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This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet.
The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people.
This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network.
C++ implementation of high-performance associative neural networks models based on pseudo-inverse learning rule, also known as projection rule or attractor-based rule
Ideal for conference and event planners, independent planners, associations, event management companies, non-profits, and more.
YesEvents offers a comprehensive suite of services that spans the entire conference lifecycle and ensures every detail is executed with precision. Our commitment to exceptional customer service extends beyond conventional boundaries, consistently exceeding expectations and enriching both organizer and attendee experiences.
Develop a neural network-based extension of Hebb/Hopfield networks to obtain powerful associative memory engine, capable of storing complex structured data, subject to its internal organization.
Provide capability of patterns "blending".
HOPEN (Hopfield Open Network) is a program for patterns recognition based on a Hopfield Artificial Neural Network. It can learn using Hebb's Rule, Iterative Learning Scheme and Repeated Hebbian Learning.
Neural network library for C++ applications in Windows and Linux.
Multi-Layer perceptron, radial-basis function networks and Hopfield networks are supported.
You can interface this with Matlab's Neural Network Toolbox using the Matlab Extensions Pack