Different approaches to implementing a radial basis function neurocomputer

Describes and analyses three different approaches to implementing a radial basis function neural network with an electronic neurocomputer. This type of network utilizes a radial basis function as the transfer function of the neuron. The three different approaches are: (1) a completely analog system using custom analog VLSI circuits, (2) a completely digital system using a commercially available digital signal processor, and (3) a hybrid analog/digital system using both analog VLSI circuits and a digital signal processor. The circuits required for all three of these approaches have been designed, fabricated, and tested. Two of these approaches, the completely digital system and the hybrid system, have been realized in hardware. Circuit performance is analysed, and the resulting system implications are considered. The tradeoffs of the three different approaches are presented, and a method is proposed for determining an optimal system in light of the constraints imposed by the application