Radial basis probabilistic neural networks: Model and application

This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neur al networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neur al network model referred to as radial basis probabilistic neural network ( RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply th is new RBPNN to the recognition of one-dimensional cross-images of radar ta rgets (five kinds of aircrafts), and the experimental results are given and discussed.