Journal:Informatica
Volume 16, Issue 4 (2005), pp. 571–586
Abstract
Due to high nonlinearities and time-varying dynamics of today's control systems fuzzy learning controllers find appliance in practice. The present paper proposes a method for the synthesis of the learning fuzzy controllers where an expert knowledge about a process is applied to form a learning mechanism that is used to acquire information for the knowledge base of the main fuzzy controller. According to the proposed method an expert knowledge is used to describe how the controller should learn to control rather than to control the process. The results of experiments on heating system and level/pressure system prove the practical relevance of the design strategy of a learning fuzzy controller.
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.