钢板缺陷识别的Volterra-SVM模型研究

针对钢板缺陷识别问题，结合超声波脉冲反射原理，提出一种基于Volterra级数和支持向量机的钢板缺陷识别方法。首先，利用Volterra级数模型建立起钢板缺陷的特征模型；其次，使用分数阶粒子群优化算法提取出原始信号中的特征参数，即Volterra级数时域核；最后，将提取到的特征向量输入支持向量机模型进行训练与测试，完成对钢板缺陷的分类识别。设计实验得到多组数据样本，进行模型验证，实验结果表明：基于Volterra级数和支持向量机的识别模型能够较好的完成对钢板缺陷的分类识别，识别准确率达93.3%。

Study on Volterra-SVM Model for Defect Recognition of Steel Plate

Aiming at the problem of steel plate defect recognition, combined with the principle of ultrasonic pulse reflection, a steel plate defect recognition method based on Volterra series and Support Vector Machine (SVM) is proposed. Firstly, Volterra series model is used to construct the characteristic model of steel plate defects. Then, the feature parameters in the original signal, namely Volterra series kernel, are extracted by using the Fractional Order Particle Swarm Optimization (FO-PSO). Finally, the extracted feature vectors are input into the SVM model for training and testing to complete the classification and recognition of steel plate defects. Experiments were designed to obtain multiple sets of data samples for model validation. The experimental results show that the recognition model based on Volterra series and SVM can better complete the classification and recognition of steel plate defects, and the recognition accuracy is 93.3%.