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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2002

Covariance Propagation and Partial Eigendecomposition Filtering on the Continuum: The Cases of Advective Dynamics

As a motivation for this lecture, we begin by stating a paradox that challenges our fundamental understanding of covariance evolution (at least it challenged my own). Attempting to resolve this 'divergence paradox' leads us to introduce the continuum fundamental solution operator for the dynamics under consideration, which will be advection dynamics in this lecture. This operator is the object that is approximated by the discrete 'tangent linear model. We then show how the fundamental solution operator can be used to describe the solution of the continuum covariance evolution equation. This description is complete enough to resolve fully the divergence paradox.

Cohn, S.↗
NASA NTRS1999

Optimized System Identification

In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.

Juang, Jer-Nan↗