Получить neural network

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.

Goldengem is a general purpose Neural Network. It is a simple no-nonsense program, containing a sophisticated mathematical calculation, which is being successfully used by an increasing number of traders. Its StockDownloader downloads free data. Choose tickers, in capitals, separated by commas that may influence the price you wish to analyze. Load a set of related tickers from the internet. Select number of days to look ahead.

Freeware for fast training, validation, and application of neural and conventional classifiers including multilayer perceptron, functional link network, piecewise linear network, self organizing map and K-Means. C source provided for applying trained networks. Extensive help. User-supplied txt-format training data files, containing rows of numbers, can be of any size. Pruning for approximate structural risk minimization.

Freeware for fast training, validation, and application of regression/approximation networks including the multilayer perceptron, functional link network, piecewise linear network, self organizing map and K-Means. C source for applying trained networks. Extensive help. User-supplied txt-format training data files, containing rows of numbers, can be of any size. Pruning for approximate structural risk minimization.

Neural network classification results live view (like a movie). Free software for playing with neural networks classification. Features: Easy, ready to play with, many network architectures, different shapes of training data sets, learning with backpropagation algorithm, optional momentum. For better understanding of neural networks.

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.