Results 1 
2 of
2
Adaptive RBFNN Versus SelfTuning Controller: An Experimental Comparative Study
 Study”, Proceedings of European Control Conference (ECC 97
, 1997
"... In this paper a comparative study evaluates two different techniques: neural adaptive control and self tuning (ST) control. The neural adaptive control is based on a new hybrid learning technique using an adaptive learning rate for the online learning of a Gaussian Radial Basis Function Neural Netw ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
In this paper a comparative study evaluates two different techniques: neural adaptive control and self tuning (ST) control. The neural adaptive control is based on a new hybrid learning technique using an adaptive learning rate for the online learning of a Gaussian Radial Basis Function Neural Network (RBFNN) type. In the self tuning structure the control parameters are updated from a pole placement design via the estimation of the process model. A selective forgetting factor method is applied to both control schemes: in the RBFNN to online estimate the second layer weights and in the ST to estimate the process parameters.
RBFNN for RealTime Process Identification and Control with Selective Forgetting
 In: Proceedings of the 2nd Portuguese Conference on Automatic Control  Controlo96
, 1996
"... This work is concerned with the problem of the realtime identification and control of dynamic nonlinear systems using a neural network approach. The chosen model was the Gaussian Radial Basis Function Neural Network (RBFNN) type, due to its universal approximation property and also to the fact tha ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
This work is concerned with the problem of the realtime identification and control of dynamic nonlinear systems using a neural network approach. The chosen model was the Gaussian Radial Basis Function Neural Network (RBFNN) type, due to its universal approximation property and also to the fact that the parameters are linearly related to the outputs allowing linear learning algorithms. A hybrid learning technique is developed, using an adaptive learning rate with process monitoring, and taking advantage of the locality property of this type of networks, we apply a selective forgetting algorithm. This online learning is used both for identification and control. A novel technique is proposed for the online adaptive control. The potential of the proposed method is demonstrated by a simulation example applied to a theoretical model and to a real laboratory process. Keywords: RealTime Identification and Control; Radial Basis Function Neural Networks; Nonlinear Systems; Selective Forge...