Identification of nonlinear systems using random amplitude poisson distributed input functions

Yu Te Wu*, Robert J. Sclabassi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper, nonlinear system identification using a doubly random input function which is a Poisson train of events with random amplitudes as a system input is investigated. These doubly random input functions are useful for identifying systems that naturally require amplitude modulated point process inputs as stimuli such as the hippocampal formation in the central neurous system. This is an extension of earlier work in which a Poisson train of events with only constant amplitude was used as the input for system identification. Analogous to the Wiener theory, we have developed both continuous and discrete functionals up to second-order for this doubly random input function. Closed form solutions for the diagonal terms of the second-order kernels in both cases have been obtained and convergence properties are demonstrated. Two hypothetical discrete second-order nonlinear systems are illustrated and one of them was simulated to test the theory presented. Discrete kernels computed from the simulated data agree with the theoretical prediction.

Original languageEnglish
Pages (from-to)222-234
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
Volume27
Issue number2
DOIs
StatePublished - 1997

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