Maximum Likelihood Estimation of Hammerstein Models
Independent thesis Basic level (professional degree)Student thesisAlternative title
Maximum Likelihood-metoden för identifierig av Hammersteinmodeller (Swedish)
In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated.
Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input.
Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.
Place, publisher, year, edition, pages
Institutionen för systemteknik , 2003. , 70 p.
Datorteknik, Hammerstein models, nonlinear dynamic systems, system identification, Maximum Likelihood, parametric identification methods.
IdentifiersURN: urn:nbn:se:liu:diva-2061OAI: oai:DiVA.org:liu-2061DiVA: diva2:19390