Priority ordered architecture of neural networks

Abstract

In the architecture introduced, outputs of neurons (or neural nets) have different priorities, beside the differences in topological position and value of these outputs. We discuss how priority ordered neural networks (PONNs) have similarity to knowledge representation in the human brain. Also a general mathematical description of the PONN is introduced. The priority ordered single layer perceptron (POSLP) and the priority ordered radial basis function nets (PORBFN) for pattern classification are analyzed. The experiment shows that the learning speed of the POSLP and PORBFN are much faster than that of the multilayered feedforward neural networks with existing BP algorithms.