Authors:
Ivo Bukovsky
1
;
Jan Voracek
1
;
Kei Ichiji
2
and
Homma Noriyasu
2
Affiliations:
1
College of Polytechnics Jihlava, Czech Republic
;
2
Tohoku University Graduate School of Medicine, Intelligent Biomedical System Engineering Laboratory and Tohoku University, Japan
Keyword(s):
Polynomial Neural Networks, Higher Order Neural Units, Model Reference Adaptive Control, Conjugate Gradient, Nonlinear Dynamical Systems.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The paper reviews the nonlinear polynomial neural architectures (HONUs) and their fundamental
supervised batch learning algorithms for both plant identification and neuronal controller training. As a
novel contribution to adaptive control with HONUs, Conjugate Gradient batch learning for weakly
nonlinear plant identification with HONUs is presented as efficient learning improvement. Further, a
straightforward MRAC strategy with efficient controller learning for linear and weakly nonlinear plants is
proposed with static HONUs that avoids recurrent computations, and its potentials and limitations with
respect to plant nonlinearity are discussed.