CAJ | 학술논문

针对非平稳的数字调制信号,构造新的高阶交又累量特征;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.
침대비평은적수자조제신호,구조신적고계교우루량특정;이용신경망락적학습궤제실현자괄응모호추리조제식별기적비선성동태건모;채취분층결책적급련결구,제고료특정여식별기적계합도,최대정도상감소료대속도함수화모호규칙적용여;근거특정양본적대치분포건립온함초시경험적급련모호신경망락계통,사지식추리결구명학가공;통과양본훈련실현결구삼수자괄응조정화우화,완성기핍근구정.방진실험증명,해계통재신조비등배경삼수변화교대적정황하구유경호적은건성,기산법식별솔화효솔상대우신경망락식별기화모호식별기유명현제고.
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.