A preliminary comparison of FIR-synapse neural networks with and without local output feedback

Author

Kuehner, Nathanael ; Stevenson, Maryhelen

Author_Institution

Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB, Canada

Volume

1

fYear

1995

Firstpage

198

Abstract

In the study of neural network architectures, a wide variety of neuron structures and interconnection schemes have been proposed and studied. The article looks at a multilayered globally feedforward network architecture in which each neuron has potential for local output feedback and where all interconnections, both feedforward and feedback, are made by FIR filters. Issues of stability and convergence, speed, efficiency, and generality are investigated. The applications of this architecture to problems of system identification are described in an attempt to assess its strengths and weaknesses in comparison to the standard feedforward FIR network

Keywords

FIR filters; convergence of numerical methods; feedback; feedforward neural nets; filtering theory; identification; learning (artificial intelligence); multilayer perceptrons; neural net architecture; numerical stability; FIR filters; FIR-synapse neural networks; convergence; efficiency; feedforward FIR network; generality; interconnections; local output feedback; multilayered globally feedforward network architecture; neural network architectures; neuron; neuron structures; speed; stability; system identification; Backpropagation; Finite impulse response filter; IIR filters; Neural networks; Neurofeedback; Neurons; Niobium; Output feedback; System identification; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1995. Canadian Conference on

Conference_Location

Montreal, Que.

ISSN

0840-7789

Print_ISBN

0-7803-2766-7

Type

conf

DOI

10.1109/CCECE.1995.528108

Filename

528108