ART Ⅱ网络以模式的相似性量度值为基础,能够对动态的输入模式样本进行自适应的聚类和识别,然而标准的ART Ⅱ网络在输入数据处理过程中,忽略了样本数据中的负数信息和幅值信息,造成信号畸变和"同相位不可分"问题,在权值调整...ART Ⅱ网络以模式的相似性量度值为基础,能够对动态的输入模式样本进行自适应的聚类和识别,然而标准的ART Ⅱ网络在输入数据处理过程中,忽略了样本数据中的负数信息和幅值信息,造成信号畸变和"同相位不可分"问题,在权值调整过程中,聚类中心发生移动,容易造成"模式漂移"现象。针对上述问题结合相关文献提出了引入非线性函数对输入数据进行变换的方法解决"同相位不可分"问题,用待测数据与同一模式类中有限数据的欧氏距离与限定值进行比较实现聚类判定,抑制"模式漂移"现象。用Matlab仿真表明,改进算法性能优于标准算法。展开更多
As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory(fuzzy ART) is plagued by th...As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory(fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART(DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating;then, the fuzzy vigilance models(FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region;finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy(quantization error dropped60%) and the de-interleaving performance(clustering quality increased by 10%) while suppressing the excessive proliferation of categories.展开更多
基金supported by the National Natural Science Foundation of China(61571043)the 111 Project of China(B14010)。
文摘As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory(fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART(DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating;then, the fuzzy vigilance models(FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region;finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy(quantization error dropped60%) and the de-interleaving performance(clustering quality increased by 10%) while suppressing the excessive proliferation of categories.
