Abstract
The set of properties characterizing a given system assumes the name of model of that system. In the literature, models of various types have been defined [1]:
-
mental or intuitive models, listed by a set of logical inferences residing in the human mind and not expressed analytically;
-
graphic models in which the properties of the system are represented by graphs or tables;
-
mathematical models in which the relations connecting the variables of a system one to another are expressed by means of differential equations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bittanti S. Identificazione Parametrica. CLUP
Billings SA. Identification of Nonlinear Systems — A Survey. Proc. IEE Vol. 127, Part D, N. 6. 1980
Leontaritis J, Billings SA. Input-Output Parametric Models for Non-linear Systems. Part I: Deterministic Non-linear Systems, Int. J. Control. 1985; 41: 2303–2328
Chen S, Billings SA. Representations of Non-linear Systems: The NARMAX Model. Int. J. Control. 1989; 43: 5: 1013–1032
Rudin W. Principles of Mathematical Analysis. McGraw-Hill, New York, 1964
Billings SA, Leontaritis J. Parameter estimation of non-linear systems. Proc. 6th IFAC Conference on Identification and Systems Parameter Estimation, Washington, 1982: 427–432
Miller WT, Sutton RS, Werbos PJ. Neural Networks for Control. The MIT Press, Cambridge, MA, 1990
Billings SA, Jamaluddin HB, Chen SA. Properties of Neural Networks with Application to Modeling Non-linear Dynamical Systems. Int. J. Control. 1992; 55: 1: 193–224
Billings SA. Voon WSF. Correlation Based Model Validity Tests for Non-linear Models. Int. J. Control. 1986; 44: 1: 235–244
Ljung L. System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ, 1993
Zurada JM. Introduction to Artificial Neural Systems. West Publishing Company, New York, 1992
Newton RT, Xu Y. Neural Network Control of Space Manipulator, Control Systems, 1993;13:6
Arena P. Reti Neurali per Modellistica e Predizione in Circuiti e Sistemi’, Ph.D. Thesis. Dipartimento Elettrico Elettronico e Sistemistico, Universita di Catania, Italy 1993
Fortuna L, Muscato G, Nunnari G, Pandolfo A, Plebe A. Application of Neural Control in Agriculture: An Orange Picking Robot. Acta Horticulturae. 1996; 406: 441–450
Fortuna L, Muscato G, Nunnari G, Papaleo R. A Neural Networks Approach to Control the Temperature on Rapid Thermal Processing. MELECON 96 IEEE Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications. Bari (Italy), 13–16 Maggio 1996; 649–652
Fortuna L, Muscato G, Xibilia MG. Attitude Feedforward Neural Controller in Quaternion Algebra, in Soft Computing with Industrial Applications: Recent Trends in Research and Development. TSI Press, Albuquerque, 1996; 5: 222–228
Fortuna L, Gallo A, Muscato G, Xibilia MG. Controllo Neurale in Algebra Quaternionica dell’Assetto di un Corpo Rigido Nello Spazio’, AEI Automazione Energia Informazione, UTET periodici. 1997; 84: 5: 72/480–79/487
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag London
About this chapter
Cite this chapter
Fortuna, L., Rizzotto, G., Lavorgna, M., Nunnari, G., Xibilia, M.G., Caponetto, R. (2001). Neural Networks for Modeling and Controlling Dynamical Systems. In: Soft Computing. Advanced Textbooks in Control and Signal Processing. Springer, London. https://doi.org/10.1007/978-1-4471-0357-8_5
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0357-8_5
Publisher Name: Springer, London
Print ISBN: 978-1-85233-308-9
Online ISBN: 978-1-4471-0357-8
eBook Packages: Springer Book Archive