Backpropagation software: Graphical neural network development tool, Classification results live view, BrainCom is an artificial neural network and more.
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.
OpenDiscreteDynamicProgrammingTemplate : founds optimal constrainted parameters of a discrete controls with second order optimization template replacing Hessian with directional derivatives and backpropagation for digital filter(as neural network)
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.
The NeuroSolutions for MATLAB neural network toolbox is a valuable addition to MATLAB's technical computing capabilities allowing users to leverage the power of NeuroSolutions (www.neurosolutions.com) inside MATLAB and Simulink. The toolbox features 15 neural models, 5 learning algorithms and a host of useful utilities integrated in an easy-to-use interface, which requires ?next to no knowledge? of neural networks to begin using the product.
BrainCom is an artificial neural network. Utilizing backpropagation algorithm It can be used in almost all research fields such as nonlinear regression, forecasting, curve fit, pattern recognition, classification, decision making, problem optimization, time series analysis. Its user friendly environment makes it very easy to handle even by a novice. It can handle unlimited number of input vectors, inputs, outputs and hidden layer nodes.
