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李师衍达先生,广东南海人,1936年10月生于广东东莞。1954年从广东广雅中学毕业,考入清华大学电机系,后转自动控制系。1957年加入中国共产党。1959年毕业后,相继执电机系(1958年提前工作至1970年)、自动化系(1970年迄今)教席。1983年晋升为副教授,1985年晋升为教授,1986年受聘为博士研究生导师,1991年被遴选为中国科学院学部委员(院士)。1992年至1994年任自动化系主任,1994年至2004年任信息科学技术学院院长,1999年至2004年任清华大学学术委员会主任,现为校学位委员会副主任。 相似文献
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联想记忆是神经网络的重要特性之一。本文提出了一种基于倒谱信息压缩,用离散Hopfield神经网络对地震事件的时间序列进行并行联想记忆,从而实现核爆地震模式识别的方法。理论分析和自动识别结果表明了该方法的有效性。 相似文献
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Nonexistence of cross-term free time-frequency distribution with concentration of Wigner-Ville distribution 总被引:2,自引:0,他引:2
Wigner-Ville distribution (WVD) is recognized as being a powerful tool and a nucleus in time-frequency representation (TFR) which gives an excellent time-frequency concentration, and more importantly, has many desirable properties. A major shortcoming of WVD is the inherent cross-term (CT) interference. Although solutions to this problem from the bulk of contributions to the literature concerning TFR are currently available, none has been able to completely eliminate the CT's in WVD. It is therefore a common belief that if there exists an auxiliary time-frequency distribution (TFD) which has the same auto-terms (AT's) as that in WVD, but has CT's with the opposite sign, then, by adding the auxiliary TFD to WVD, an ideal TFD, which preserves the concentration of WVD while annihilating the CT's, is readily obtained. However, we prove that the auxiliary TFD does not exist. Moreover, it is found that in general, CT free joint distributions with their concentrations close to that of WVD do not exist either. 相似文献
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地下核爆炸的自动识别研究 总被引:3,自引:3,他引:3
本文简要叙述了远区核爆探测的研究历史和现状,从研究的目的与任务,系统结构与研究思路,程序设计与实现,信号分析理论与模式分类器设计和模式识别结果分析等几方面介绍了“核爆地震模式识别系统”并对下一步研究工作提出了设想。 相似文献
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Subspaces of FM~mlet transform 总被引:3,自引:0,他引:3
The subspaces of FMmlet transform are investigated. It is shown that some of the existing transforms like the Fourier transform, short-time Fourier transform, Gabor transform, wavelet transform, chirplet transform, the mean of signal, and the FM-1let transform, and the butterfly subspace are all special cases of FMmlet transform. Therefore the FMmlet transform is more flexible for delineating both the linear and nonlinear time-varying structures of a signal. 相似文献