Title :
Analysis of Artificial Neural Networks for Pattern-Based Adaptive Control
Author :
Sbarbaro, D. ; Johansen, T.A.
Author_Institution :
Dept. of Electr. Eng., Univ. de Concepcion
Abstract :
Adaptive pattern-based control strategies adapt their parameters from an analysis of response patterns exhibited by the system. This work presents an analysis of a class of artificial neural network (ANN) pattern-based adaptive control. It provides conditions under which the adaptive algorithm will converge, and it also characterizes the closed-loop stability properties. In addition, a method for monitoring the adaptation is also proposed. Several simulation examples illustrate our findings
Keywords :
adaptive control; artificial intelligence; closed loop systems; neurocontrollers; pattern recognition; artificial neural networks; closed-loop stability; pattern-based adaptive control; response patterns; Adaptive algorithm; Adaptive control; Artificial neural networks; Control systems; Convergence; Electrical equipment industry; Pattern analysis; Pattern recognition; Programmable control; Proportional control; Adaptive control; neural networks (NNs); pattern recognition; proportional–integral (PI) control; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Computing Methodologies; Feedback; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.879762
