Abstract
In this final chapter, we consider the problem of System Identification✝ which (largely because of the increasing use of high-speed large-memory digital computers) has been growing interest in recent years. As we have seen, before we can apply the theories of stochastic control, it is necessary to know the parameters characterizing the system (that is, the matrices A,B,F, etc.). In many cases, in a sense in all cases, these are not known in sufficient precision and have to be deduced from measurements made while the system is operating. The measurements are subject to error; for many purposes the errors can be modelled as additive Gaussian noise. The particular problem we shall consider here is that of identifying a linear dynamic system driven by state ‘noise’ as well as known inputs, from observed output in additive white noise. (See [41] for a direct application).
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© 1973 Springer-Verlag Berlin · Heidelberg
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Balakrishnan, A.V. (1973). System Identification. In: Stochastic Differential Systems I. Lecture Notes in Economics and Mathematical Systems, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80759-6_8
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DOI: https://doi.org/10.1007/978-3-642-80759-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-06303-2
Online ISBN: 978-3-642-80759-6
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