郑州大学学报（理学版）2011，Vol.43Issue(1)：95-98,4.
高斯隶属度函数模糊神经网络在肺癌诊断中的应用
The Application of Fuzzy Neural Network with Gaussian Membership Function to Lung Cancer Diagnosis
摘要
Abstract
The usefulness of a fuzzy neural network (FNN) with Gaussian membership function (GMF) for distinguishing between lung cancer and benign cases was studied to improve lung cancer diagnosis. Thirteen non-binary parameters were fuzzed using GMF. The fuzzed outputs added with the other 13 binary parameters served as inputs of the BP neural network. Including lung cancer and benign cases, 117 cases were used to train the FNN. Tens of cases were sampled from the 117 cases at random as training set, and the other cases as validation set. The validation set was used to test the performance of the trained FNN. The performance of the FNN with GMF was compared with that of the FNN with triangle membership function (TMF). The performance of the FNN with GMF was better than that of the FNN with TMF.关键词
高斯隶属度函数/模糊神经网络/三角形隶属度函数/肺癌诊断Key words
Gauss membership function/ fuzzy neural network/ triangle membership function/lung cancer diagnosis分类
信息技术与安全科学引用本文复制引用
徐力平,张华杰,吴逸明..高斯隶属度函数模糊神经网络在肺癌诊断中的应用[J].郑州大学学报（理学版）,2011,43(1):95-98,4.基金项目
国家自然科学基金资助项目,编号30571552. （）
