Software Downloads for "Learning Neural Network"

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..

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

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

.

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

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

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:

OptimuStock is the new generation of intelligent forecasting software based on neural network technology. Don’t be intimidated by these terms. You don’t need to be an expert in artificial intelligence to use our software. In just 3 clicks, you can start to predict the stock prices. Generally, OptimuStock use the ability of nonlinear machine learning models to forecast market direction/prices using technical indicators and fundamental data as features. OptimuStock is the only forecasting Excel add in with an automatic neural network architecture and parameters selection.

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

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

This is a learning model for matrix-based data. It can be more efficient than standard vector-based models when the training sample is small or the inputs are matrices, since this model has less parameters than standard ones and exploits an inner structure of the inputs. This model can be interpreted as linear neural network with matrix inputs. You can see theory at http://pd.hacker.lt/linear_mnn.pdf (only a draft at this time).

  • Platform: Matlab, Scripts
  • Publisher: Povilas Daniu?is
  • Date: 10-04-2013
  • 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

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

NeuroSolutions is a highly graphical neural network development tool for Windows. This leading edge software combines a modular, icon-based network design interface with an implementation of advanced learning procedures, such as recurrent backpropagation, backpropagation through time and genetic optimization. The result is a virtually unconstrained environment for designing neural networks for research or to solve real-world problems..

ANN (Artificial Neural Networks) is inspired by biology. Specially, they use thoughts from the way in which the brain of human works. The brain of human is poised of extraordinary cells known as neurons. Estimation of the total neurons number in the brain of human cover a broad range, and there are over a hundred different types of neurons, divided into different groups known as networks. The entire network comprises different thousand neurons which are very much interconnected. Therefore, the brain can be analysis as the neural networks collection.

NeuroXL Classifier is a neural network add-in for Microsoft Excel. NeuroXL Classifier (ClassifierXL) is an add-in for Excel designed to aid experts in real-world data mining and pattern recognition tasks. It hides the underlying complexity of neural network processes while providing graphs and statistics for the user to easily understand results. NeuroXL Classifier uses only proven algorithms and techniques, and integrates seamlessly with Microsoft Excel. Neural networks are a proven, widely used technology to solve complex classification problems.

  • Platform: Windows
  • Publisher: OLSOFT
  • Date: 05-12-2008
  • Size: 961 KB

EasyNN-plus is a neural network system for Microsoft Windows. It makes the creation of neural networks easy. It allows the user to produce multilayer neural networks from a grid or from text files and images. The user can produce training, validating and querying files using the facilities in EasyNN-plus or using any editor, word processor or spreadsheet that supports text files. EasyNN-plus can learn from training data and can self validate while learning. It can be queried from a file or interactively.

We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face.

  • Platform: Matlab, Scripts
  • Publisher: than Png
  • Date: 08-02-2013
  • Size: 18698 KB

Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the Apache 2.0 license, and it's FREE for you to use it.

Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.

  • Platform: Windows
  • Publisher: Zoran Sevarac
  • Date:
  • Size: 65843 KB
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