Search Results for "engineering software" - Page 4

Todbot is an AI addon to the gamestool virtual machine. The aim is to create an evolving neural network topology that should be capable of creating the optimal topology and network weighting to solve any problem given it.

Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.

Este proyecto constituye una adaptacion y mejora del codigo ANFIS de dominio público de Roger Jang. / This project is an adaptation and improvement of the original public domain ANFIS code of Roger Jang.

The STAIR Vision Library (SVL), originally developed to support the STanford AI Robot, provides software infrastructure for computer vision, machine learning, and probabilistic graphical models.

Optex Analyzer is a software to analyze and compare algorithms to solve approximately optimization problems. It has a GUI that allows select a set of input files containing raw algorithm results. The analysis is shown with tables and charts.

Este projeto objetiva a criação de um Toolbox para utilização de algoritmos de Computação Bioinspirada. O Toobox oferece uma vasta coleção de algoritmos de ferramentas para o projeto de Algoritmos Genéticos e Redes Neurais.

A java tool for anytime and interactive sequence mining. Aims at providing users with a way of analyzing her activity traces and extract activity schemes from them.

A unique natural-language processing software, called Discovery, created on the CA Visual Objects/Vulcan.NET environment, which also has potential for effective "shallow approach" machine translation.

This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.

Multiclass machine learning

Neural - is neural network engine with object-oriented design. Features: - Supports: backpropogation, RPROP algorithms. - Flexible input/outputs framework. - Distributed calculations.

Extensible framework that enables productive design, analysis, and execution of arbitrary-sized neural-networks or system on a distributed, scalable, high-throughput runtime platform. Enables synapse-oriented-programming.

The project goal is to develop several IP cores that would implement artificial neural networks using FPGA resources. These cores will be designed in such a way to allow easy integration in the Xilinx EDK framework.

Blunder is an automated tool for analyzing chained exceptions in Java. It's usefull for classify, generate a customized error message and a list for possible solutions.

ANT is a lightweight implementation in C of a kind of artificial neural net called Multilayer Perceptron, which uses the backpropagation algorithm as learning method. The package includes an introductory example to start using artificial neural nets.

Betelgeuse is a machine learning modeling package designed to meet the requirements of heavy-duty industry use. It was designed to be efficient, reliable, and highly modular; it is developed primarily in Python to promote maintainability and rapid development, but uses Cython and C in critical bottlenecks for efficiency. It focuses on high-quality implementations of a diverse set of the most widely used machine learning algorithms. An important goal of Betelgeuse is to have a clean,...

pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due...

The open source project to port image analysis MaZda program to Linux and OS X platforms. See: http://www.eletel.p.lodz.pl/programy/mazda/

Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.