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a simple matlab code to apply trail and error method .



The file includes mexfunction code calling "Kinect for Windows SDK" to receive depth data and color image from Kinect and a Matlab code to reshape the information matries. Please see the Readme.txt for usage.



Matlab code to read analog signal coming from a daq or from your sound card. This code allows to display the data in real-time or when the acquisition finished.
The real-time visualization method is better for monitoring.
The non...



RubyFann Bindings to use FANN (Fast Artificial Neural Network) from within ruby/rails environment. Requires: Ruby 1.8.6 or greater. gnu make tools or equiv for native code in ext To install: sudo gem install ruby-fann



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.



This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO) technique (Kennedy, Eberhart et al. 2001).

This add-in acts...



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...



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



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



A Ruby extension that provides a 2-Layer Back Propagation Neural Network, which can be used to categorize datasets of arbitrary size. The network can be easily (re-)stored to/from the hard disk.



The attached zip file contains what is needed to implement a two layer neural network. This will hopefully be the first part of a broader collection of neural network tools.

It can be used to train and simulate a NN with two layers, an...



Generic Neural Network. It's a class to build almost any type of Neural Networks, from a simple Perceptron to a SOM, supporting recurrence.



A very simple program that trains a neural network with 9 images(3 rectangles, 3 triangles and 3 circles)and then simulates the neural network in way to recognize 3 others images(1 rectangles, 1 triangles and 1 circles).



Learn how to use the Profiler tool, vectorized functions, and other tricks to writing efficient MATLAB code. This article includes how to convert any array into a column vector, bounding a value without if statements, and repeating/tiling a vector...



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...



This Program allows a Neural Network in conjuction with Image Processing to compute the best picture quality. This is done by splitting the Image into Its RGB components.



Neuroph is friendly Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and nice GUI make it easy to learn and use.



This project aims at creating a flexible, extendible and re-usable neural network library, and an XML based programming language to create, train and run neural networks using the above mentioned library.



Given a neural network object, this function returns the closed, symbolic, expression implemented by the network (as a string).

This allows you to use a neural network model without relying on the neural network toolbox.

Note...



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...