ifs.m

Plots the attractor of a generic 2-dimensional (linear) IFS by chaos game.

[X,h] = ifs(A, p, N, opt1)

Some examples of the use of ifs.m are here: ifs.zip

bcsw.m

A raw and simple program to numerically simulate the model developed by:

P. Bak, K. Chen, J. Scheinkman and M. Woodford (1993),

in a Model of Production and Inventory Dynamics

Click here to download a screenshot of the program.

biavati_sandri_zarri.m

The simulation software of the model presented in

Biavati M., Sandri M., Zarri L. (2002),

in Sacco P.L. e Zamagni S. (a cura di),

Complessità relazionale e comportamento economico.

Verso un nuovo paradigma di razionalità, ll Mulino, Bologna

andrews.m

Plots high-dimensional data using the method proposed by Andrews (1972).

Y = ANDREWS(X,N) plots each row of the matrix X as a trigonometric function defined in the interval (-pi,pi).

N is the number of points taken into account in the interval(-pi,pi). The default value for N is 100.

Y is the matrix containing the values f(t) of the trigonometric functions.

Reference: D.F. Andrews (1972), "

An example of the use of andrews.m is: testandr.m

lce.m for Mathematica < 5.2

lce.m for Mathematica > 7

The Lyapunov characteristic exponents play a crucial role in the description of the behavior

of dynamical systems. They measure the average rate of divergence or convergence of orbits

starting from nearby initial points. Therefore, they can be used to analyze the stability of limit

sets and to check sensitive dependence on initial conditions, that is, the presence of chaotic

attractors.

This package shows how to use Mathematica to compute the Lyapunov spectrum of a smooth

dynamical system.

(Alternatively, you can download lce.m from the

Nonlinear Dynamics and Topological Time Series Analysis Archive by Nicholas B. Tufillaro)

melissa.m

The idea behind SSA was originally purposed as a data adaptive method for choosing an optimal

embedding dimension for attractor reconstruction. Later the technique was developed as a "stand

alone" time series analysis technique.

During the last decade it has been very successful and has become a standard tool in many different

scientific fields, such as climatic, meteorological, geophysical, and astronomical time series analysis.

Download the tutorial for MELISSA (in italian)

Updated: 8 May 2012

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