Stable Recursive Identification Using Radial Basis Function Networks

The methodology developed for adaptive control applications of radial basis function networks can easily also be used to produce stable, convergent, recursive identifiers, in both continuous and discrete time. The latter is of particular interest as it can serve as a model of the general neural network functional learning process, and hence gives some direct insights into the factors influencing the success of these methods.