Software Listing of Author : "Ivo Petras"

A new general FIR digital fractional order differentiator/integrator based on power series expansion of the backward difference (Euler) rule. For more details see books: [1] Ivo Petras, Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation, Springer, Series: Nonlinear Physical Science, 2011, ISBN 978-3-642-18100-9. http://www.springer.com/engineering/contro...8-3-642-18100-9 [2] Riccardo Caponetto, Giovanni Dongola, Luigi Fortuna and Ivo Petras, Fractional Order Systems: Modeling and Control Applications, World Scientific, Singapore, 2010, ISBN 978-9-814-30419-1. http://www.worldscibooks.com/chaos/7709.html

  • Platform: Matlab, Scripts

A new general IIR digital fractional order differentiator/integrator based on continued fraction expansion of weighted operator which is mixed scheme of the trapezoidal (Tustin) rule and the backward difference (Euler) rule. For more details see books: [1] Ivo Petras, Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation, Springer, Series: Nonlinear Physical Science, 2011, ISBN 978-3-642-18100-9. http://www.springer.com/engineering/contro...8-3-642-18100-9 [2] Riccardo Caponetto, Giovanni Dongola, Luigi Fortuna and Ivo Petras, Fractional Order Systems: Modeling and Control Applications, World Scientific, Singapore, 2010, ISBN 978-9-814-30419-1. http://www.worldscibooks.com/chaos/7709.html

  • Platform: Matlab, Scripts

A new general IIR type digital fractional-order differentiator and integrator based on power series expansion of the trapezoidal (Tustin) rule. For more details and help write: >> help dfod3 See also my previous digital fractional-order differentiators and integrators: http://www.mathworks.com/matlabcentral/fileexchange/3672 http://www.mathworks.com/matlabcentral/fileexchange/3673 or more information in the article: http://www.advancesindifferenceequations.c...t/2011/1/652789

  • Platform: Matlab, Scripts

Digital version of Fractional-Order PID Controller (DFOC) of the form: C(s) = K + Ti/s^m + Td*s^d for given sampling period Ts [sec]. For more details and help write: >> help DFOC

  • Platform: Matlab, Scripts

This toolbox contains the functions which can be used to simulate some of the well-known fractional order chaotic systems, such as: - Chen's system, - Arneodo's system, - Genesio-Tesi's system, - Lorenz's system, - Newton-Leipnik's system, - Rossler's system, - Lotka-Volterra system, - Duffing's system, - Van der Pol's oscillator, - Volta's system, - Lu's system, - Liu's system, - Chua's systems, - Financial system, - 3 cells CNN. The functions numerically compute a solution of the fractional nonlinear differential equations, which describe the chaotic system. Each function returns the state trajectory (attractor) for total simulation time. For more details see book: Ivo Petras, Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation, Springer, Series: Nonlinear Physical Science, 2011, ISBN 978-3-642-18100-9....

  • Platform: Matlab, Scripts

Orthogonal Linear Regression in 3D-space by using Principal Components Analysis This is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components Analysis" (http://www.mathworks.com/products/statisti...thoregdemo.html), which is Copyright by the MathWorks, Inc. Input parameters: - XData: input data block -- x: axis - YData: input data block -- y: axis - ZData: input data block -- z: axis - geometry: type of approximation ('line','plane') - visualization: figure ('on','off') -- default is 'on' - sod: show orthogonal distances ('on','off') -- default is 'on' Return parameters: - Err: error of approximation - sum of orthogonal distances - N: normal vector for plane, direction vector for line - P: point on plane or line in 3D space...

  • Platform: Matlab, Scripts

We present a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of: - linear regression model - nonlinear regression model - fitting data in 3D space - identification of dynamical system This toolbox requires another two functions, which are already published in Matlab Central File Exchange. Those functions will be installed to computer via supporting install package 'requireFEXpackage' included in TLS package. For more details see ReadMe.txt file. Authors: Ivo Petras, Dagmar Bednarova, Tomas Skovranek, and Igor Podlubny (Technical University of Kosice, Slovakia) Detailed description of the functions, examples and demos can be found at the link: Ivo Petras and Dagmar Bednarova: Total Least Squares...

  • Platform: Matlab, Scripts
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