A new connectionist model based on a non-linear adaptive filter

A connectionist model that is introduced based on the nonlinear extension of adaptive filter theory is introduced. It is shown that the model converges in the learning process to a global optimum. Experimental results indicate that the rate of convergence is considerably faster than has been reported for other models. It is concluded that the extended space approach leads to networks that can, with sufficient extension, synthesize any nonlinear discriminant function while maintaining unimodality. The adaptive filter knowledge also allows analytical solutions to the vital problem of parameter tuning, thus allowing good first-run performance on practical data