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This paper focuses on the inverse problem of predicting inputs from measured outputs in the context of linear systems in steady-state. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can likely improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. Topics for further research are also suggested in this paper. [ABSTRACT FROM AUTHOR]

This paper explores the issue of model misspecification, or bias, in the context of response surface design problems involving quantitative and qualitative factors. New designs are proposed specifically to address bias and compared with five types of alternatives ranging from types of composite to D-optimal designs using four criteria including D-efficiency and measured accuracy on test problems. Findings include that certain designs from the literature are expected to cause prediction errors that practitioners would likely find unacceptable. A case study relating to the selection of science, technology, engineering, or mathematics majors by college students confirms that the expected substantial improvements in prediction accuracy using the proposed designs can be realized in relevant situations. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When experimentation on a real system is expensive, data are often collected by using cheaper, lower fidelity surrogate systems. The paper concerns response surface methods in the context of variable fidelity experimentation. We propose the use of generalized least squares to generate the predictions. We also present perhaps the first optimal designs for variable fidelity experimentation, using an extension of the expected integrated mean-squared error criterion. Numerical tests are used to compare the performance of the method with alternatives and to investigate the robustness to incorporated assumptions. The method is applied to automotive engine valve heat treatment process design in which real world data were mixed with data from two types of computer simulation. [ABSTRACT FROM AUTHOR]

Provides comparisons of the accuracy of meta-models derived from computer experiments. Use of alternative approaches to experimentation and analysis; Interaction between relative meta-model accuracy and the choice of experimental design; Design of a plastic seal for an automotive application.

Proposes several supersaturated designs and associated analysis methods to identify active factors in situations in which only a very limited number of experimental runs is available. Proposed design criteria; Performance of existing designs from the supersaturated design literature; Comparison of the proposed designs with alternatives using additional simulation studies.

Describes a case study based instruction approach designed to reinforce lecture material. Increase in motivation of students to learn; Application of statistical process control to engineering contexts; Use of printed circuit boards in case study exercise.

We propose ‘low-cost response surface methods’ (LCRSMs) that typically require half the experimental runs of standard response surface methods based on central composite and Box Behnken designs, but yield comparable or lower modeling errors under realistic assumptions. In addition, the LCRSMs have substantially lower modeling errors and greater expected savings compared with alternatives with comparable numbers of runs, including small composite designs and computer-generated designs based on popular criteria such as D-optimality. The LCRSM procedures appear to be the first experimental design methods derived as the solution to a simulation optimization problem. Together with modern computers, simulation optimization offers unprecedented opportunities for applying clear, realistic multicriterion objectives and assumptions to produce useful experimental design methods. We compare the proposed LCRSMs with alternatives based on six criteria. We conclude that the proposed methods offer attractive alternatives when the experimenter is considering dropping factors to use standard response surface methods or would like to perform relatively few runs and stop with a second-order model. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]