Abstract
The GMDH(Group Method of Data Handing) family of modeling algorithm emulates the self-organizing activity of the central nervous system, and discovers the structure (functional form) of empirical models that include many input variables.A generalized susccessive projection method for fast learning algorithm of the GMDH type model whose partial descriptions are represented by Radial Basis Function networks is developed and compared with the instantaneous learning algorithms such as the Least mean Square.(1) For the learning of partial descriptions of the perceptron type GMDH, a combined algorithm of the Successive Projection Method and the Orthogonal Projection Method is deveoped. (2) For the learning of the network type GMDH, an algorithm is derived ast he solution of an optimizaiton problem in which the Minkowski norm of distance travelled (step size) is minimized. Several examples show the validity of the methods.
