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estimators

  • Comparison of the performances of the LS and the MMSE channel estimators for a 64 sub carrier OFDM

    Comparison of the performances of the LS and the MMSE channel estimators for a 64 sub carrier OFDM system based on the parameter of Mean square error

    标签: the performances Comparison estimators

    上传时间: 2016-02-01

    上传用户:hgy9473

  • 数据挖掘estimators算法

    数据挖掘estimators算法,用JAVA实现的评价算法。

    标签: estimators 数据挖掘 算法

    上传时间: 2016-05-25

    上传用户:zhenyushaw

  • Comparison of the performances of the LS and the MMSE channel estimators

    Comparison of the performances of the LS and the MMSE channel estimators

    标签: the performances Comparison estimators

    上传时间: 2013-11-28

    上传用户:xc216

  • This paper examines the asymptotic (large sample) performance of a family of non-data aided feedfor

    This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Ricean-fading channels. The asymptotic performance of these estimators is established in closed-form expression and compared with the modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.

    标签: performance asymptotic examines non-data

    上传时间: 2015-12-30

    上传用户:225588

  • observable distribution grid are investigated. A distribution grid is observable if the state of th

    observable distribution grid are investigated. A distribution grid is observable if the state of the grid can be fully determined. For the simulations, the modified 34-bus IEEE test feeder is used. The measurements needed for the state estimation are generated by the ladder iterative technique. Two methods for the state estimation are analyzed: Weighted Least Squares and Extended Kalman Filter. Both estimators try to find the most probable state based on the available measurements. The result is that the Kalman filter mostly needs less iterations and calculation time. The disadvantage of the Kalman filter is that it needs some foreknowlegde about the state.

    标签: distribution observable grid investigated

    上传时间: 2014-12-07

    上传用户:ls530720646

  • 使用INTEL矢量统计类库的程序,包括以下功能:  Raw and central moments up to 4th order  Kurtosis and

    使用INTEL矢量统计类库的程序,包括以下功能:  Raw and central moments up to 4th order  Kurtosis and Skewness  Variation Coefficient  Quantiles and Order Statistics  Minimum and Maximum  Variance-Covariance/Correlation matrix  Pooled/Group Variance-Covariance/Correlation Matrix and Mean  Partial Variance-Covariance/Correlation matrix  Robust estimators for Variance-Covariance Matrix and Mean in presence of outliers

    标签: 61623 and Kurtosis central

    上传时间: 2017-05-14

    上传用户:yzy6007

  • Analog and Digital Control System Design

    This texts contemporary approach focuses on the concepts of linear control systems, rather than computational mechanics. Straightforward coverage includes an integrated treatment of both classical and modern control system methods. The text emphasizes design with discussions of problem formulation, design criteria, physical constraints, several design methods, and implementation of compensators.Discussions of topics not found in other texts--such as pole placement, model matching and robust tracking--add to the texts cutting-edge presentation. Students will appreciate the applications and discussions of practical aspects, including the leading problem in developing block diagrams, noise, disturbances, and plant perturbations. State feedback and state estimators are designed using state variable equations and transfer functions, offering a comparison of the two approaches. The incorporation of MATLAB throughout the text helps students to avoid time-consuming computation and concentrate on control system design and analysis

    标签: 控制系统

    上传时间: 2021-12-15

    上传用户:突破自我