Search Results for "backpropagation network"

Neuroph is lightweight 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 GUI editor makes it easy to learn and use.

eANN is an implementation of several kind of neural networks written with the intention of providing a (hopefully) easy to use, and easy to modify, OOP source code. It is possible to have several different sized networks running simultaneously, each functioning independently of the others or acting as inputs between them. It also easy to modify the structure so that neurons (or even whole layers) can be created/pruned during simulation allowing dynamic expansion/contraction of the network.

Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin.

Syntactic is a simple C++ library for constructing general Neural networks. For now the library supports creation of multi layered networks for the Feedforward Backpropagation algorithm as well as Time Series Networks. More will be implemented soon.

3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar"

PlexBench is a cross-platform, web-enabled, analysis tool that is driven by a scalable backpropagation feed-forward neural network. It uses embedded Perl for scripting and is written in the style of an in-process Component Object Model (COM) C++ program.

Breeze a multiple class model neural network development system. Network models include multi-threaded Backpropagation and Radial Basis Function Networks, for time series prediction.

OpenDiscreteDynamicProgrammingTemplate : founds optimal constrainted parameters of a discrete controls with second order optimization template replacing Hessian with directional derivatives and backpropagation for digital filter(as neural network)

Searches for adecuate design for feedforward backpropagation neural network, employing genetic algorithm as refining engine. The result topolgy may not be orthodox.

Lightweight backpropagation neural network in C++. The project provides a class implementing a feedforward neural network, and a class for easily train it. It is highly customizable to manage your problem and comes with a simple graphical interface.

Lightweight backpropagation neural network in C. Intended for programs that need a simple neural network and do not want needlessly complex neural network libraries. Includes example application that trains a network to recognize handwritten digits.

Multilayered feed-forward neural network software written in C++. Backpropagation and RPROP are available as training algorithms. Design goals: speed of execution when calculating the output to new data, and quality of training (preprocessing: PCA).