Results 261 to 270 of about 128,037 (313)

Nonlinear modelling and identification

Proceedings of IEEE Systems Man and Cybernetics Conference - SMC, 2002
There has been a considerable increase in activity in the field of identification of nonlinear systems. Side by side with the identification of discrete-time models based on Kolmogorov-Gabor polynomials, artificial neural networks, etc., there has been a great deal of progress in the identification of continuous-time models governed by ordinary ...
A. Patra, H. Unbehauen
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Evolutionary fuzzy models for nonlinear identification

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012), 2012
This paper proposes a new method for identification problems for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA).
Jérôme Mendes, Samuel Pinto, Rui Araújo, Francisco Souza 0001   +3 more
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Nonlinear Runoff Modeling: Parameter Identification

Journal of Hydraulic Engineering, 1983
A nonlinear functional rainfall‐runoff model is applied to an urban watershed (Curotte‐Papineau, Montreal) and the results are compared with those from the ILLUDAS model. Simulations are performed using a 5 minute time interval in order to better define the characteristics of the hydrographs.
Gilles G. Patry, Miguel A. Mariño
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Identification and Modelling of Hydraulic System with Nonlinearities

Applied Simulation and Modelling, 2011
The paper deals with development of a mathematical model of a hydraulic system. A three tank laboratory model (Amira DTS200) was investigated, its characteristics were measured and a process of evaluation of obtained data is described in detail. Although the three tank system is a classical modelling task described in many publications, this papers ...
Chalupa, Petr, Novák, Jakub, Bobál, Vladimír   +2 more
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A recursive algorithm for nonlinear model identification

Applied Mathematics and Computation, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, P.K., Li, Kang
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Model quality in identification of nonlinear systems

IEEE Transactions on Automatic Control, 2005
In this note, the problem of the quality of identified models of nonlinear systems, measured by the errors in simulating the system behavior for future inputs, is investigated. Models identified by classical methods minimizing the prediction error, do not necessary give "small" simulation error on future inputs and even boundedness of this error is not
MILANESE, Mario, NOVARA, Carlo
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Subsystem identification for nonlinear model updating

2006 American Control Conference, 2006
We consider model updating by adding correction terms to the model equations in the state space form. Two classes of errors, namely model errors in the dynamics equation and model errors in the output equation, are considered. The model errors are assumed to arise from an unknown nonlinear subsystem.
Harish J. Palanthandalam-Madapusi, Steven Gillijns, Bart De Moor, Dennis S. Bernstein   +3 more
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Nonlinear identification of neuron models

2015 IEEE Conference on Control Applications (CCA), 2015
The paper discusses when and how a nonlinear autonomous system can be uniquely identified from periodic data. This is a central problem in systems biology, where it e.g. arises when the dynamics of spiking neurons are modeled. The paper illustrates the problem by least squares identification of two autonomous neuron models, of order 4 and 2.
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Identification of a nonlinear block-oriented model

2010 Sixth International Conference on Natural Computation, 2010
Key term separation principle, auxiliary model and a modified particle swarm optimization (MPSO) algorithm are applied to identify parameters of a block-oriented model represented by Hammerstein model with two-segment piecewise nonlinearities. Expressing output of the nonlinear Hammerstein models as a regressive equation in all parameters via the key ...
Xiaoping Xu, Fucai Qian, Ding Liu, Feng Wang   +3 more
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Modeling, identification, and control of a class of nonlinear systems

IEEE Transactions on Fuzzy Systems, 2001
In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove
Huaguang Zhang, Yongbing Quan
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