发帖
雷达卡
by Smith H.
This book focuses on in-depth treatment of predictive analytic models.
The most important content is as follows:
Linear and Nonlinear Regression
Parametric Fitting
Selecting a Model Type Interactively
Selecting Model Type Programmatically
Use Library Models to Fit Data
Library Model Types
Polynomial Models
Selecting a Polynomial Fit Interactively
Defining Polynomial Terms for Polynomial Surface Fits
Exponential Models
Selecting an Exponential Fit Interactively
Selecting an Exponential Fit at the Command Line
Fourier Series
Selecting a Fourier Fit Interactively
Selecting a Fourier Fit at the Command Line
Gaussian Models
Selecting a Gaussian Fit Interactively
Selecting a Gaussian Fit at the Command Line
Power Series
Selecting a Power Fit Interactively
Selecting a Power Fit at the Command Line
Rational Polynomials
Selecting a Rational Fit Interactively
Selecting a Rational Fit at the Command Line
Rational Fit
Sum of Sines Models
Selecting a Weibull Fit Interactively
Selecting a Weibull Fit at the Command Line
Least-Squares Fitting
Linear Least Squares
Weighted Least Squares
Robust Least Squares
Nonlinear Least Squares
Custom Linear and Nonlinear Regression
Interpolation and Smoothing
Nonparametric Fitting
Selecting a Smoothing Spline Fit Interactively
Selecting a Smoothing Spline Fit at the Command Line
Lowess Smoothing
Selecting a Lowess Fit Interactively
Selecting a Lowess Fit at the Command Line
Filtering and Smoothing Data
About Data Smoothing and Filtering
Moving Average Filtering
Savitzky-Golay Filtering
Local Regression Smoothing
Fit Postprocessing
Exploring and Customizing Plots
Displaying Fit and Residual Plots
Viewing Surface Plots and Contour Plots
Removing Outliers
Selecting Validation Data
Generating Code and Exporting Fits to the
Workspace
Evaluating Goodness of Fit
Residual Analysis
Confidence Bounds on Coefficients
Prediction Bounds on Fits
General Global Models
Polynomials and Polynomial Splines
Truncated Power Series
Growth Models
Linear Models
Average Fit
Multiple Models
Transient Models
Covariance Modeling
Correlation Models
Transforms
Boundary Model Setup
Combining Best Boundary Models
Radial Basis Function
Hybrid RBF
Interpolating RBF
Multiple Linear Models
Free Knot Spline
Neural Network
Datum Models
Compare and Select Best Models
Plots and Statistics for Comparing Models
Determining the Best Fit
Validation
Trends
Using Information Criteria to Compare Models
Calculate and Compare MLE
Two-Stage Response Model
Stepwise Regression
Automatic Stepwise
Box-Cox Transformation
Two-Stage Models for Engines
Local Covariance Modeling
Response Features
Global Models
Two-Stage Models
Global Model Selection
Linear Regression
Use Statistical Models for Plant Modeling and
Optimization
Use Statistical Models for Hardware-in-the-Loop
Testing
Evaluate Response Models and PEV
Evaluate Confidence Intervals
Evaluate Boundary Models in the Workspace
Radial Basis Functions
Radial Basis Functions for Model Building
Hybrid Radial Basis Functions
本帖隐藏的内容
PREDICTIVE ANALYTICS with MATLAB.pdf
(5.54 MB, 需要: 15 个论坛币)
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