Curve And Surface Fitting With Matlab Interpolation Smoothing And Spline Fitting
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Curve And Surface Fitting With Matlab Interpolation Smoothing And Spline Fitting
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Author : A Ramirez
language : en
Publisher:
Release Date : 2019-07-24
Curve And Surface Fitting With Matlab Interpolation Smoothing And Spline Fitting written by A Ramirez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with categories.
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.
Fitting Curves And Sourfaces Using Matlab
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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-17
Fitting Curves And Sourfaces Using Matlab written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with categories.
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"
Curve And Surface Fitting With Splines
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Author : Paul Dierckx
language : en
Publisher: Oxford University Press
Release Date : 1995
Curve And Surface Fitting With Splines written by Paul Dierckx and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
Describes the algorithms and mathematical fundamentals of a widely-used FORTRAN package for curve and surface fitting with splines.
Fitting Curves And Sourfaces Using Matlab Functions
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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-17
Fitting Curves And Sourfaces Using Matlab Functions written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with categories.
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 methods and using curve fitting functions.MATLAB Curve Fitting Functions lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis and provide optimized solver parameters and starting conditions to improve the quality of your fits. The functions 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 And Surface Fitting With Matlab Functions And Examples
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Author : A Ramirez
language : en
Publisher:
Release Date : 2019-07-24
Curve And Surface Fitting With Matlab Functions And Examples written by A Ramirez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with categories.
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.This book delves into the curve and surface fitting functions presented its complete syntax and completing them with examples.
Matlab For Civil Engineers
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Author : Dimitrios Sargiotis
language : en
Publisher: Springer Nature
Release Date : 2025-05-21
Matlab For Civil Engineers written by Dimitrios Sargiotis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-21 with Technology & Engineering categories.
This book is a comprehensive and rigorous guide to MATLAB for Civil Engineers, bridging the critical gap between theoretical mathematics and practical engineering solutions. With an approachable introduction for students and deep insights for experienced professionals, it caters to a wide range of audiences across civil engineering disciplines—environmental, structural, geotechnical, and transportation engineering. Structured to guide readers progressively, the book begins with foundational MATLAB operations such as syntax and matrix manipulation, then advances into sophisticated engineering applications, including optimization, numerical methods, and data visualization. It covers essential MATLAB functionalities, offering detailed instruction on computation, visualization, and programming, all within the context of solving real-world engineering challenges. What sets this book apart is its hands-on approach. Readers are immersed in practical learning through real-world case studies, examples, and step-by-step exercises designed to reinforce key concepts. The text provides both academic and professional readers with the tools they need to model, analyze, and optimize engineering systems using MATLAB, ensuring they are equipped to handle both routine and complex engineering challenges with confidence. By the end, readers will not only master MATLAB's powerful tools but will also understand how to apply them directly to critical civil engineering problems, positioning themselves to innovate and lead in a field where computational proficiency is increasingly essential.
Computational Methods With Matlab
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Author : Erik Cuevas
language : en
Publisher: Springer Nature
Release Date : 2023-08-24
Computational Methods With Matlab written by Erik Cuevas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-24 with Technology & Engineering categories.
This textbook provides readers a comprehensive introduction to numerical methods, using MATLAB®. The authors discuss the theory and application of the most often used numerical methods, using MATLAB as a computational tool. The book is designed to be accessible to readers of varying backgrounds, so the presentation focuses more on the description, implementation, and application of the methods and less on the mathematical details. This book not only covers the most important methods and techniques of scientific computation, but also contains a great amount of code and implementations, facilitating the process of learning and application.
Numerical Symbolic And Statistical Computing For Chemical Engineers Using Matlab
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Author : Ghosh, Pallab
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2018-09-01
Numerical Symbolic And Statistical Computing For Chemical Engineers Using Matlab written by Ghosh, Pallab and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Mathematics categories.
Numerical, analytical and statistical computations are routine affairs for chemical engineers. They usually prefer a single software to solve their computational problems, and at present, MATLAB has emerged as a powerful computational language, which is preferably used for this purpose, due to its built-in functions and toolboxes. Considering the needs and convenience of the students, the author has made an attempt to write this book, which explains the various concepts of MATLAB in a systematic way and makes its readers proficient in using MATLAB for computing. It mainly focuses on the applications of MATLAB, rather than its use in programming basic numerical algorithms. Commencing with the introduction to MATLAB, the text covers vector and matrix computations, solution of linear and non-linear equations, differentiation and integration, and solution of ordinary and partial differential equations. Next, analytical computations using the Symbolic Math Toolbox and statistical computations using the Statistics and Machine Learning Toolbox are explained. Finally, the book describes various curve fitting techniques using the Curve Fitting Toolbox. Inclusion of all these advanced-level topics in the book stands it out from the rest. KEY FEATURES Numerous worked-out examples to enable the readers understand the steps involved in solving the chemical engineering problems MATLAB codes to explain the computational techniques Several snapshots to help the readers understand the step-by-step procedures of using the toolboxes Chapter-end exercises, including short-answer questions and numerical problems Appendix comprising the definitions of some important and special matrices Supplemented with Solutions Manual containing complete detailed solutions to the unsolved analytical problems Accessibility of selected colour figures (including screenshots and results/outputs of the programs) cited in the text at www.phindia.com/Pallab_Ghosh. TARGET AUDIENCE • BE/B.Tech (Chemical Engineering) • ME/M.Tech (Chemical Engineering)
Curve Fitting With Matlab
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Author : J. Braselton
language : en
Publisher: CreateSpace
Release Date : 2014-09-10
Curve Fitting With Matlab written by J. Braselton and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-10 with Mathematics categories.
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
Trajectory Planning For Automatic Machines And Robots
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Author : Luigi Biagiotti
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-23
Trajectory Planning For Automatic Machines And Robots written by Luigi Biagiotti and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-23 with Technology & Engineering categories.
This book deals with the problems related to planning motion laws and t- jectories for the actuation system of automatic machines, in particular for those based on electric drives, and robots. The problem of planning suitable trajectories is relevant not only for the proper use of these machines, in order to avoid undesired e?ects such as vibrations or even damages on the mech- ical structure, but also in some phases of their design and in the choice and sizing of the actuators. This is particularly true now that the concept of “el- tronic cams” has replaced, in the design of automatic machines, the classical approach based on “mechanical cams”. The choice of a particular trajectory has direct and relevant implications on several aspects of the design and use of an automatic machine, like the dimensioning of the actuators and of the reduction gears, the vibrations and e?orts generated on the machine and on the load, the tracking errors during the motion execution. For these reasons, in order to understand and appreciate the peculiarities of the di?erent techniques available for trajectory planning, besides the ma- ematical aspects of their implementation also a detailed analysis in the time and frequency domains, a comparison of their main properties under di?erent points of view, and general considerations related to their practical use are reported.