摘要
采用3层BP神经网络对织物疵点进行识别,提出织物疵点识别网络不适宜规则,将其应用于隐含层神经元个数选择和训练方法筛选,以优化网络结构,提高训练速度和网络识别精度,设计出较优的织物疵点识别网络。将丝织物中常见的断经、断纬、重纬、档疵、破洞和油污6类疵点作为识别样本,对按照网络不适宜规则设计的网络进行测试。从识别结果来看,BP神经网络可以满足织物疵点识别需要,且具有正确识别率高,识别速度快的优点。
A 3-layer BP neural network is used to identify fabric defects.An unsuitability rule is proposed innovatively to optimize the neural network structure,improve the training speed and identification correctness,which is executed during the selection of neuron number in hidden layer and the choice of training methods.The designed neural network is trained with test samples,including warp-lacking,weft-lacking,double weft,loom bar,oil stain and hole,according to the unsuitability rule.Experimental results show that the neural network designed with the way proposed in this paper can satisfy the identification requirement and which possesses advantages of high identification veracity and speed.
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2008年第9期43-46,55,共5页
Journal of Textile Research
关键词
BP神经网络
不适宜规则
织物疵点
疵点识别
BP neural network
the unsuitability rule
fabric defect
defect identification
