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Applications of Particle Swarm Optimization
Particle swarm optimization can be used in a variety of different applications. A few examples involving nonconvex, multi-objective, discontinuous search spaces and applications in neural networks and support vector machines are mentioned.
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In-depth details of Particle Swarm Optimization
I explain and show code to construct the Particle Swarm Optimization in Python. I conclude by optimizing on the Rastrigin function, a function that researchers use to test optimization algorithms on.
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An Overview of Particle Swarm Optimization
Particle swarm optimization is often used to optimize functions in rather unfriendly non-convex, non-continuous spaces. The idea behind the algorithm involves a swarm of particles flying through a space both collaboratively and independently.
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Rewiring the RSS icon link for Platform
Step-by-step instructions for rewiring where your RSS link icon points to within the Platform theme (by PageLines) for Wordpress (Updated 2012-06-21)
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Estimating Lyapunov Spectra of ODEs using Python
Python code is shown that estimates the Lyapunov spectra for the Rossler and Lorenz systems. This code is written in a way that makes it adaptable for other continuous-time systems.
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Modelling Chaotic Systems using Support Vector Regression and Python
Support Vector Regression is a technique in machine learning that can be used to model chaotic data. A program is shown to work on Delayed Henon Map data.
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Lyapunov spectra of inverted discrete dynamical systems
One can estimate the lyapunov spectrum of dynamical systems and their inverted counterparts using local Jacobian matrices and Wolf’s algorithm.
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Automatically Link Text using VB.Net
Automatically add HTML to link any words with http:// or https:// within text using this VB.net script
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Generating Poincaré Sections and Return Maps
Using the iterative 4-th order Runge-Kutta method as described here, we can create low dimensional slices of the system’s attractor known as Poincare Sections, Return maps, or Recurrence maps. We will use the Rossler attractor for this example, with a, b, and c set to 0.2, 0.2, and 5.7, respectively. Poincare sections are important for visualizing…
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Generating time series for Ordinary Differential Equations
We can produce a time series from ordinary differential equations by solving the equations using the iterative 4-th order Runge-Kutta method and plugging each of the solutions back into the equations.
