Hopfield Network Toolbox 2.0

New App, Simulink Models, Runge-Kutta simulation method for TSP and GQKP, improved documentation, new examples, minor bugs and enhancements

List of Bugs fixed and Enhancements in this release.

Hopfield Network Toolbox 1.2

Interface redesign to include schemes. Minor bugs and enhancements

List of Bugs fixed and Enhancements in this release.

Hopfield Network Toolbox 1.1.2

Bug Fixing and minor usability enhancements

List of Bugs fixed and Enhancements in this release.

Hopfield Network Toolbox 1.1.1

Bug fixing for Unix platforms.

List of Bugs fixed in this release.

Hopfield Network Toolbox 1.1

New App, Toolbox Documentation and Examples.

New Features:

• Hopfield Net TSP solver App
• Toolbox documentation
• Step-by-step examples
• TSPLIB automatic download

List of Bugs fixed in this release.

Hopfield Network Toolbox 1.0

Initial release of Hopfield Network Toolbox.

This release is mainly focused in solving the Traveling Salesman Problem (TSP) using the Continuous Hopfield Network (CHN). However, the release also provides a class structure to solve generic combinatorial optimization problems. Development in this area is undergoing.

The class to solve the TSP problems using CHNs is |tsphopfieldnet|. This network can solve any TSP problem, provided its coordinates or distance matrix.
The Toolbox also includes the library TSPLIB, a de facto library for TSP benchmarks. Instances with up to 13509 cities have been tested using the |tsphopfieldnet| network. Note that solving such instances might require a large amount of memory.

Three main simulation methods can be tested in this release:

• euler: traditional simulation method for Hopfield Networks.
• talavan-yanez: based on the paper A continuous Hopfield network equilibrium points algorithm by Pedro M. Talaván and Javier Yáñez
• divide-conquer: hybrid method based on the paper (pending publishing at the time of this release) Improving the Hopfield model performance when applied to the traveling salesman problem: A divide-and-conquer scheme by Lucas García, Pedro M. Talaván and Javier Yáñez.

The appropiate parametrization of the network (often called training phase in the literature) is performed by mapping of the CHN onto the TSP. This procedure for euler and talavan-yanez simulation methods is detailed in the paper Parameter setting of the Hopfield network applied to TSP by Pedro M. Talaván and Javier Yáñez, while for divide-conquer is explained in the already referenced paper.