FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1934206655> ?p ?o ?g. }

• W1934206655 abstract "System identification is the art of modelling of a process (physical, biological,etc.) or to predict its behaviour or output when the environment conditionor parameter changes. One is modelling the input-output relationship of a system,for example, linking temperature of a greenhouse (output) to the sunlight intensity(input), power of a car engine (output) with fuel injection rate (input). In linearsystems, changing an input parameter will result in a proportional increase in thesystem output. This is not the case in a nonlinear system. Linear system identificationhas been extensively studied, more so than nonlinear system identification.Since most systems are nonlinear to some extent, there is significant interest in thistopic as industrial processes become more and more complex.In a linear dynamical system, knowing the impulse response function of asystem will allow one to predict the output given any input. For nonlinear systemsthis is not the case. If advanced theory is not available, it is possible to approximatea nonlinear system by a linear one. One tool is the Best Linear Approximation(Bla), which is an impulse response function of a linear system that minimises theoutput differences between its nonlinear counterparts for a given class of input. TheBla is often the starting point for modelling a nonlinear system. There is extensiveliterature on the Bla obtained from input signals with a Gaussian probabilitydensity function (p.d.f.), but there has been very little for other kinds of inputs.A Bla estimated from Gaussian inputs is useful in decoupling the linear dynamicsfrom the nonlinearity, and in initialisation of parameterised models. As Gaussianinputs are not always practical to be introduced as excitations, it is important toinvestigate the dependence of the Bla on the amplitude distribution in more detail.This thesis studies the behaviour of the Bla with regards to other types of signals,and in particular, binary sequences where a signal takes only two levels. Such aninput is valuable in many practical situations, for example where the input actuatoris a switch or a valve and hence can only be turned either on or off.While it is known in the literature that the Bla depends on the amplitudedistribution of the input, as far as the author is aware, there is a lack of comprehensivetheoretical study on this topic. In this thesis, the Blas of discrete-timetime-invariant nonlinear systems are studied theoretically for white inputs with an arbitrary amplitude distribution, including Gaussian and binary sequences. In doingso, the thesis offers answers to fundamental questions of interest to system engineers,for example: 1) How the amplitude distribution of the input and the systemdynamics affect the Bla? 2) How does one quantify the difference between theBla obtained from a Gaussian input and that obtained from an arbitrary input?3) Is the difference (if any) negligible? 4) What can be done in terms of experimentdesign to minimise such difference?To answer these questions, the theoretical expressions for the Bla have beendeveloped for both Wiener-Hammerstein (Wh) systems and the more general Volterrasystems. The theory for the Wh case has been verified by simulation and physicalexperiments in Chapter 3 and Chapter 6 respectively. It is shown in Chapter 3that the difference between the Gaussian and non-Gaussian Bla’s depends on thesystem memory as well as the higher order moments of the non-Gaussian input.To quantify this difference, a measure called the Discrepancy Factor—a measure ofrelative error, was developed. It has been shown that when the system memory isshort, the discrepancy can be as high as 44.4%, which is not negligible. This justifiesthe need for a method to decrease such discrepancy. One method is to design a randommultilevel sequence for Gaussianity with respect to its higher order moments,and this is discussed in Chapter 5.When estimating the Bla even in the absence of environment and measurementnoise, the nonlinearity inevitably introduces nonlinear distortions—deviationsfrom the Bla specific to the realisation of input used. This also explains why morethan one realisation of input and averaging is required to obtain a good estimate ofthe Bla. It is observed that with a specific class of pseudorandom binary sequence(Prbs), called the maximum length binary sequence (Mlbs or the m-sequence), thenonlinear distortions appear structured in the time domain. Chapter 4 illustratesa simple and computationally inexpensive method to take advantage this structureto obtain better estimates of the Bla—by replacing mean averaging by medianaveraging.Lastly, Chapters 7 and 8 document two independent benchmark studies separatefrom the main theoretical work of the thesis. The benchmark in Chapter 7 isconcerned with the modelling of an electrical Wh system proposed in a special sessionof the 15th International Federation of Automatic Control (Ifac) Symposium onSystem Identification (Sysid) 2009 (Schoukens, Suykens & Ljung, 2009). Chapter 8is concerned with the modelling of a ‘hyperfast’ Peltier cooling system first proposedin the U.K. Automatic Control Council (Ukacc) International Conferenceon Control, 2010 (Control 2010)." @default.
• W1934206655 created "2016-06-24" @default.
• W1934206655 creator A5037508947 @default.
• W1934206655 date "2013-01-01" @default.
• W1934206655 modified "2023-09-26" @default.
• W1934206655 title "Study of the best linear approximation of nonlinear systems with arbitrary inputs" @default.
