Download or read book Spline Toolbox for Use with MATLAB written by Carl De Boor and published by . This book was released on 2005 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Spline Toolbox written by Carl De Boor and published by . This book was released on 2000 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Spline Toolbox written by Carl de Boor and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book CURVE and SURFACE FITTING with MATLAB INTERPOLATION SMOOTHING and SPLINE FITTING written by A Ramirez and published by . This book was released on 2019-07-24 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Curve Fitting Toolbox software supports these nonparametric fitting methods: -"Interpolation Methods" - Estimate values that lie between known data points.-"Smoothing Splines" - Create a smooth curve through the data. You adjust the level of smoothness by varying a parameter that changes the curve from a least-squares straight-line approximation to a cubic spline interpolant.-"Lowess Smoothing" - Create a smooth surface through the data using locally weighted linear regression to smooth data.Interpolation is a process for estimating values that lie between known data points. There are several interpolation methods: - Linear: Linear interpolation. This method fit a different linear polynomial between each pair of data points for curves, or between sets of three points for surfaces.- Nearest neighbor: Nearest neighbor interpolation. This method sets the value of an interpolated point to the value of the nearest data point. Therefore, this method does not generate any new data points.- Cubic spline: Cubic spline interpolation. This method fit a different cubic polynomial between each pair of data points for curves, or between sets of three points for surfaces.After fitting data with one or more models, you should evaluate the goodness of fit A visual examination of the fitte curve displayed in Curve Fitting app should be your firs step. Beyond that, the toolbox provides these methods to assess goodness of fi for both linear and nonlinear parametric fits-"Goodness-of-Fit Statistics" -"Residual Analysis" -"Confidence and Prediction Bounds" The Curve Fitting Toolbox spline functions are a collection of tools for creating, viewing, and analyzing spline approximations of data. Splines are smooth piecewise polynomials that can be used to represent functions over large intervals, where it would be impractical to use a single approximating polynomial. The spline functionality includes a graphical user interface (GUI) that provides easy access to functions for creating, visualizing, and manipulating splines. The toolbox also contains functions that enable you to evaluate, plot, combine, differentiate and integrate splines. Because all toolbox functions are implemented in the open MATLAB language, you can inspect the algorithms, modify the source code, and create your own custom functions. Key spline features: -GUIs that let you create, view, and manipulate splines and manage and compare spline approximations-Functions for advanced spline operations, including differentiation integration, break/knot manipulation, and optimal knot placement-Support for piecewise polynomial form (ppform) and basis form (B-form) splines-Support for tensor-product splines and rational splines (including NURBS)- Shape-preserving: Piecewise cubic Hermite interpolation (PCHIP). This method preserves monotonicity and the shape of the data. For curves only.- Biharmonic (v4): MATLAB 4 grid data method. For surfaces only.- Thin-plate spline: Thin-plate spline interpolation. This method fit smooth surfaces that also extrapolate well. For surfaces only.If your data is noisy, you might want to fit it using a smoothing spline. Alternatively, you can use one of the smoothing methods. The smoothing spline s is constructed for the specified smoothing parameter p and the specified weights wi.
Download or read book Spline Fitting with MATLAB written by J. Braselton and published by Createspace Independent Publishing Platform. This book was released on 2016-06-22 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: You can work with splines in Curve Fitting Toolbox(tm) in several ways.Using the Curve Fitting app or the fit function you can:Fit cubic spline interpolants to curves or surfacesFit smoothing splines and shape-preserving cubic spline interpolants to curves (but not surfaces)Fit thin-plate splines to surfaces (but not curves)The toolbox also contains specific splines functions to allow greater control over what you can create. For example, you can use the csapi function for cubic spline interpolation. Why would you use csapi instead of the fit function 'cubicinterp' option? You might require greater flexibility to work with splines for the following reasons:You want to combine the results with other splines, You want vector-valued splines. You can use csapi with scalars, vectors, matrices, and ND-arrays. The fit function only allows scalar-valued splines.You want other types of splines such as ppform, B-form, tensor-product, rational, and stform thin-plate splines.You want to create splines without data.You want to specify breaks, optimize knot placement, and use specialized functions for spline manipulation such as differentiation and integration.If you require specialized spline functions, see the following sections for interactive and programmatic spline fitting.
