A new training pattern selection method for adaptive neural control us
ing backpropagation learning is presented. When applying this method t
o an asynchronous transfer mode (ATM) call admission control, some adv
antages are observed: A very small training pattern table is sufficien
t, the learning is independent of observed data, and the controller is
easily adaptable to traffic changes. The proposed neural control mode
l is analysed by computer simulations in heterogeneous traffic environ
ments and the results show its effectiveness compared with other metho
ds.