parameters alone. It means that a nonlinear classifier has a higher and more reliable rate of correct recognition than the linear classifier. The results showed that the nonlinear pattern recognition and classification method based on wavelet transform and BP neural network theory can recognize correctly the images in which the texture is complex, with a better adjustability provided.更多还原