Title :
Ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator
Author_Institution :
Dept. of Phys., Christopher Newport Univ., Newport, VA, USA
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;
Conference_Titel :
Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CICA.2014.7013235
