Identification of noisy distributed parameter systems using stochastic approximation

The parameter identification problem in systems governed by partial differential equations is investigated. Stochastic approximation algorithms are applied for identifying a class of distributed systems driven by random inputs and observed through noisy measurements. No restrictions about the probability distributions are imposed. These algorithms converge with probability one, and are suitable for on-line applications. The proposed identification method assumes that a previous system classification has been performed, such that the model to be identified is known up to a set of space-varying parameters, where extraneous terms may be included. The very real case of noisy measurements taken at a limited number of discrete points Located in the spatial domain is considered.