PARAMETER-IDENTIFICATION IN TIDAL MODELS WITH UNCERTAIN BOUNDARIES

In this paper we consider a simultaneous state and parameter estimatio n procedure for tidal models with random inputs, which is formulated a s a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open bound aries. The hyperbolic nature of the governing dynamical equations is e xploited in order to determine the smoothed states efficiently. This e nables us to also apply the procedure to nonlinear tidal models withou t an excessive computational load. The main aspects of this paper are that the method of Chavent (Identification and System Parameter Estima tion. Proc. 5th IFAC Symp. Pergamon, Oxford, pp 85-97, 1979), used to calculate the gradient of a criterion that is to be minimized, is now embedded in a stochastic environment and that the estimation method ca n also be applied to practical, large-scale problems.