A hybrid neural network/rule-rased architecture for analogue function approximation
K.M. Curtis, J.D. Burniston · 2004
Investigations have been carried out into combining a rule-based system and an artificial neural network (ANN) to achieve a new computing structure for function approximation. Results are presented for the performance of the hybrid structure when applied to modelling a continuous nonlinear function, and are compared to the results obtained when modelling the function using only an ANN.