Distributed H∞ state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case

Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises

Extended Kalman filtering for stochastic nonlinear systems with randomly occurring cyber attacks

In this paper, we review briefly some methods for minimizing a functionF(x), which proceed by follwoing the solution curve of a system of ordinary differential equations. Such methods have often been thought to be unacceptably expensive; but we show, by means of extensive numerical tests, using a variety of algorithms, that the ODE approach can in fact be implemented in such a way as to be more than competitive with currently available conventional techniques.

Some effective methods for unconstrained optimization based on the solution of systems of ordinary differential equations

The Armijo and Goldstein step-size rules are modified to allow steps along a curvilinear path of the formx(α) + x + αs + α2d, wherex is the current estimate of the minimum,s is a descent direction andd is a nonascent direction of negative curvature By using directions of negative curvature when they exist, we are able to prove, under fairly mild assumptions, that the sequences of iterates produced by these algorithms converge to stationary points at which the Hessian matrix of the objective function is positive semidefinite

D.H Jacobson

Papers

A general result in stochastic optimal control of nonlinear discrete-time systems with quadratic performance criteria

A Newton-type curvilinear search method for optimization

A modified homogeneous algorithm for function minimization