RS与FNN集成在焊接缺陷识别中应用

Research and application of integration of RS and FNN in defect recognition of welding

针对焊接图像缺陷识别中提取的特征受噪声干扰比较严重,以及现有的识别算法准确率低的问题,提出了一种粗糙集(RS)和模糊神经网络(FNN)集成的缺陷识别算法.首先使用模糊C-均值聚类算法对样本属性离散化,然后使用RS对离散化后的样本数据进行属性约简得到决策规则,并使用π函数根据聚类的中心和半径对属性进行模糊化,克服RS对噪声敏感问题;根据得到的精简模糊决策规则和模糊逻辑推理确定FNN的结构,使用依赖度和规则的条件覆盖度确定网络的初始参数,考虑到样本中数据的可信度问题,用加权代价函数调整模型参数.仿真实验表明,本算法解决了分类过程中样本数据受到噪声干扰引起的不确定性、FNN结构难以确定的问题,能有效地提高焊缝图像缺陷的识别能力.

Because the detect recognition characteristic extracted from the welding image has been seriously interfered by noises, and the accuracy of existing recognition algorithms is low, a defect recognition algorithm integrating rough set （RS） with fuzzy neural network （FNN） is presented in this paper. Firstly, the fuzzy C- mean （FCM）clustering algorithm was adopted to discretize the attributes of samples, and RS was used to reduce the attributes of sample data and obtain decision rules, then for function was used to fuzzified the attributes of samples according to the center and radius of clustering to overcome the problem that RS is sensitive to noises. Then, the obtained reduced fuzzy decision rules and fuzzy logical inference were used to ascertain the structure of FNN, and dependent factors together with antecedent coverage factors were employed to determine the initial parameters of network. In consideration of the reliability of the data in the samples, the weighted cost function was used to adjust model parameters. The simulation result shows that this algorithm can solve the problems, such as the uncertaint of sample data caused by noise interference in the process of classification and the difficulty in determining the structure of FNN, and can greatly improve the recognition capability of welding image defects.