Abstract
This paper considers the problem of identifying unknown parameters appearing in discrete time-varying bilinear models. The system is assumed to operate in a stochastic environment, where the input disturbance and the noise observation have unknown probability distribution. Identifiability conditions are investigated in the light of a state covariance analysis for stochastic bilinear systems. By assuming that the only accessible process is supplied by the noisy observation sequence, a recursive identification procedure is proposed which is suitable for on-line applications. Stochastic approximation algorithms are used for identifying time-invariant bilinear systems.
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