The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Zhihui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Accurate Position Estimation of SRM Based on Optimal Interval Selection and Linear Regression Analysis
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Shoujun Song, Lefei Ge, Zhihui Zhang
    Abstract:

    This paper proposes a novel accurate position estimation method for switched reluctance machine (SRM). The method requires only the flux-linkage characteristics at two particular rotor positions, which can be conveniently measured by the torque-balanced method. The interval which takes these two positions as endpoints is selected as the optimal interval due to the good Linearity between the flux linkage and position and the low sensitivity to the errors of the flux linkage. The positions in the optimal interval are obtained by the Linear flux-linkage model, and the positions that do not lie in this interval are estimated by the monadic Linear Regression Analysis (MLRA). Furthermore, the rotational speed is also estimated based on MLRA. The accuracy of the proposed method is verified by detailed simulation and experiment under different operating conditions such as angle position control and current chopping control.

Yong-ming Li - One of the best experts on this subject based on the ideXlab platform.

  • A general Linear-Regression Analysis applied to the 3-parameter Weibull distribution
    IEEE Transactions on Reliability, 1994
    Co-Authors: Yong-ming Li
    Abstract:

    The conventional techniques of Linear Regression Analysis (Linear least squares) applied to the 3-parameter Weibull distribution are extended (not modified), and new techniques are developed for the 3-parameter Weibull distribution. The three pragmatic estimation methods in this paper are simple, accurate, flexible, and powerful in dealing with difficult problems such as estimates of the 3 parameters becoming nonpositive. In addition, the inherent disadvantages of the 3-parameter Weibull distribution are revealed; the advantages of a new 3-parameter Weibull-like distribution over the original Weibull distribution are explored; and the potential of a 4-parameter Weibull-like distribution is briefly mentioned. This paper demonstrates how a general Linear Regression Analysis or Linear least-squares breaks away from the classical or modern nonLinear Regression Analysis or nonLinear least-squares. By adding a parameter to the simplest 2-parameter Linear Regression model (AB-model), two kinds of ABC models (elementary 3-parameter nonLinear Regression models) are found, and then a 4-parameter AABC model is built as an example of multi-parameter nonLinear Regression models. Although some other techniques are still necessary, additional applications of the ABC models are strongly implied.

Maria Blettner - One of the best experts on this subject based on the ideXlab platform.

  • Linear Regression Analysis part 14 of a series on evaluation of scientific publications
    Deutsches Arzteblatt International, 2010
    Co-Authors: A Schneider, Gerhard Hommel, Maria Blettner
    Abstract:

    SUMMARY Background: Regression Analysis is an important statistical method for the Analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. Methods: This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. Results: After a brief introduction of the uni- and multivariable Regression models, illustrative examples are given to explain what the important considerations are before a Regression Analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. Conclusion: The performance and interpretation of Linear Regression Analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying Linear Regression Analysis and its limitations are presented. ►Cite this as:

Shoujun Song - One of the best experts on this subject based on the ideXlab platform.

  • Accurate Position Estimation of SRM Based on Optimal Interval Selection and Linear Regression Analysis
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Shoujun Song, Lefei Ge, Zhihui Zhang
    Abstract:

    This paper proposes a novel accurate position estimation method for switched reluctance machine (SRM). The method requires only the flux-linkage characteristics at two particular rotor positions, which can be conveniently measured by the torque-balanced method. The interval which takes these two positions as endpoints is selected as the optimal interval due to the good Linearity between the flux linkage and position and the low sensitivity to the errors of the flux linkage. The positions in the optimal interval are obtained by the Linear flux-linkage model, and the positions that do not lie in this interval are estimated by the monadic Linear Regression Analysis (MLRA). Furthermore, the rotational speed is also estimated based on MLRA. The accuracy of the proposed method is verified by detailed simulation and experiment under different operating conditions such as angle position control and current chopping control.

Angus M Brown - One of the best experts on this subject based on the ideXlab platform.

  • a non Linear Regression Analysis program for describing electrophysiological data with multiple functions using microsoft excel
    Computer Methods and Programs in Biomedicine, 2006
    Co-Authors: Angus M Brown
    Abstract:

    The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with Linear functions, and we have previously demonstrated a method of non-Linear Regression Analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.