Neural Network Demo


ALVINN - Autonomous Vehicle Navigation using Neural Nets (CMU)
ALVINN uses neural networks to learn visual servoing. It watches a person drive for five minutes, and can then take over driving. ALVINN has been trained to drive on dirt paths, single-lane country roads, city streets, and multi-lane highways. Click
here for images of the vehicles and videos of ALVINN in action. The sucessor to ALVINN, called RALPH, was the core of a system that drove a vehicle autonomously all but 52 of the 2,849 miles from Pittsburgh to San Diego, averaging 63 miles per hour, day and night, rain or shine.

Common Lisp Hypermedia Server(MIT)
This server is created with Lisp. It has inductive learning ability and uses natural language processing techniques to answer questions.

The ICE Neural Nets Hot List has intro material on artificial neural networks, as well as some Java demonstrations

NeuroOn-Line Producta complete graphical, object-oriented software tool kit for building neural network applications and applying them to dynamic environments.

Whale Identification using a Decision Tree

WebWatcher (CMU)  

EightPuzzle Applet in A* (AI Engineering)

PCNN Net Demo (Alabama A & M) : A demo Pulse Coupled Neural Network Java applet.

SOM Search of the Web (University of Arizona) : An adaptive 2-D Kohonen-based Self-organizing Map has been developed for use with Alta Vista searching. This SOM allows user customization of the level of categorization the tool provides.

Fisheye and Fractal SOM (University of Arizona) : Generated in 1997 on a set of 200 electronic brainstorming comments, this version of the self-organizing map uses a 3-D representation to demonstrate relationships between concepts.

Artificial Neural Networks Overview (Battelle Memorial Institute)

Artificial Neural Networks: A Quick Introduction (Bradley University)

Motion Planner Applet using A* (University of California at Berkeley) : A demo of the A* algorithm

Backprop_XOR (Caltech - California Institute of Technology) : This Java simulation implements the backpropagation error learning algorithm to train a network with two synaptic layers with two inputs and a single output. The network can be trained to emulate the functions of XOR, AND, OR, etc.

Neural Network Demo (Cambridge University)

Dynamic Associative Neural Memory Simulator (David Clark) : The Dynamic Associative Neural Memory Simulator performs the following learning algorithms: Hopfield, Optimal Linear Associative Memory (OLAM), Semi-Adaptive OLAM, Adaptive OLAM, Perceptron, Adaline, and the learning algorithm presented in David Clark's thesis

Hopfield Network JAVA Demo (Dublin City University - DCU)

Self-Organizing Map Demo by Simon Lucas (University of Essex) : This is a simple demonstration of using self-organising feature maps to project a high-dimensional space onto a lower-dimensional space - though in this case the mapping is from a 3-d rgb colour to a 2-d grid.

The HTML Neural Net Consulter (hav.Software) : Several example Neural Net Consultation apps are provided including 2-way XOR and 3D Kohonen.

Binary Hopfield Applet (Matt Hill)

KnowMan on the Internet (Intellix A/S) : Demonstrations of our intractive knowledge mapping technology and experience how it works on the Internet.

Black Jack and Reinforcement Learning (École Polytechnique Fédérale de Lausanne - EPFL) : A Java applet uses a reinforcement learning algorithm to play a simplified version of the game of Black Jack. One or two players can play against the dealer (i.e., the casino).

IHearYourPitch 1.0 (Monowave Corp.) : Transcription of pitch in speech or music

Artificial Neural Network Lab on the Web (National Institute of Bioscience and Human-Technology ) : Demonstrations on artificial neural networks using java-applets and GIF-animations

Bayesian Self-Organizing Map Simulation (National Institute of Bioscience and Human-Technology ) : The Bayesian self-organizing map (BSOM) is a method for estimating a probability distribution generating data points on the basis of a Bayesian stochastic model. It is also regarded as a learning method for a kind of neural network.

Calculate Weld Pool Shape of Pulsed Laser Welding Process via Neural Network (Oak Ridge National Laboratory - ORNL)

Neural Network Ferrite Number Predictions for Stainless Steel Welds (Oak Ridge National Laboratory - ORNL)

Traveling Salesman Applet with Kohonen SOM (Patol)

XOR Applet with Backprop (Patol) : XOR applet is a little example that implements a 3-input XOR gate with a 3 layered neuronal network.

SOM Applet (Patol)

The Boltzmann Machine: Necker Cube Example (University of Queensland)

Neural Networks with Java by Jochen Fröhlich (Fachhochschule Regensburg ) : Examples of backprogation and Kohonen self-organizing feature maps with full documentation and description of the JAVA code.

DemoGNG (Ruhr Universität Bochum) : DemoGNG, a Java applet, implements several methods related to competitive learning. It is possible to experiment with the methods using various data distributions and observe the learning process. A common terminology is used to make it easy to compare one method to the other.

Real Estate Appraisal Demo (VirtualMind Pty Ltd) : The neural network behind this demo has had lots of experience, in fact it has been trained with the sales data of 1700 homes. From this data the neural network has found general patterns, not rules, that enable it to appraise the value of homes.

Soybean Disease Diagnosis (VirtualMind Pty Ltd) : This demonstration illustrates the type of service that could be offered to agriculturists. It is able to diagnose 15 different diseases common to Soybean crops.

Heart Disease Diagnosis (VirtualMind Pty Ltd) : Neural computing gained publicity in a Wall Street Journal when a neural network was able to diagnose heart attacks with better accuracy than physicians. This demonstration is of a heart disease diagnostic service.

Mushroom Edible/Poisonous (VirtualMind Pty Ltd) : There are thousands of different species of mushrooms some are edible and others poisonous, even deadly. The neural network behind this demo is able to identify mushrooms as being edible or poisonous.

Predicting Politics (VirtualMind Pty Ltd) : Here we have two demonstrations both based on the 1984 United States congressional voting records.

Iris Classifying (VirtualMind Pty Ltd) : This demonstration is able to classify three types of Iris from only the size of the sepal and petal.

Animated Perceptron Learning Rule (Wayne State University) : This program applies the pereceptrone learning rule to draw a separating surface between to classes of points X & O.

Backprop Tool for Function Approximation and Classification (Wayne State University) : This applet can be used to experiment with backprop learning for function approximation problems. You can choose an underlying function to be approximated, then choose a number of training samples, network size, and learning rate.

Neural Nets for Control: The Ball Balancing Problem (Wayne State University) : This problem is a classic regulator-type control problem and is precisely posed as: Given any initial condition , what is an appropriate control signal , which can produce the desired final state ? A neural net can be trained to learn such a control by observing the actions of a skilled human operator.

Image Compression using Backprop (Wayne State University) : Computer images are extremely data intensive and hence require large amounts of memory for storage. As a result, the transmission of an image from one machine to another can be very time consuming. By using data compression techniques, it is possible to remove some of the redundant information contained in images, requiring less storage space and less time to transmit. Neural nets can be used for the purpose of image compression.

Generalizations of the Hamming Associative Memory (Wayne State University)

2D Time Dynamical System Java Program (Wayne State University) : The program plots the state trajectory of this system from the starting point (you selected). You can change the value of delta T.

Localizing Algorithm with WebSim (Wright Laboratory) : This is an on-line demo of Radial Basis Function networks and 2-layer sigmoidal networks.

Robert Saunders : SOM Java applet (source code )   Learning Vector Quantisation   Simplified ART    Category ART   Genetic ART