%0 Journal Article
%T Adaptive Hierarchical Fuzzy Inference-based Modulation Recognition Algorithm<br>基于自适应级联模糊推理的调制识别算法
%A CHEN Xiao-qian
%A WANG Hong-yuan
%A <br>陈筱倩
%A 王宏远
%J 计算机科学
%D 2009
%I 
%X For the non-stationary digitally modulated signal a novel feature; High Order Cross Cumulant (HOCC) was proposed. The non-linear dynamic modeling of an adaptive fuzzy modulation classifier, which based on the training mechanism within the neural network, was first presented. The model adopted the hierarchical decision-based structure,which made the features match the classifier and reduced the redundancy of the membership functions and fuzzy rules.According to the distribution of the feature samples, we established the Hierarchical Fuzzy Neural Network System (HFNNS) with initial experience to guarantee the controllability of the knowledge inference structure. By applying the training data,the algorithm adaptively adjusted and optimized the structure parameter and completed the approximation process. The simulation results verified the better robustness of the system in the presence of various environment parameters (SNR etc) , as well as the improvement of the average probability of correct classification and the algorithm efficiency, compared with the neural network classifier and fuzzy classifier.
%K Modulation recognition
%K HFNNS
%K HOCC
%K Fuzzy inference
%K Adaptation<br>调制识剐
%K 级联模糊神经网络
%K 高阶交叉累量
%K 模糊推理
%K 自适应
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=C2A5A0D5E555493B5659EDFB771751D0&yid=DE12191FBD62783C&vid=933658645952ED9F&iid=59906B3B2830C2C5&sid=DD74772618543076&eid=50BBDFAC8381694B&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=7