Download or read book Spline Fitting With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-10 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Interactive Spline Fitting Programmatic Spline Fitting Curve Fitting Toolbox Splines MATLAB Splines Expected Background Vector Data Type Support Spline Function Naming Conventions Arguments for Curve Fitting Toolbox Spline Functions Cubic Spline Interpolation Cubic Spline Interpolant of Smooth Data Periodic Data Other End Conditions General Spline Interpolation Knot Choices Smoothing Least Squares Vector-Valued Functions Fitting Values at N-D Grid with Tensor-Product Splines Fitting Values at Scattered 2-D Sites with Thin-Plate Smoothing Splines Postprocessing Splines B-Splines and Smoothing Splines Multivariate and Rational SplinesLeast-Squares Approximation by Natural Cubic Splines Solving A Nonlinear ODE Construction of the Chebyshev Spline Approximation by Tensor Product Splines
Download or read book Spline Toolbox 3 written by Carl De_Boor and published by . This book was released on 2007 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Curve Fitting Toolbox written by and published by . This book was released on 2002 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Curve and Surface Fitting with Splines written by Paul Dierckx and published by Oxford University Press. This book was released on 1995 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fitting of a curve or surface through a set of observational data is a very frequent problem in different disciplines (mathematics, engineering, medicine, ...) with many interesting applications. This book describes the algorithms and mathematical fundamentals of a widely used software package for data fitting with (tensor product) splines. As such it gives a survey of possibilities and benefits but also of the problems to cope with when approximating with this popular type of function. In particular it is demonstrated in detail how the properties of B-splines can be fully exploited for improving the computational efficiency and for incorporating different boundary or shape preserving constraints. Special attention is also paid to strategies for an automatic and adaptive knot selection with intent to obtain serious data reductions. The practical use of the smoothing software is illustrated with many examples, academic as well as taken from real life.
Download or read book Modeling of Curves and Surfaces with MATLAB written by Vladimir Rovenski and published by Springer Science & Business Media. This book was released on 2010-07-03 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text on geometry is devoted to various central geometrical topics including: graphs of functions, transformations, (non-)Euclidean geometries, curves and surfaces as well as their applications in a variety of disciplines. This book presents elementary methods for analytical modeling and demonstrates the potential for symbolic computational tools to support the development of analytical solutions. The author systematically examines several powerful tools of MATLAB® including 2D and 3D animation of geometric images with shadows and colors and transformations using matrices. With over 150 stimulating exercises and problems, this text integrates traditional differential and non-Euclidean geometries with more current computer systems in a practical and user-friendly format. This text is an excellent classroom resource or self-study reference for undergraduate students in a variety of disciplines.
Download or read book Fitting Curves and Sourfaces Using Matlab written by Perez C. and published by . This book was released on 2017-08-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Curve Fitting Toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting,interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methodsThe more important features of this toolbox ar de next:* Curve Fitting app for curve and surface fitting* Linear and nonlinear regression with custom equations* Library of regression models with optimized starting points and solver parameters* Interpolation methods, including B-splines, thin plate splines, and tensor-productsplines* Smoothing techniques, including smoothing splines, localized regression, Savitzky-Golay filters, and moving averages* Preprocessing routines, including outlier removal and sectioning, scaling, and weighting data* Post-processing routines, including interpolation, extrapolation, confidence intervals, integrals and derivatives This book develops the following topics:* "Interpolation and Smoothing" * "Nonparametric Fitting" * "Interpolation Methods" * "Smoothing Splines" * "Lowess Smoothing" * "Filtering and Smoothing Data"* "Fit Postprocessing" * "Explore and Customize Plots" * "Remove Outliers" * "Select Validation Data" * "Evaluate a Curve Fit" * "Evaluate a Surface Fit"* "Compare Fits Programmatically" * "Evaluating Goodness of Fit"* "Residual Analysis" * "Confidence and Prediction Bounds"* "Differentiating and Integrating a Fit" * "Spline Fitting" * "Curve Fitting Toolbox Splines and MATLAB Splines" * "Cubic Spline Interpolation" * "Fitting Values at N-D Grid with Tensor-Product Splines" * "Postprocessing Splines"* "Types of Splines: ppform and B-form" * "B-Splines and Smoothing Splines"* "Multivariate and Rational Splines" * "Multivariate Tensor Product Splines"* "NURBS and Other Rational Splines" * "Least-Squares Approximation by Natural Cubic Splines" * "Solving A Nonlinear ODE" * "Construction of the Chebyshev Spline" * "Approximation by Tensor Product Splines"
Download or read book Econometrics With Matlab written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-08 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important content is the following: -Nonparametric Fitting -Interpolation Methods -Selecting an Interpolant Fit -Smoothing Splines -Lowess Smoothing -Fit Smooth Surfaces To Investigate Fuel Efficiency -Filtering and Smoothing Data -Introducing Spline Fitting -Curve Fitting Toolbox Splines and MATLAB Splines -Cubic Spline Interpolation -Vector-Valued Functions -Fitting Values at N-D Grid with Tensor-Product -Splines -Fitting Values at Scattered 2-D Sites with Thin-Plate -Smoothing Splines -Postprocessing Splines -Types of Splines: ppform and B-form -B-Splines and Smoothing Splines -Multivariate and Rational Splines -The ppform -Constructing and Working with ppform Splines -The B-form -B-form and B-Splines -B-Spline Knot Multiplicity -Choice of Knots for B-form -Constructing and Working with B-form Splines -Multivariate Tensor Product Splines -B-form of Tensor Product Splines -Construction With Gridded Data -ppform of Tensor Product Splines -NURBS and Other Rational Splines -Constructing and Working with Rational Splines -Constructing and Working with stform Splines -Least-Squares Approximation by Natural Cubic Splines -Solving A Nonlinear ODE -Construction of the Chebyshev Spline -Approximation by Tensor Product Splines
Download or read book Curve Fitting Toolbox written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Curve Fitting with MATLAB Linear and Non Linear Regression Interpolation written by Braselton J. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-21 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curve Fitting Toolbox(tm) provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.
