Modified growing neural gas algorithm for faster convergence on signal distribution sudden change

The paper deals with the problem of faster optimal coverage of a Growing Neural Gas algorithm for random signals appearing with non-stationary distributions. A modification of the algorithm that successfully solves this problem will be presented with simulations in a 2-D environment and statistical results that will show its efficiency. A comparison with a previous solution for the same problem using so called Utility measure will be also given.