Journal:Informatica
Volume 7, Issue 4 (1996), pp. 431–454
Abstract
Adaptive Control Distributed Parameter Systems (ACDPS) with adaptive learning algorithms based on orthogonal neural network methodology are presented in this paper. We discuss a modification of orthogonal least squares learning to find appropriate efficient algorithms for solution of ACDPS problems. A two times problem linked with the real time of plant control dynamic processes and the learning time for adjustment of parameters in adaptive control of unknown distributed systems is discussed.
The simulation results demonstrate that the orthogonal learning algorithms on a neural network concept allow to find perfectly tracked output control distributed parameters in ACDPS and have rather a good perspective in the development of generalised ACDSP theory and practice in the future.
Journal:Informatica
Volume 3, Issue 1 (1992), pp. 98–118
Abstract
We consider a class of identification algorithms for distributed parameter systems. Utilizing stochastic optimization techniques, sequences of estimators are constructed by minimizing appropriate functionals. The main effort is to develop weak and strong invariance principles for the underlying algorithms. By means of weak convergence methods, a functional central limit theorem is established. Using the Skorohod imbedding, a strong invariance principle is obtained. These invariance principles provide very precise rates of convergence results for parameter estimates, yielding important information for experimental design.