Download or read book Curve Fitting With Matlab written by J. Braselton and published by CreateSpace. This book was released on 2014-09-10 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Linear and Nonlinear Regression Parametric Fitting Parametric Fitting with Library Models Selecting a Model Type Interactively Selecting Model Type Programmatically Using Normalize or Center and Scale Specifying Fit Options and Optimized Starting Points List of Library Models for Curve and Surface Fitting Use Library Models to Fit Data Library Model Types Model Names and Equations Polynomial Models About Polynomial Models Selecting a Polynomial Fit Interactively Selecting a Polynomial Fit at the Command Line Defining Polynomial Terms for Polynomial Surface Fits Exponential Models About Exponential Models Selecting an Exponential Fit Interactively Selecting an Exponential Fit at the Command Line Fourier Series About Fourier Series Models Selecting a Fourier Fit Interactively Selecting a Fourier Fit at the Command Line Gaussian Models About Gaussian Models Selecting a Gaussian Fit Interactively Selecting a Gaussian Fit at the Command Line Power Series About Power Series Models Selecting a Power Fit Interactively Selecting a Power Fit at the Command Line Rational Polynomials About Rational Models Selecting a Rational Fit Interactively Selecting a Rational Fit at the Command Line Sum of Sines Models About Sum of Sines Models Selecting a Sum of Sine Fit Interactively Selecting a Sum of Sine Fit at the Command Line Weibull Distributions About Weibull Distribution Models Selecting a Weibull Fit Interactively Selecting a Weibull Fit at the Command Line Least-Squares Fitting Introduction Error Distributions Linear Least Squares Weighted Least Squares Robust Least Squares Nonlinear Least Squares Custom Linear and Nonlinear Regression Interpolation and Smoothing Nonparametric Fitting Interpolants Interpolation Methods Selecting an Interpolant Fit Interactively Selecting an Interpolant Fit at the Command Line Smoothing Splines About Smoothing Splines Selecting a Smoothing Spline Fit Interactively Selecting a Smoothing Spline Fit at the Command Line Lowess Smoothing About Lowess Smoothing Selecting a Lowess Fit Interactively Selecting a Lowess Fit at the Command Line Fitting Automotive Fuel Efficiency Surfaces 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 Using Zoom, Pan, Data Cursor, and Outlier Exclusion Customizing the Fit Display Print to MATLAB Figures Removing Outliers Selecting Validation Data Generating Code and Exporting Fits to the Workspace Generating Code from the Curve Fitting Tool Exporting a Fit to the Workspace Evaluating Goodness of Fit How to Evaluate Goodness of Fit Goodness-of-Fit Statistics Residual Analysis Plotting and Analysing Residuals Confidence and Prediction Bounds About Confidence and Prediction Bounds Confidence Bounds on Coefficients Prediction Bounds on Fits Differentiating and Integrating a Fit Surface Fitting Objects and Methods
Download or read book Scientific Computing with MATLAB written by Dingyu Xue and published by CRC Press. This book was released on 2018-09-03 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Computing with MATLAB®, Second Edition improves students’ ability to tackle mathematical problems. It helps students understand the mathematical background and find reliable and accurate solutions to mathematical problems with the use of MATLAB, avoiding the tedious and complex technical details of mathematics. This edition retains the structure of its predecessor while expanding and updating the content of each chapter. The book bridges the gap between problems and solutions through well-grouped topics and clear MATLAB example scripts and reproducible MATLAB-generated plots. Students can effortlessly experiment with the scripts for a deep, hands-on exploration. Each chapter also includes a set of problems to strengthen understanding of the material.
Download or read book Summer Schools MATLAB 94 95 written by Ivana Horová and published by . This book was released on 1997 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
