Title :
Estimates of performance sensitivity of a stochastic system
Author_Institution :
Div. of Appl. Sci., Harvard Univ., Cambridge, MA, USA
fDate :
9/1/1989 12:00:00 AM
Abstract :
Three kinds of estimates of the performance sensitivity of a stochastic system are discussed. The convergence properties of these estimates are investigated. The first estimate, using the time average of the derivative of the performance function calculated along a sample trajectory, is generally preferable when certain conditions hold for the performance function. The variance of the second estimate, using the same input random process, is much less than that of the third estimate, which uses tow different input processes. An example of a one-dimensional linear system with a quadratic performance function is given; it illustrates the general approach to verifying the conditions related to the first estimate for linear systems
Keywords :
convergence; information theory; parameter estimation; sensitivity analysis; stochastic processes; convergence properties; input processes; one-dimensional linear system; performance function; performance sensitivity estimation; quadratic performance function; random process; stochastic system; Convergence; Equations; Linear systems; Monte Carlo methods; Optimization methods; Random processes; Random variables; Stochastic processes; Stochastic systems; Time measurement;
Journal_Title :
Information Theory, IEEE Transactions on
