Ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator

Author

Ming Zhang

Author_Institution

Dept. of Phys., Christopher Newport Univ., Newport, VA, USA

fYear

2014

fDate

9-12 Dec. 2014

Firstpage

1

Lastpage

8

Abstract

New open box and nonlinear model of Ultra High Frequency Polynomial and Sine Artificial Higher Order Neural Network (UPS-HONN) is presented in this paper. A new learning algorithm for UPS-HONN is also developed from this study. A control signal generating system, UPS-HONN Simulator, is built based on the UPS-HONN models. Test results show that, to generate any nonlinear control signal, average error of UPS-HONN models is under 1e-6.

Keywords

feedforward neural nets; higher order statistics; learning (artificial intelligence); nonlinear control systems; polynomials; signal generators; UPS-HONN simulator; control signal generating system; control signal generator; learning algorithm; nonlinear control signal generator; nonlinear model; sine artificial higher order neural network; ultra high frequency polynomial; Adaptation models; Artificial neural networks; Biological neural networks; Data models; Neurons; Polynomials; Signal generators; Control Signal; Control Signal Generator; HONN; UPS-HONN;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on

Conference_Location

Orlando, FL

Type

conf

DOI

10.1109/CICA.2014.7013235

Filename

7013235