Mathematical Systems Theory In Biology Communications Computation And Finance

Mathematical Systems Theory in Biology, Communications, Computation and Finance

Mathematical systems theory is a vibrant research area in its own right. The theory has an impact in numerous applications areas including aeronautics, biological systems, chemical engineering, communication systems, financial engineering and robotics to name just a few. This volume contains survey and research articles by some of the leading researchers in mathematical systems theory. Many authors have taken special care that their articles are self-contained and accessible also to non-specialists. The articles contained in this volume are from those presented as plenary lectures, invited one hour lectures and minisymposia at the 15th International Symposium on the Mathematical Theory of Networks and Systems held at the University of Notre Dame, August 12-16, 2002.

Directions in Mathematical Systems Theory and Optimization

For more than three decades, Anders Lindquist has delivered fundamental cont- butions to the ?elds of systems, signals and control. Throughout this period, four themes can perhaps characterize his interests: Modeling, estimation and ?ltering, feedback and robust control. His contributions to modeling include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identi?cation. His contributions to ?ltering and estimation include the development of fast ?ltering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. His further research on the phase portrait of this dynamical system gave a better understanding of when the Kalman ?lter will converge, answering an open question raised by Kalman. While still a student he established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. He continued his interest in feedback control by deriving optimal and robust control feedback laws for suppressing the effects of harmonic disturbances. Moreover, his recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.

Mathematical Systems Theory in Biology, Communications, Computation and Finance