Maximum likelihood identification of nonlinear systems.
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Maximum likelihood technique 1s used to estimate the parameters of single input single output nonlinear systems. Two algorithms are described and applied . One is for the identification of Hammerstein nonlinear models, which is useful if no priori knowledge about the mathematical form of the nonlinearity is available. The other algorithm is for the identification of systems which have known forms for the nonlinearities. It is derived for continuous nonlinear systems, and applied for simulated data generated from linear and nonlinear second order continuous models. It is also used to fit linear, and nonlinear second order continuous models to practical data taken from a test on the glucose homeostatic control system of dogs. The emphasis is on obtaining simplified algorithm for continuous nonlinear systems in order to save computing time, and get satisfactory results.
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This work is available on request. You can request a copy at https://library.carleton.ca/forms/request-pdf-copy-thesis
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Copyright © 1972 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 1972
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