3 edition of **System identification using interval dynamic models** found in the catalog.

System identification using interval dynamic models

- 206 Want to read
- 3 Currently reading

Published
**1993**
by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va
.

Written in English

- System identification.

**Edition Notes**

Statement | by L.H. Keel, J.S. Lew, S.P. Bhattacharyya. |

Series | [NASA contractor report] -- NASA CR-192895., NASA contractor report -- NASA CR-192895. |

Contributions | Lew, J. S., Bhattacharyya, S. P., United States. National Aeronautics and Space Administration. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL14694293M |

comfortable with using a static model formulation as long as we are aware of its limitations (section 5). Section 6 shows brie ﬂy that underlying both dynamic policy analysis and the correspondence between dynamic and static formulations, is the nature of the so-lution of dynamic models. Section 7 sketches how the analysis can be extended to. The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective.

The accurate interval estimation of structural responses can be efficiently obtained by application of Monte Carlo (MC) simulation combined with surrogate models. By means of the concept of interval length, a novel quantitative metric named as interval deviation degree (IDD) is constructed to characterize the disagreement of interval. interval identification technique is deduced, which al-lows the detection ofbehaviours like theonein Figure 1. Finally, the waythe interval identification technique is implemented is described. QualitativeDescription ofMeasuredData The procedure described here follows the concept presentedin (King, Schaich, Miinker, and Hellinger ).

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. In this paper, an inverse method that combines the interval analysis with regularization is presented to stably identify the bounds of dynamic load acting on the uncertain structures. The uncertain parameters of the structure are treated as intervals and hence only their bounds are needed.

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The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction.

A common approach is to start from measurements of the behavior of the system and the external. You can use the System Identification app or commands to estimate linear and nonlinear models of various structures.

In most cases, you choose a model structure and estimate the model parameters using a single command. Consider the mass-spring-damper system, described in About Dynamic Systems and Models.

If you do not know the equation of. interval estimation in system dynamics models. Introduction Statistical parameter estimation is increasingly used in system System identification using interval dynamic models book. Popular simulation software (e.g., Vensim, Powersim) includes tools for model calibration, allowing modelers to estimate parameters easily.

These packages enable modelers to use. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the : Springer-Verlag Berlin Heidelberg.

Modeling errors, represented as uncertainty associated with the parameters of a mathematical model, inevitably exist in the process of constructing a theoretical model of real structures and limit the practical application of system identification. They are usually represented either in a deterministic manner or in a probabilistic way.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A new technique is presented to identify intervals for parameters and initial conditions for nonlinear dynamic systems based on an imprecise mathematical model and measurements of system variables.

This technique employs a fuzz interval qualitative simulator for interval dynamical models and a qualitative. Ways of classifying models are presented and the experimental approach to model development through system identification and optimisation is introduced.

Issues of model quality are emphasised, together with model reuse, the development of libraries of sub-models, the potential of generic descriptions and use of modelling within procurement. The general trend in the system identification theory is to model system with inaccuracy as a set of mathematical models, which represent all possible aspects of physical plants, so inaccuracy appears as sets of bounded range parameters.

A system identification technique is presented in [2]; it is based on taking input-output data in frequency. Excel Solution. The dynamic model can be written in discrete form by specifying that the voltage is constant between sampling intervals.

The discrete form of the equation accounts for non-zero initial conditions in temperature (x 0) and voltage (u 0).In. An interval model updating strategy using interval response surface models Mechanical Systems and Signal Processing, Vol.

Dynamic response analysis on torsional vibrations of wind turbine geared transmission system with uncertainty. Interval Analysis for System Identification of Linear MDOF Structures in the Presence of Modeling Errors Journal of Engineering Mechanics, Vol.No. 11 Structural Damage Detection Using Generalized Flexibility Matrix and Changes in Natural Frequencies.

System Dynamics Tools System Dynamics is an approach to solving problems that utilizes different tools, most notably simulation, to support the work. These pages contain links to many tools, both open source and proprietary, that are frequently used by people working in the field.

Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process s: 1.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A new technique is presented to identify intervals for parameters and initial conditions for nonlinear dynamic systems based on an imprecise mathe-matical model and measurements of system vari-ables.

This technique employs a fuzz interval qualitative simulator for interval dynamical mod-els and a qualitative. • Time series – A function x(t)=x(t,w) whose values depend on a random variable w is called a random or stochastic process. (This function is also a r.v.) – For a fixed w, x(t,w) is only a function of t and is called a realization of the stochastic processor sample function.

– In discrete time, infinite sequence of {xk} (k=1,∞) or a sequence {x. Interval Identification a Modelling and Design Technique for Dynamic Systems. VI Preface and periodic test signals serve to understand some basics of identiﬁcation and lay ground for other identiﬁcations methods.

Part IIisdevoted tothedetermination of impulseresponses withauto- andcross- correlation functions, both in continuous and discrete correlation meth.

An understanding of the basic concepts and the terminology of linear dynamic system identification is required in order to study the identification of nonlinear dynamic systems, which is the subject of all subsequent chapters.

The purpose of this chapter is to introduce the terminology, concepts, and algorithms for linear system identification. and apply the methods of system identification. The use of the methods covered in this course and even more sophisticated methods such as finite element methods for modeling real engineering systems, even simple ones, yield only approximate results and the models must be adjusted using data obtained from the system.

• How can we use this knowledge to provide a model for the plant, the process noise, with reasonable accuracy. Lecture 12System Identification. The System Identification app enables you to identify models of dynamic systems from measured input-output data.

You can estimate both linear and nonlinear models and compare responses of different models.A model is a mathematical representation of a physical, biological or in-formation system.

Models allow us to reason about a system and make predictions about who a system will behave. In this text, we will mainly be interested in models describing the input/output behavior of systems and often in so-called \state space" form.The identification of the system consists in determining the transfer function coefficients from the value set.

As can be observed in [ 10 ], when the values and are known, then the system of equations given in [ 10, equation 14] can be solved and therefore the interval plant is identified (see [ 10 ] for details).