OpenNN - Open Neural Networks Library
lucashnegri / NeuralView
Bitbucket is a code hosting site with unlimited public and private repositories. We're also free for small teams! Sign up for freeClose NeuralView is a graphical interface for FANN 1, making possible to graphically design, train, and test artificial neural networks.
Fast Artificial Neural Network Library
PyBrain
Java Neural Network Framework Neuroph
Réseau de neurones artificiels
Un article de Wikipédia, l'encyclopédie libre. Un réseau de neurones artificiels est un modèle de calcul dont la conception est très schématiquement inspirée du fonctionnement des neurones biologiques. Les réseaux de neurones sont généralement optimisés par des méthodes d’apprentissage de type probabiliste, en particulier bayésien. Ils sont placés d’une part dans la famille des applications statistiques, qu’ils enrichissent avec un ensemble de paradigmes [1] permettant de créer des classifications rapides (réseaux de Kohonen en particulier), et d’autre part dans la famille des méthodes de l’intelligence artificielle auxquelles ils fournissent un mécanisme perceptif indépendant des idées propres de l'implémenteur, et fournissant des informations d'entrée au raisonnement logique formel. En modélisation des circuits biologiques, ils permettent de tester quelques hypothèses fonctionnelles issues de la neurophysiologie, ou encore les conséquences de ces hypothèses pour les comparer au réel.
AForge.NET :: Computer Vision, Artificial Intelligence, Robotics
NeuronDotNet - Neural Networks in C# | Free Science & Engineering software downloads
NeuroBox Neural Network Library
Project Overview NeuroBox is a .NET OOP Library written in C# to generate, propagate and train complex neuronal networks with technologies like backpropagation using weight decay, momentum term, manhattan training, flatspot elimination etc. What's coming, current development Weblog about current thoughts, developments and excperiences around the NeuroBox project. NeuroBox Designer A graphical user interface for composing and manipulating neural networks in a convenient way. QuickProp, RProp Improved learning algorithms will speed up the network training compared with backpropagation in many scenarios. Related People and Projects, Derived Works, Contributions Many Thanks for the contributions from the community (see 'Demos' and 'Links'), in particular to Francois Vanderseypen, Tobias Finazzi, Leopold Rehberger and Matt Jallo. Questions, Support Please use the contact form to contact me. :.
Financial Predictor via Neural Network
Contents Introduction Each year, the field of computer science becomes more sophisticated as new types of technologies hit the market. Despite that, the problem of developing intelligent agents that will precisely simulate human brain activity is still unsolved. During my intellectual trip into the world of artificial intelligence, I was fascinated how "magically" a correctly constructed artificial neural network (specifically feed-forward network) can predict values, according to those specified at the input. Function interpolation and approximation Prediction of trends in numerical data Prediction of movements in financial markets All the examples are actually very similar, because in mathematical terms, you are trying to define a prediction function F(X1, X2, ..., Xn), which according to the input data (vector [X1, X2, ..., Xn]), is going to "guess" (interpolate) the output Y. Background Dow Jones Industrial Average NASDAQ Composite Prime Interest Rate Artificial Neural Network Input Output
