Application of generalized radial basis function networks to recognition of radar targets

This paper extends general radial basis function networks (RBFN) with Gauss ian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognit ion of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window f unctions (or called as potential functions). We present using recursive lea st square-backpropagation (RLS-BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional c ross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are intro duced. Six generalized kernel functions such as Gaussian, Double-Exponentia l, Triangle, Hyperbolic, Sine and Cauchy, are used as the hidden I/O functi ons of the RBFNCs, and the classification performance of corresponding GRBF NCs for classifying one-dimensional cross profiles of radar targets is disc ussed.