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 Artificial Neural Network Training Model In Java 

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RubyFann Bindings to use FANN (Fast Artificial Neural Network) from within ruby/rails environment. Requires: Ruby 1.8.6 or greater. gnu make tools or equiv for native code in ext To install: sudo gem install ruby-fann



NNEGEN (Neural Network Experiment GENerator) is a framework for building advanced experiments with Artificial Neural Networks (ANN) programmed in Java. Most common of ANN (MLP, RBF, SOM) are supported.



This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO) technique (Kennedy, Eberhart et al. 2001).

This add-in acts...



NeuronDotNet is a neural network engine written in C#. It provides an interface for advanced AI programmers to design various types of artificial neural networks and use them.



VitoshaTrade is Distributed Artificial Neural Network trained by Differential Evolution for prediction of Forex. Project development is in Sofia, Bulgaria. Vitosha is a mountain massif, on the outskirts of Sofia, the capital of Bulgaria.



This project is goaled a r-language wrap of Artificial Neural Network library libfann.

also known as R-binding libfann



The Subneuron is a project for a set of c# classes for develop and simulate a Artificial Neural Network (ANN).



this model show the design of sun seeker control system using neural network model refrence with neural network toolbox and SIMULINK with MATLAB



Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink.
Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink.

Marcelo Augusto Costa Fernandes
DCA - CT - UFRN



We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on...



Neuroph is friendly Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and nice GUI make it easy to learn and use.



A very simple program that trains a neural network with 9 images(3 rectangles, 3 triangles and 3 circles)and then simulates the neural network in way to recognize 3 others images(1 rectangles, 1 triangles and 1 circles).



This package includes files for modelling nonlinear dynamic systems using a recurrent generalized neural network. The learning scheme uses the complex method of nonlinear nonderivative optimization, thereby avoiding the problems of computing the...



This little package contains a Parzen Neural Network classifier that can classify data between N classes in D dimensions. The classifier is really fast and simple to learn. The good classification performance can be obtained for a certain class of...



This Program allows a Neural Network in conjuction with Image Processing to compute the best picture quality. This is done by splitting the Image into Its RGB components.



LiNNS is not just a Neural Network Simulator, but a Neural Network System - a framework which covers the full lifecycle of a neural network, from design and research till usage in an external application.



Given a neural network object, this function returns the closed, symbolic, expression implemented by the network (as a string).

This allows you to use a neural network model without relying on the neural network toolbox.

Note...



The form of a single layer feed forward neural network lends itself to finding the gradient. This is useful when the network is used for surrogate optimization or other algorithms that use gradients. Requires creating a file by modifying a NN...



A Ruby extension that provides a 2-Layer Back Propagation Neural Network, which can be used to categorize datasets of arbitrary size. The network can be easily (re-)stored to/from the hard disk.



Generic Neural Network. It's a class to build almost any type of Neural Networks, from a simple Perceptron to a SOM, supporting recurrence.