The State of the State
Friday, November 15, 1996 - 9:30am - 10:30am
Eric Metois (Massachusetts Institute of Technology)
Research in nonlinear dynamics in the last decade has led to a number of much more broadly applicable techniques for inferring the underlying unknown state of a system from accessible observables, and then building predictive models in the recovered state-space. I will discuss the relationship between state reconstruction and signal separation by time-delay embedding, and estimation by conventional linear filters. I will then look at how nonlinear dissipative entrainment can be applied to state estimation in coding problems, and the connection to recursive estimation. The talk will close with a description of Cluster-Weighted Modeling, a new framework for the associated inference problem for nonlinear stochastic data in high-dimensional spaces.