Impact of prescription drug diversion control systems on medical practice and patient care /

Mode of access: Internet.

Manual on installation of refinery instruments and control systems.

Mode of access: Internet.

The evaluation and application of chemically regenerative atmospheric control systems.

" Report ; no. TDR-269 (4560-50)-2)."

The air quality and economic implications of supplementary control systems in Illinois /

Bibliography: p. 90-92.

Energy monitoring and control systems (EMCS).

"January 1991."

Railroad claims (Statute of limitations) Hearing before a subcommittee of the Committee on Interstate and Foreign Commerce, House of Representatives, Seventy-first Congress, third session, on S. 571 and H.R. 3500, to amend section 204 of the act entitled "An act to provide for the termination of federal control of railroads and systems of transportation; to provide for the settlement of disputes between carriers and their employees; to further amend an act entitled 'An act to regulate commerce,' approved February 4, 1887, as amended, and for other purposes," approved February 28, 1920. February 23, 1931 ...

Carl E. Mapess, chairman of subcommittee.

Diversion and corridor control systems in Western Europe /

Preprint of IRF report, issued June 1977.

Control of aircraft and missile powerplants; an introduction to the analysis and design of engine control systems

Mode of access: Internet.

A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.