Neural Network Fourier Series

Neuroph is lightweight 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 GUI editor makes it easy to learn and use. Platforms: Windows, Mac, Linux

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 I only implemented for feed forward nets (MLPs) and not all possible... Platforms: Matlab

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 like a bridge or interface between MATLABd-deOaos NN toolbox and... 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

Program FFT2SURF plots double Fourier series representations for several different surfaces. The figures show effects of the number of series terms and use of Lanczos sigma factors to smooth Gibbs oscillations. The Fourier series of a doubly periodic function with periods px and py has the... 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

Program FFTSIGMA plots Fourier series representations with sigma approximation. The figures show effects of the number of series terms and use of Lanczos sigma factors to smooth Gibbs oscillations. The Fourier series of a periodic function with period px has the approximate form: f(x) = sum(... Platforms: Matlab

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

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... Platforms: Windows

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. 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

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

The output figures will show effects of the number of series terms and use of the Lanczos sigma factors to smooth the Gibbs oscillations. Platforms: Matlab

Your data is imported into a grid and used to train a neural network. The input values are forced to swing beyond their limits. The output values are forecasted by the neural network. A new neural network is created using the new inputs and forecasted outputs. The neural networks are... Platforms: Windows

This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others (see references for more... Platforms: Matlab

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... Platforms: Windows

Neural networks can discover patterns in data that humans might not notice and successfully predict the future trend. Addaptron Software has developed NNSTP-1, neural network computer tool, to help stock traders in predicting stock prices for short terms. NNSTP-1 predicts future share prices or... Platforms: Windows

Neural networks can discover patterns in data that humans might not notice and successfully predict the future trend. Addaptron Software has developed NNSTP-2, neural network computer tool, to help stock traders in predicting stock prices for short terms. NNSTP-2 predicts future share prices or... Platforms: Windows

annie stands for Artificial Neural Network Library and is a C++ API (library) for neural network training and execution. ersions exist for both Windows and flavours of Unix (tested on Linux). The library currently has support for training, saving and executing multi-layer perceptron,... Platforms: *nix

ffnet is meant to be an easy and fast feed-forward neural network training solution for python.. Platforms: *nix