Search Results for "artificial neural network python"

In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet controller...

tlearn is a backpropagation neural network simulator, written by Jeff Elman. xtlearn is a version of tlearn for the X Window System. OSXtlearn is xtlearn wrapped in a MacOSX application bundle that runs ons MacOSX 10.5 or higher and that requires XQuartz. OSXtlearn is created by Harm Brouwer.

AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope...

NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.

This is an implementation of the Granular Neural Network architecture defined by S. Dick, A. Tappenden, C. Badke, O. Olarewaju. It is provided for the use of the public, and the convenience of researchers who may wish to develop or use this new system.

SNNSraster is a utility for quick ANN analysis of raster GIS maps with the use of Stuttgart Neural Network Simulator trained network files. It was developed to read and write binary raster files. SNNSraster is a project of the Geography Laboratory of the University of Siena. The code was developed by Giancarlo Macchi Jánica between 2006 and 2007. SNNSraster's fundamental objective is to improve the ability to integrate the use of artificial neural networks in GIS environments.

GNNS - GNNS Neural Network Simulator, is both a set of libraries and an interface for creating and learning neural networks. It is aimed to support as many network typs and learning algorithms as possible. GNNS is meant to support different platforms.