Software Downloads for "Growing Neural"

DynNet is built as a Java library that contains basic elements that are necessary in order to build neural networks.

Neural network classical models are already available (Multi-layer perceptron, Kohonen self-organizing maps, neural gas, growing neural gas, etc.).

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SNARLI (Simple Neural ARchitecture LIbrary) is a Java package containing a back-prop layer class and classes for the Self-Organizing Map and Incremental Growing Grid. The back-prop class supports sigma-pi connections and back-prop-through-time..

Neural network classification results live view (like a movie).
Free software for playing with neural networks classification.

Major features
* Easy, ready to play with.
* Live view.
* Many network architectures.
* Different shapes of training data sets.
* Learning with backpropagation algorithm.
* Optional momentum.

For better understanding of neural networks. SharkTime Software - Sharky Neural Network - Classification neural network in action..

Neural network classification results live view (like a movie). Free software for playing with neural networks classification. Major features * Easy, ready to play with. * Live view. * Many network architectures. * Different shapes of training data sets. * Learning with backpropagation algorithm. * Optional momentum. For better understanding of neural networks..

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 toolbox file..

  • Platform: Matlab, Scripts
  • Publisher: Alan Jennings
  • Date: 14-01-2013
  • Size: 41 KB

Simple tutorial on pattern recognition using back propagation neural networks. the program has 3 classes with 3 images per class..

  • Platform: Matlab, Scripts
  • Publisher: Alaa Eleyan
  • Date: 27-01-2013
  • Size: 10 KB

SFAM is an incremental neural network classifier. It is a simple and fast version of Fuzzy ARTMAP (FAM). Both FAM and SFAM produce the same output given the same input. References: [1] Kasuba, T. (1993). "Simplified fuzzy ARTMAP." AI Expert, Nov., 18 25. [2] Carpenter, G. A., Grossberg, S., Markuzon, N. and Rosen, D. B., 'Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps', IEEE Trans. Neural Networks, 3(5):698-713, 1992. Disclaimer: Author is not responsible for currently unknown bugs.

  • Platform: Matlab, Scripts
  • Publisher: Emre Akbas
  • Date: 05-03-2013
  • Size: 20 KB

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 derivative of an infinite impulse response filter such as a recurrent neural network. 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 derivative of an infinite impulse response filter such as a recurrent neural network.

  • Platform: Matlab, Scripts
  • Publisher: Travis Wiens
  • Date: 18-01-2013
  • Size: 256 KB

Similar to using the extended Kalman filter, Neural Networks can also be trained through parameter estimation using the unscented Kalman filter. This file provides a function for this purpose. It also includes an example to show how to use this function. It requires the unscented Kalman filter, ukf function, which can be downloaded from: http://www.mathworks.com/matlabcentral/fil...objectType=FILE.

  • Platform: Matlab, Scripts
  • Publisher: Yi Cao
  • Date: 09-01-2013
  • Size: 10 KB

In this article, procedure for using the the Matlab Neural Toolbox in order to identify and control an hypothetical non linear plant is explained with details enough to help anybody interested in that issue to know what to do and how to do a complete simulation using the tools that Matlab 7.1 and later offers to us. Many figures are included to be more explicit..

Simple Matlab Code for Neural Network Hebb Learning Rule. It is good for NN beginners students. It can be applied for simple tasks e.g. Logic "and", "or", "not" and simple images classification..

  • Platform: Matlab, Scripts
  • Publisher: Ibraheem
  • Date: 26-06-2013
  • Size: 10 KB

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

  • Platform: Matlab, Scripts
  • Publisher: Waleed Khalid
  • Date: 19-02-2013
  • Size: 20 KB

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 mfernandes@dca.ufrn.br.

  • Platform: Matlab, Scripts
  • Publisher: Marcelo Fernandes
  • Date: 23-04-2013
  • Size: 195 KB

Simple tutorial on pattern recognition using back propagation neural networks. the program has 3 classes with 3 images per class..

  • Platform: Matlab, Scripts
  • Publisher: Alaa Eleyan
  • Date: 24-04-2013
  • Size: 82 KB

OLSOFT Neural Network Library is a fully self-contained COM ActiveX control written in Visual C++ 6.0 and ATL. There are no external dependencies required for its operation such as other DLLs.

OLSOFT Neural Network Library is the class to create, learn and use Back Propagation neural networks and SOFM (Self-Organizing Feature Map). The library makes integration of neural networks functionality into your own applications easy and seamless. It enables your programs to handle data analysis, classification and forecasting needs.

  • Platform: Windows
  • Publisher: OLSOFT
  • Date:
  • Size: 460 KB

NEural SOrter, also known as NESO, was developed as an accessible and handy tool that can help you with neural sorting.

Now, you can perform the sorting you need, with the help of this useful piece of software. All you have to do is input your sorting options.


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Inspired by neurons and their connections in the brain, neural network is a representation used in machine learning. After running the back-propagation learning algorithm on a given set of examples, the neural network can be used to predict outcomes for any set of input values.

Neural Networks is a handy, easy to use tool specially designed to visually demonstrate the feedforward backpropagation algorithm. There is visual feedback for weight adjustments and error analysis.

Neural Network features support for graphical modification and creation of neural networks.

  • Platform: WinOther
  • Publisher: AIspace
  • Date:

Artificial Neural Network is a little application designed to demonstrate artificial intelligence.

Taking advantage of serialization, there are two parts of the network. The actual network, and then training data. The C# version of the Artificial Neural Network is much cleaner, more concise, and more efficient. for WindowsAll

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Generates knight's tours using a neural network algorithm.

There was a paper in an issue of Neurocomputing that got me intrigued: it spoke of a neural network solution to the knight's tour problem. I decided to write a quick C++ implementation to see for myself, and the results, although limited, were thoroughly fascinating.

The neural network is designed such that each legal knight's move on the chessboard is represented by a neuron. Therefore, the network basically takes the shape of the knight's graph over an n x n chess board.

  • Platform: WinOther
  • Publisher: Dmitry Brant
  • Date:
  • Size: 256 KB

This unique application is for all students of Neural Network & Fuzzy Systems across the world. It covers 149 topics of Neural Network & Fuzzy Systems in detail. These 149 topics are divided in 10 units.

Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail.

The USP of this application is "ultra-portability". Students can access the content on-the-go from any where they like.

  • Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
  • Publisher: faadooengineers.com
  • Date: 28-03-2014
  • Size: 2150 KB
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