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马洪, 张可数, 游志胜, 梅田三千雄
电子学报,2001,29(9):1161~1163,-0001,():
-1年11月30日
本文用基于最小均方误差准则的最优门限方法估计叠加高斯白噪声的分形布朗运动,并给出其离散小 波变换分解级数确定方法。与多尺度维纳滤波相比,本方法不需估计1/f类分形信号的方差,且其离散小波变换分解级数可预先确定,因此有着更好的实用性和可操作性。
分形信号, 分形布朗运动, 1/, f过程, 信号估计, 小波变换
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马洪, 赵娟, 游志胜, 梅田三千雄
电子学报,2001,29(9):1157~1160,-0001,():
-1年11月30日
本文基于多尺度卡尔曼滤波方法来估计淹没在加性高斯白噪声中的分形布朗运动。针对每一尺度,给出了相应的动态系统参数和运动模型方程以及更精确的估计算法。并与多尺度维纳滤波进行了对比,计算机仿真结果证明了其优越性。
分形随机信号, 分形布朗运动, 1/, f过程, 卡尔曼滤波, 小波变换
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【期刊论文】Synchronization of two 3-scroll hyperchaotic attractors using wavelet transform
马洪, Li Jian, Zhou Jiliu, Wang Yong & Zhi Yong
Journal of Systems Engineering and Electronics, Vol. 17, No.2, 2006, pp. 387~389,-0001,():
-1年11月30日
The synchronization of two 3-scroll hyperchaotic attractors is realized based on wavelet transform and single variables feedback. In the transmitter, one signal is decomposed by wavelet transform and the detailed information is removed, then the component with low frequency is reconstructed and sent into the channel. In the receiver, the received signal is used as the feedback signal to realize the synchronization of two chaotic systems. Using this synchronous method, the transmitting signal is transported in compressible way, the system resource is saved, furthermore, because the transported signal is not a whole chaotic signal, the performance of security of the system is improved.
wavelet transform,, 3-scroll hyperchaotic attractor,, synchronization,, single variable', s feedback.,
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马洪, MA HONG
Ma, H. Osaka J. Math. 24 (1987), 321-330,-0001,():
-1年11月30日
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马洪, SEONG-WHAN LEE, †‡ CHANG-HUN KIM, † HONG MA§ and YUAN Y. TANG‖
Pattern Recognition, Vol. 29, No. 12, pp. 1953-1961, 199,-0001,():
-1年11月30日
In this paper, we propose a new scheme for multiresolution recognition of unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, Electro-Technical Laboratory of Japan, and Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes. Copyright.
Multiresolution recognition, Handwritten numeral recognition, Multilayer cluster neural network
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