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一种改进的变步长LMS自适应滤波算法 被引量:4
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作者 石嘉豪 罗雅愉 +2 位作者 刘威杨 梅剑 王为凯 《计算机应用与软件》 CSCD 北大核心 2013年第9期183-186,共4页
针对最小均方算法收敛过程中收敛速度与稳态误差的矛盾,提出一种基于反正切函数的归一化最小均方算法。该算法利用反正切函数和误差自相关的时间估计建立了步长与误差之间的非线性关系,抑制环境中的非相关噪声,同时引入归一化信号功率... 针对最小均方算法收敛过程中收敛速度与稳态误差的矛盾,提出一种基于反正切函数的归一化最小均方算法。该算法利用反正切函数和误差自相关的时间估计建立了步长与误差之间的非线性关系,抑制环境中的非相关噪声,同时引入归一化信号功率扩大输入信号的取值。仿真结果表明,该算法具有较快的收敛速度、较低的稳态误差,同时具备较好的系统时变跟踪能力。 展开更多
关键词 自适应滤波 变步长 最小均方 反正切函数
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New shape clustering method based on contour DFT descriptor and modified SOFM neural network 被引量:1
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作者 刘威杨 徐向民 +1 位作者 梅剑 王为凯 《Journal of Beijing Institute of Technology》 EI CAS 2014年第1期89-95,共7页
A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is ... A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes. 展开更多
关键词 contour shape descriptor discrete Fourier transform (DFT) serf-organizing featuremap (SOFM) neural network K-means algorithm
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