摘要
在分析分形维、纹理粗细度及二维回归系数共同组成的纹理特征基础上 ,本文运用人工神经网络方法实现了对灰铸铁石墨形态的识别 .相对于传统方法 ,该方法识别速度快、重现性强、数据更为客观准确 ,避免了由于主观判断所引起的人为误差 .在识别过程中 ,本文使用了一种确定隐层节点数的新方法 ,结果显示良好 。
On the base of feature vector composed of fractal parameter, roughness parameter and regression, we complete the classification of the gray cast iron's graphite morphology by ANN. compared with the traditional method, it works much quicker, and is with better repeatability, more objective and veracious results. Furthermore, we apply a new method to decide the hidden node's number of ANN, which shows valuable to such a difficulty in ANN structure decision. Result shows available.
出处
《武汉大学学报（自然科学版）》
CSCD
2000年第3期385-388,共4页
Journal of Wuhan University(Natural Science Edition)
基金
国家科技部科研基金资助项目!(JG-99-9)
关键词
灰铸铁
石墨形态
分形维
BP网络
神经网络
识别
gray cast iron
graphite morphology
fractal
roughness
regression
BP network
