RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION
Author:
- 21 November 1999
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Pattern Recognition and Artificial Intelligence
- Vol. 13 (07) , 1083-1101
Abstract
This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural 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 neural 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 this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.Keywords
This publication has 6 references indexed in Scilit:
- Progress in supervised neural networksIEEE Signal Processing Magazine, 1993
- Networks for approximation and learningProceedings of the IEEE, 1990
- The multilayer perceptron as an approximation to a Bayes optimal discriminant functionIEEE Transactions on Neural Networks, 1990
- Probabilistic neural networksNeural Networks, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- On Estimation of a Probability Density Function and ModeThe Annals of Mathematical Statistics, 1962