Authors:
Karima Amoura
1
;
Patrice Wira
2
and
Said Djennoune
1
Affiliations:
1
Université Mouloud Mammeri, Algeria
;
2
Université de Haute Alsace, France
Keyword(s):
Artificial neural networks, Recurrent network, State space, State estimation, System identification, System dynamics.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
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:
In this paper, a specific neural-based model for identification of dynamical nonlinear systems is proposed. This artificial neural network, called State-Space Neural Network (SSNN), is different from other existing neural networks. Indeed, it uses a state-space representation while being able to adapt and learn its parameters. These parameters are the neural weights which are intelligible or understandable. After learning, the SSNN therefore is able to provide a state-space model of the dynamical nonlinear system. Examples are presented which show the capability of the SSNN for identification of multivariate dynamical nonlinear systems.