The changing criterion design.

Design of a Class of Multirate Systems Using a Maximum Relativè 2 -error Criterion an Example of a Rate-changing Multirate System. 1. Introduction

A criterion for designing the class of multirate systems for rate-changing is presented. This criterion arises from a model-matching perspective with maximum relativè 2-error over a general class of inputs and is a natural extension of the standard Chebyshev method for lter design. Using multirate and convex analysis techniques, the criterion is shown to lead to a convex matrix-valued-function ...

Design of a class of multirate systems

The design of rate-changing multirate systems using a maximum relative `-error criterion is analyzed. Using multirate techniques, the criterion is simpli ed to a matrix-response approximation problem. An algorithm using convex optimization is proposed to solve the problem. An example illustrates the use of the algorithm and e ectiveness over methods intended for LTI system design.

Recent Innovations in the Changing Criterion Design: Implications for Research and Practice in Special Education

In 1982, Schloss, Sedlack, Elliott, and Smothers published an article in The Journal of Special Education that illustrated “applications of the changing criterion designs to special education classrooms” (p. 361). These applications were based on the classic changing criterion (CC) research design, which Hartmann and Hall (1976) said was “initially named by Hall (1971) and illustrated by Weis a...

The changing-criterion design: assessment of running behavior of a handicapped youth.

Bayesian Optimum Design Criterion for Multi Models Discrimination

The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an extension of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where...