Priority ordered architecture of neural networks

Author

Shoujue, Wang ; Huaxiang, Lu ; Xiangdong, Chen ; Yujian, Li

Author_Institution

Inst. of Semicond., Acad. Sinica, Beijing, China

Volume

2

fYear

1999

fDate

36342

Firstpage

808

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

Keywords

learning (artificial intelligence); neural net architecture; pattern classification; perceptrons; radial basis function networks; human brain; knowledge representation; learning speed; multilayered feedforward neural networks; priority ordered architecture; radial basis function nets; single layer perceptron; Artificial neural networks; Biological neural networks; Feedforward neural networks; Humans; Knowledge representation; Multi-layer neural network; Neural networks; Neurons; Pattern analysis; Pattern classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831054

Filename

831054