Application of the Klopfian neuron model to function minimization

Author

Politis, Demetrios T.

Author_Institution

Adv. Concepts Div., Environ. Res. Inst. of Michigan, Ann Arbor, MI, USA

fYear

1988

fDate

25-27 May 1988

Firstpage

537

Lastpage

541

Abstract

The author discusses the use of the adaptive learning controller (ALC) algorithms developed by A.G. Barto and R.S. Sutton (1981), based on the Klopfian neuron model, for function minimization. In this application the ALC is placed directly into the signal processing loop of a synthetic aperture radar and the task assigned to it is to minimize the 3-dB width of the system impulse response function. This results in the correction of the quadratic and possibly higher-order system phase errors

Keywords

adaptive systems; learning systems; minimisation; neural nets; radar systems; signal processing; transient response; Klopfian neuron model; adaptive learning controller; function minimization; signal processing loop; synthetic aperture radar; system impulse response function; Adaptive control; Adaptive signal processing; Additive noise; Automatic logic units; Control systems; Minimization methods; Neurons; Programmable control; Radar signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence for Industrial Applications, 1988. IEEE AI '88., Proceedings of the International Workshop on

Conference_Location

Hitachi City

Type

conf

DOI

10.1109/AIIA.1988.13344

Filename

13344

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3438033