Comparison of different growing radial basis functions algorithms for control systems applications

Supervised growing neural networks (SGNNs) are a class of self-organizing maps without a predefined structure. In fact the structure of the approximators is generated autonomously from a set of training data. New algorithms for SGNNs have been proposed previously with the objective to provide improved performance for on-line sequential learning. In this work two important class of algorithms for SGNNs are compared: resource allocating networks (RAN) and dynamic cell structures (DCS). The main objective is to provide a clear comparative study, which could help to assess the performance among the different algorithms for on-line real time application purposes. The main performance criteria are: the accuracy following the same amount of training-in terms of standard deviation and estimation error trends-and the computational complexity of the algorithm. The comparison has been performed through two different studies. The first study is relative to the learning of a nonlinear 3-D function. The second study is relative to the learning of a 3-D look-up table of a specific aerodynamic parameter of an aircraft.