A Stochastic Algorithm for Sensitivity Analysis

A direct stochastic sensitivity analysis algorithm is developed for linear dynamical systems having incompletely known input statistics. The new algorithm extends previous results by applying covariance propagation concepts which utilize as a forcing function the sensitivity covariance matrix associated with the uncertainty in the elements of the system input covariance matrix itself. The developed algorithm is evaluated in the context of a generalized sensitivity analysis formulation involving nonlinear transformations on the input signals. Numerical results are provided to demonstrate the usefulness of the new algorithm.