基于自适应级联模糊推理的调制识别算法  

Adaptive Hierarchical Fuzzy Inference-based Modulation Recognition Algorithm

作　　者：陈筱倩 王宏远 

机构地区：[1]中科技大学电子与信息工程系,湖北武汉430074

出　　处：《计算机科学》2009年第12期183-186,共4页Computer Science

基　　金：国家自然科学基金项目(60475024)资助

摘　　要：针对非平稳的数字调制信号,构造新的高阶交叉累量特征;利用神经网络的学习机制实现自适应模糊推理调制识别器的非线性动态建模;采取分层决策的级联结构,提高了特征与识别器的契合度,最大程度上减少了隶属度函数和模糊规则的冗余;根据特征样本的大致分布建立蕴涵初始经验的级联模糊神经网络系统,使知识推理结构明确可控;通过样本训练实现结构参数自适应调整和优化,完成其逼近求精。仿真实验证明,该系统在信噪比等环境参数变化较大的情况下具有更好的稳健性,其算法识别率和效率相对于神经网络识别器和模糊识别器有明显提高。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 pa- rameters （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.

关 键 词：调制识别 级联模糊神经网络 高阶交叉累量 模糊推理 自适应 

分 类 号：TN971.1[电子电信—信号与信息处理]