| Java MLP Backprop Code
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Get a quick start to coding your first neural network with this code for a multilayer percepron trained with the backpropagation learning algorithm.
Submitted Jun 20, 2002
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| Java Neural Network Software Available
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This is to announce the availability of a Java implementation of the following algorithms and neural network models:
* Hard Competitive Learning (standard algorithm) * Neural Gas (Martinetz and Schulten 1991) ... The software can be accessed at http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html where it is embedded as Java applet into a Web page and is downloaded for immediate execution when you visit this page (if you have a slow link or only ftp access, please see below).
Submitted Dec 01, 1999
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| Neural Net Components in an Object Oriented Class Structure
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Neural Net Components in an Object Oriented Class Structure: The classes are all written in Java (JDK Version 1.0.2) and contain components of the Backpropagation Net and the Kohonen Feature Map neural network types.
Submitted Nov 21, 1999
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Total Visits: 228
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| Multi-Layer Perceptron
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This is a multi-layer perceptron applet.
Submitted Nov 07, 1999
Updated Nov 15, 2004
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Total Visits: 202
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| ANN Handwriting Recognizer
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This applet demonstrates an ANN based handwriting recognizer.
Submitted Nov 07, 1999
Updated Jul 26, 2004
by tyty tyty
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Total Visits: 194
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| Artificial Neuron
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This applet demonstrates the basic structure and behaviour of artificial neurons. These neurons, on their own, are very simple structures and are generally more useful when connected together as part of a larger network.
Submitted Nov 07, 1999
Updated Aug 10, 2005
by S
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Total Visits: 130
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| SOM: Self-Organizing Maps
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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.
Submitted Nov 07, 1999
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Total Visits: 111
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| Simulation of Cellular Neural Networks (CNNs)
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Here is a simulation of CNNs. Cellular Neural Networks (CNN) are a massive parallel computing paradigm defined in discrete N-dimensional spaces.
Submitted Dec 02, 1999
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Total Visits: 95
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| Perceptron Learning
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This applet demonstrates a simple form of supervised learning entitled the perceptron learning algorithm. Using this applet, you can train the perceptron to act as a binary logic unit. It can compute or approximate most 2-input Boolean functions.
Submitted Nov 07, 1999
Updated Nov 15, 2004
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Total Visits: 89
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| Neural Nets for Control
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Consider the problem of balancing a ball on an adjustable beam as shown in Figure 4. The ball starts with an initial position and initial velocity, and we require the ball to be brought to a rest at the center of the beam by dynamically adjusting the angle of the beam. 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...
The given applet demonstrates the Ball Balancing Problem.
Submitted Nov 07, 1999
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Total Visits: 82
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| Genetic Algorithm (GA) Demo (by Marshall C. Ramsey)
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The genetic algorithm performs a parallel, non-comprehensive search for the global maximum of the graph. The search is not precise meaning that there is no guarantee that the global maximum will be found. However, the result should be a good approximation of the maximum value.
Submitted Nov 07, 1999
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Total Visits: 76
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| Learning of Function Approximation
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The following applet can be used to experiment with backprop learning for function approximation problems.
Submitted Nov 07, 1999
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Total Visits: 71
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| Principal Component Extraction via Various Hebbian-Type Rules (CNNL)
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This applet implements three different learning rules for extracting the major principal component for an arbitrary zero-mean distribution. These rules are Normalized Hebbian rule, Oja's rule, and Yullie et al rule as discribed by Equations (3.3.5), (3.3.6), and (3.3.7) on page 92 in Mohamad Hassoun's textbook.
Submitted Nov 07, 1999
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Total Visits: 66
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| Temporal Difference Learning Project
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Java sources for TD learning applets of Tic Tac Toe and random walk.
Submitted Feb 10, 2003
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Total Visits: 58
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| Perceptron Learning Rule (CNNL)
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This program applies the perceptron learning rule to generate a separating surface for a two class problem (classes X and O). The X's are represented by a Red Box while the O's are represented by a Purple Box.
Submitted Nov 07, 1999
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Total Visits: 57
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| Clustering via Simple Competitive Learning (CNNL)
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This is a program that impliments the simple competitive learning process as described on page 103 in M. Hassoun, Fundementals of Artificial Neural Networks.
Submitted Nov 07, 1999
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Total Visits: 55
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| PCNN Demo
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Pulse Coupled Neural Networks (PCNNs), a Java applet demonstrating a PCNN.
Submitted Dec 02, 1999
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Total Visits: 50
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| 2 Dimensional Linear Dynamical Systems (CNNL)
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This page contains a java program that starts an applet which plots 2D Time Dynamical functions.
Submitted Nov 07, 1999
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Total Visits: 38
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