On modifying the weights in a modular recurrent connectionist system

Author

Elsherif, H. ; Hambaba, M.

Author_Institution

Intelligent Syst. Lab., Stevens Inst. of Technol., Hoboken, NJ, USA

Volume

1

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

535

Abstract

A modular recurrent connectionist architecture is proposed to classify binary and continuous patterns. This system consists of three networks: one feedforward backpropagation (BP) network and two self-organization map (SOM) networks. The feedforward (basic) network is trained until a saturation error level occurs. Simultaneously, the first SOM (input control) network and the last SOM (output control) define the mapping features for the given input/output patterns. The resultant features are used by a Gaussian potential function to adjust the weights of the basic network and to classify the given patterns

Keywords

feature extraction; feedforward neural nets; pattern classification; recurrent neural nets; self-organising feature maps; Gaussian potential function; binary pattern classification; continuous pattern classification; feedforward backpropagation network; input/output patterns; mapping features; modular recurrent connectionist system; saturation error level; self-organization map networks; weights; Computer architecture; Computer science; Feedforward systems; Hardware; Intelligent systems; Laboratories; Neurons; Performance evaluation; Psychology; Simultaneous localization and mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374221

Filename

374221