Search Results for "structural analysis software" - Page 2
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Showing 36 open source projects for "structural analysis software"
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Java Kohonen Neural Network Library Kohonen neural network library is a set of classes and functions for design, train and use Kohonen network (self organizing map).
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A graphical data manipulation and processing system including data import, numerical analysis and visualisation. The software is written in Java and built upon the Netbeans platform to provide a modular desktop data manipulation application.
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This project provides a JAVA library and a graphical user interface which implement the "FastICA" algorithm based on the paper "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis (ICA)" by Aapo Hyvärinen.
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Digital graph 1.3 is an advanced JAVA SWING component for modelling, displaying, saving and loading graphs(using XML and XMLSchema). It is fully documented and requires jdk 1.5.x.Version 1.3 adds 9 bug fixs, 3 rengineering and 4 new features.
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Java Randomness Test Suite is a gui application to run randomness tests on random stream resource (file or algorithm). Application can be run in command line. Includes general statistical tests, NIST and DIEHARD tests.
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TM4 is a suite of applications for managing and analyzing microarray data. TM4 provides data storage and tracking, image analysis, normalization, data filtering, clustering and statistical analysis capabilities. Includes MADAM, Spotfinder, MIDAS, and MeV.
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Library made with the quantity/formatter pattern. (Currency/CurrencyFormat, Density/DensityFormat, Dimension/DimensionFormat, Distance/DistanceFormat, ElapsedTime/ElapsedTimeFormat, Temperature/TemperatureFormat, Volume/VolumeFormat, Weight/WeightFormat)
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A java based neural network framework. The Auratus network is built around an XML messaging system, allowing for a complete MVC design. Additionally, Auratus networks are constructed and at the node/edge level, allowing for advanced topologies.
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RunAsApproximator es un programa java multilenguaje que implementa algoritmos numéricos. Los datos de entrada son aquellos requeridos por cada algoritmo y los de salida son resultado final y los datos intermedios necesarios para alcanzar su resolución.
