Neural Fuzzy

This code implements the basic back propagation of error learning algorithm. the network has tanh hidden neurons and a linear output neuron, and applied for predicting y=sin(2pix1)*sin(2pix2).We didn't use any feature of neural network toolbox.

FCMTHRESH Thresholding by 3-class fuzzy c-means clustering [bw,level]=fcmthresh(IM,sw) outputs the binary image bw and threshold level of image IM using a 3-class fuzzy c-means clustering. It often works better than Otsu's methold which outputs larger or smaller threshold on fluorescence images....

Fuzzy k-nearest neighbors classifier that can work with training samples whose label info is fuzzified. The prototype is as follows.[y,predict_class] = f_knn(tr,tr_memberships,te,k)tr: training samplestr_memberships: membership values of traning sampleste: testing samplesk: k value vector (more...


Platforms: Matlab

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 input layer and an output layer. The output layer's activation...


Platforms: Matlab

This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing.For a full book description and ordering information, please refer to http://www.mathworks.com/support/books/book1444.jsp.


Platforms: Matlab

-Compatible with pre-2010 vers. of Matlab and Neural network toolbox-Trains a perceptron for the spring and one for the damper. -Runs a simulation with forcing function and noise.-If you don't have the toolbox, you can still use my back-propagation.


Platforms: Matlab

The extended Kalman filter can not only estimate states of nonlinear dynamic systems from noisy measurements but also can be used to estimate parameters of a nonlinear system. A direct application of parameter estimation is to train artificial neural networks. This function and an embeded example...


Platforms: Matlab

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...


Platforms: Matlab

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...


Platforms: Matlab

Noisy environments make difficult the pattern recognition tasks. In this sense, and due to the artifacts that are present in the acquisition process, the most of the biomedical images (i.e. coming from magnetic resonance tests) become noisy and it is complicated even to define a Region Of...


Platforms: Matlab

This code removes a feature from data file which it considers bearing least amount of relevant information.It's usage intended to classification problems. It is based on fuzzy measures and similarity. Much wider description, explanation and some results can be found in article:P. Luukka, (2011)...


Platforms: Matlab

Brightness Preserving Dynamic Fuzzy Histogram Equalization(BPDFHE) proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. This...


Platforms: Matlab

Here we have an unknown function but we can have every value of function respect to every input. so there I used a fuzzy approximation method Ito approximate the function, here I used sin function to compare answers.there is a function "isinrange" to find membership values.


Platforms: Matlab

The enclosed function implements nonlinear fuzzy robust PCA algorithmintroduced inP. Luukka, 'A New Nonlinear Fuzzy Robust PCA Algorithm and Similarityclassifier in classification of medical data sets',International Journal of Fuzzy Systems, Vol. 13, No. 3, September 2011pp. 153-162.


Platforms: Matlab

The matrix implementation of the two-layer Multilayer Perceptron (MLP) neural networks.The matrix implementation of the MLP and Backpropagation algorithm for two-layer Multilayer Perceptron (MLP) neural networks.Marcelo Augusto Costa FernandesDCA - CT - UFRNmfernandes@dca.ufrn.br


Platforms: Matlab

Again I'm uploading my homework...This is a GUI which enables to load images and train a Hopfield network according to the image.You can run the network on other images (or add noise to the same image) and see how well it recognize the patterns.Hopfield neural networks simulate how a neural...


Platforms: Matlab

Fuzzy SEO Booster increases your sites search engine positions for long tail keywords automatically!Here is how it works: When someone finds your site using a long tail keyword combination not already on that page, it records it, and adds it to the page in a widget box! Think of it as an auto...


Platforms: PHP

JavaBrains is an implementation of common AI algorithms in Java. Current algorithms implemented are: * Neural Networks * Genetic Algorithms * A* Pathfinding I hope to add the following features in the near future: * Bayesian Networks * State Machines * Rule-based inference * Fuzzy logic *...


Platforms: Mac

Neural network poker is a Texas Hold 'Em program with the option to add a player controlled by a neural network. This program is in alpha stage so there are still some bugs and unimplemented features...


Platforms: Mac

SINCO is a GPL library with functions to implement Artificial Neural Networks simulators. You are free to define the network (ilimited neurons and layers) and the functions (training, threshold, etc.).


Platforms: *nix