Classical elements in NetLogo: Fire

11 de Junio de 2016, ha tenido 217 vistas

Following with the simulation of Classical Elements in NetLogo, and after Earth and Water, we will address in this post how to simulate some fire features, but taking into account the same goals of decentralized and as simple as possible models.

Fire is formed by a set of incandescent particles or molecules of combustible material capable of emitting visible light. The flames are the parts of the fire that emit visible light, while smoke are physically the same but that no longer emit. Because the most common and graphical picture of fire is the flame, we will be interested in this post in simulate flame productions.

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Classical elements in NetLogo: Water

23 de Mayo de 2016, ha tenido 391 vistas

After Earth, and to continue with the simulation of Classical Elements in NetLogo, in this post we will give some simple, but very graphical and good looking, models to simulate the behaviour of water.

In this post we will simplify so much the assumptions that the model we will obtain only will be useful to simulate liquids under some conditions, but not gasses. You can find very realistic and nice simulation of different fluids behaviours under several and more general assumptions, but here we will give only a fast and simple way to obtain a behaviour that we visually recognize as a liquid.

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Classical elements in NetLogo: Earth

14 de Mayo de 2016, ha tenido 372 vistas

With this post we begin a series of posts that aim to simulate the creation and behavior of the 4 classic elements of nature in NetLogo: Earth, Water, Fire and Air.

In this first post we will see one of the most classical algorithms for the formation of realistic landscapes: the mid point displacement algorithm. This algorithm handles only generate a map of heights above a defined level, then apply a flooding process, color and shading to create more realistic results.

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Self Organizing Maps (SOM) in NetLogo

1 de Mayo de 2016, ha tenido 327 vistas

In this post we present how to implement Self Organizing Maps (Kohonen, 1992) in NetLogo. Specifically, we will implement two versions, a first introduction example to project 3D colors in a 2D plane, and a second one where we can input a file with N-dimensional data and it will make a projection to 2D space mantaining the topological structure of the original vectors.

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Artificial Neural Networks in NetLogo

30 de Abril de 2016, ha tenido 1047 vistas

As a way to continue with AI algorithms implemented in NetLogo, in this post we will see how we can make a simple model to investigate about Artificial Neural Networks (ANN). We will restrict ourselves to the more common and classical ones, the Multilayer Perceptron Network... my excuses for those of you that are waiting to see here something about Deep Neural Networks (DNN, as those used in AlphaGo from Google, or CaptionBot from Microsoft), maybe in a later post I will try to think about how to extract the main features of some convolutional neural network and test it on a very simple NetLogo model, but I am afraid that we will need too many computational resources to obtain anything of interest with this tool... Who knows? I will keep thinking.

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Elm: Introducción

28 de Octubre de 2014, 1215 vistas

Elm es un lenguaje para crear páginas web y aplicaciones web que hace uso de técnicas basadas en Programación Funcional Reactiva. Es relativamente nuevo, fuertemente basado en Haskell, y evoluciona rápidamente, por lo que puede haber detalles del mismo que pueden ser cambiados en versiones posteriores. La versión del lenguaje en el momento de escribir estas notas es la 0.16.

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Mapas Auto-Organizados

10 de Enero de 2014, 1573 vistas

Los Self Organizing Maps (Mapas Auto-Organizados), o SOM, fueron inventados en 1982 por Teuvo Kohonen, profesor de la Academia de Finlandia, y proporcionan una forma de representar datos multidimensionales (vectores) en espacios de dimensión inferior, normalmente, en 2D. Este proceso de reducir la dimensionalidad de vectores es una técnica de compresión de datos conocida como Cuantización Vectorial. Además, la técnica de Kohonen crea una red que almacena información de forma que las relaciones topológicas del conjunto de entrenamiento se mantienen.

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