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A modified response surface methodol...

Schamburg, Jeffrey B.

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A modified response surface methodology for knowledge discovery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A modified response surface methodology for knowledge discovery./
作者:	Schamburg, Jeffrey B.
面頁冊數:	218 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2072.
Contained By:	Dissertation Abstracts International65-04B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3131400
ISBN:	0496786410

A modified response surface methodology for knowledge discovery.
Schamburg, Jeffrey B.

A modified response surface methodology for knowledge discovery. - 218 p.

Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2072.

Thesis (Ph.D.)--University of Virginia, 2004.

The response surface methodology (RSM) provides an iterative process for learning that involves the sequential use of experimental design, empirical model building, and analysis of the developed models. The traditional response surface methodology (TRSM) is commonly applied to optimization or mixture problems involving a limited number of variables. The approach is usually straightforward and single focused. A modified response surface methodology (MRSM) has been developed in this research. The MRSM can be applied to more complex systems engineering problems. These problems involve a larger number of input variables, multiple measures of performance, and complex systems relationships. Furthermore, the MRSM considers systems analyses that have multiple objectives. These may include: (1) determining near optimal solutions; (2) understanding the tradeoffs among important variables in the study; and (3) translating the findings into generalizations for operational procedures. In many of these applications, one may begin the analysis with relatively little understanding of the variable relationships in the systems under study. The MRSM capitalizes on the underlying learning philosophy of TRSM while benefiting from other knowledge discovery concepts and data mining techniques.

ISBN: 0496786410Subjects--Topical Terms:

1018128
Engineering, System Science.

A modified response surface methodology for knowledge discovery.
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500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2072.
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502 $a Thesis (Ph.D.)--University of Virginia, 2004.
520 $a The response surface methodology (RSM) provides an iterative process for learning that involves the sequential use of experimental design, empirical model building, and analysis of the developed models. The traditional response surface methodology (TRSM) is commonly applied to optimization or mixture problems involving a limited number of variables. The approach is usually straightforward and single focused. A modified response surface methodology (MRSM) has been developed in this research. The MRSM can be applied to more complex systems engineering problems. These problems involve a larger number of input variables, multiple measures of performance, and complex systems relationships. Furthermore, the MRSM considers systems analyses that have multiple objectives. These may include: (1) determining near optimal solutions; (2) understanding the tradeoffs among important variables in the study; and (3) translating the findings into generalizations for operational procedures. In many of these applications, one may begin the analysis with relatively little understanding of the variable relationships in the systems under study. The MRSM capitalizes on the underlying learning philosophy of TRSM while benefiting from other knowledge discovery concepts and data mining techniques.
520 $a As an example, the MRSM may be applied to analysis involving agent-based simulation models. Agent-based simulation models have become increasingly used in a variety of domains. These models offer great potential but their use can cause difficulties for systems engineers. Analysis problems involving agent-based simulations may include a large number of potential input variables and rules, multiple performance measures of interest, and complex relationships. The application of the MRSM in this research involves the use of a military combat agent-based simulation model. Currently, combat analysis of future military information technologies involves human-in-the-loop simulations. These experiments are time consuming and resource intensive. With agent-based simulations, thousands of experiments can be conducted without humans-in-the-loop. However, until this point, an appropriate learning methodology for using the results from these experiments did not exist. The MRSM developed in this research addresses this military application. However, the approach is a general one and will be useful in other applications areas such as industrial engineering, economics, law enforcement, and others.
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