Artificial Intelligence: Neural Networks: Software

A set of MATLAB tools for neural network based identification of nonlinear dynamic systems.

A library of MATLAB functions for simulating neural network algorithms based on the book Neural Networks for Pattern Recognition by Chris Bishop.

An environment for neural network research, design, and simulation within MATLAB.

Neural network simulator based on C and Tcl. Made up of C libraries to build networks, and pre-built simulators.

A standard environment for evaluating the performance of learning methods. Includes a number of datasets and an archive of learning methods.

A simulation environment for research on applications of neural networks. Written in C and has an X based GUI.

A neural-network simulation system based on C++. This is the next generation of the PDP software originally released with McClelland and Rumelhart's PDP book.

Showcases a java applet designed to recognize hand printed digits using a neural network. Digits can be drawn using the mouse and recognized by the applet in the browser.

Neural network for classification and regression problems. Supports ODBC and Excel.

A library of state-of-the-art machine learning algorithms. Licensed under the GPL, and designed for Unix and Linux environments.

A neural network based system to recognize leaves written in Java. A Java-Applet is also available.

Professional II/PLUS is a comprehensive neural network development environment. Available for Windows and Unix. Predict is a neural network tool for solving prediction and classification problems. Available for Unix or as an Excel add-in for Windows.

Neural network freeware for building prediction and classification models in Excel. Uses backpropagation. Can handle missing values and categorical data.

A back propagation neural network application.

Java backpropagation neural network simulator. Supports wizard-based GUI and performance monitoring charts.

Open-source software for simulating spiking neural networks, written in C++.