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Design and identification of the models parameters are detailed.
The identification of the models and the fault detection concept are conducted solely based on the on-line recorded data streams.
Local identification of the models is considered for 3 and 4 occasions analytically.
In DBM-PSO, two random values in the updating function are generated by the following double-bottom map function: (18) DBM r j, t + 1 = [ sin (4 π DBM r j, t ) + 1 ] 2. In this study, all methods used the same parameters to test the search ability for the identification of the models of gene-gene interaction.
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We also derive the expectation maximization (EM) algorithm for the parameter identification of the model.
Note that identification of the model X and W could be identical.
To discuss the identification of the model I follow Heuchenne (1997) and Paxton et al. (2011).
In this appendix, I discuss the identification of the model presented in Eqs.
The first is the identification of the model, where the observed series is transformed into a series stationary.
I merge the first two categories to improve identification of the model, since the first two categories are characterized by low frequencies.
In the second part, we focus on current developments concerning the identification of the model using dynamic experiments.
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identification of the ones
identification of the systems
identification of the specimens
identification of the modes
characterization of the models
identification of the samples
identification of the later
identification of the model
identities of the models
identification of the effects
identification of modalities
identify the models
identification of designs
identity of the models
identification of forms
identification of standards
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com