• W1934206655 cites W1479947343 @default.
• W1934206655 cites W1485114642 @default.
• W1934206655 cites W1553786812 @default.
• W1934206655 cites W1553887387 @default.
• W1934206655 cites W1573643143 @default.
• W1934206655 cites W1583488702 @default.
• W1934206655 cites W1964717036 @default.
• W1934206655 cites W1965324089 @default.
• W1934206655 cites W1974349551 @default.
• W1934206655 cites W1977880122 @default.
• W1934206655 cites W1979845339 @default.
• W1934206655 cites W1980201978 @default.
• W1934206655 cites W1983888154 @default.
• W1934206655 cites W1985691328 @default.
• W1934206655 cites W1995569434 @default.
• W1934206655 cites W1998095566 @default.
• W1934206655 cites W2002402416 @default.
• W1934206655 cites W2009890895 @default.
• W1934206655 cites W2019020850 @default.
• W1934206655 cites W2023109623 @default.
• W1934206655 cites W2027681971 @default.
• W1934206655 cites W2029390222 @default.
• W1934206655 cites W2034319233 @default.
• W1934206655 cites W2039535251 @default.
• W1934206655 cites W2041030128 @default.
• W1934206655 cites W2041069153 @default.
• W1934206655 cites W2043508111 @default.
• W1934206655 cites W2050397314 @default.
• W1934206655 cites W2064193464 @default.
• W1934206655 cites W2074467411 @default.
• W1934206655 cites W2074878962 @default.
• W1934206655 cites W2075273870 @default.
• W1934206655 cites W2078127574 @default.
• W1934206655 cites W2088079069 @default.
• W1934206655 cites W2106644107 @default.
• W1934206655 cites W2110435534 @default.
• W1934206655 cites W2114096275 @default.
• W1934206655 cites W2114678423 @default.
• W1934206655 cites W2117713663 @default.
• W1934206655 cites W2117851648 @default.
• W1934206655 cites W2133087726 @default.
• W1934206655 cites W2137666560 @default.
• W1934206655 cites W2137720674 @default.
• W1934206655 cites W2151383158 @default.
• W1934206655 cites W2152649662 @default.
• W1934206655 cites W2154265253 @default.
• W1934206655 cites W2159806329 @default.
• W1934206655 cites W2168884607 @default.
• W1934206655 cites W2171372399 @default.
• W1934206655 cites W2298088669 @default.
• W1934206655 cites W2333631137 @default.
• W1934206655 cites W2543977995 @default.
• W1934206655 cites W2595917337 @default.
• W1934206655 cites W2599909283 @default.
• W1934206655 cites W2994876357 @default.
• W1934206655 cites W3136837693 @default.
• W1934206655 cites W32284682 @default.
• W1934206655 cites W587742752 @default.
• W1934206655 cites W70967002 @default.
• W1934206655 cites W939280201 @default.
• W1934206655 cites W1525682647 @default.
• W1934206655 hasPublicationYear "2013" @default.
• W1934206655 type Work @default.
• W1934206655 sameAs 1934206655 @default.
• W1934206655 citedByCount "0" @default.
• W1934206655 crossrefType "dissertation" @default.
• W1934206655 hasAuthorship W1934206655A5037508947 @default.
• W1934206655 hasConcept C105795698 @default.
• W1934206655 hasConcept C111919701 @default.
• W1934206655 hasConcept C119247159 @default.
• W1934206655 hasConcept C121332964 @default.
• W1934206655 hasConcept C127413603 @default.
• W1934206655 hasConcept C134306372 @default.
• W1934206655 hasConcept C14036430 @default.
• W1934206655 hasConcept C154945302 @default.
• W1934206655 hasConcept C158622935 @default.
• W1934206655 hasConcept C163175372 @default.
• W1934206655 hasConcept C163716315 @default.
• W1934206655 hasConcept C196697905 @default.
• W1934206655 hasConcept C22157029 @default.
• W1934206655 hasConcept C22762622 @default.
• W1934206655 hasConcept C24326235 @default.
• W1934206655 hasConcept C2775924081 @default.
• W1934206655 hasConcept C28826006 @default.
• W1934206655 hasConcept C33923547 @default.
• W1934206655 hasConcept C41008148 @default.
• W1934206655 hasConcept C47446073 @default.
• W1934206655 hasConcept C62520636 @default.
• W1934206655 hasConcept C67186912 @default.
• W1934206655 hasConcept C6802819 @default.
• W1934206655 hasConcept C72279823 @default.
• W1934206655 hasConcept C77088390 @default.
• W1934206655 hasConcept C78458016 @default.
• W1934206655 hasConcept C86803240 @default.

• next