Random Numbers in Matlab – Part III

This is the final post in our series on random numbers in Matlab. In the first post, we discussed basic random number functions, and in the second post, we discussed the control of random number generation in Matlab and alternatives for applications with stronger requirements. In this post, we will demonstrate how to create probability distributions with the basic rand and randn functions of Matlab. This is useful in many engineering applications, including reliability analysis and communications.

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Modeling with ODEs in Matlab – Part 5B

And so we reach the end. We will wrap up this series with a look at the fascinating Lorenz Attractor. Like the logistic map of the previous lesson, the Lorenz Attractor has the structure and behavior of a complex system. Unlike the logistic map, the Lorenz Attractor is defined by a system of first order autonomous ordinary differential equations. Thus, it is a perfect example to use for this last lesson where we examine the importance of error tolerance in evaluation chaotic systems of ODEs.
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Modeling with ODEs in Matlab – Part 5A

We are going to wrap up this tutorial series with a fun exploration of complex systems. Complex systems behave in unpredictable ways. This often makes it difficult to design and use models to examine their behavior. In this lesson we will look at some hallmarks of complex systems and examine a canonical example. Finally, in the next installment we will look at how differential equation models of complex systems can be difficult to examine using numerical solutions.
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Modeling with ODEs in Matlab – Part 4B

Welcome to Modeling with ODEs in Matlab – Part 4B! The previous post, Part 4A, introduced the idea of fitting ODE coefficients to empirical data. We saw that proper use of the nlinfit function combined with ode45 or ode15s allows us to fit a model to data when given a good initial estimate of the parameter values. Unfortunately, this approach does not work as well if the initial guess is not within the basin of attraction of the best fit. Today we will look at a new approach to function optimization: Genetic Algorithms (GAs). Genetic Algorithms are part of a search family I like to call “intelligent randomized search”, which also includes techniques such as Simulated Annealing and Particle Swarm Optimization.
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