An Equilibrium Analysis of Defensive Response to Entry Using a Coupled Response Function Model

1992 ◽  
Vol 11 (4) ◽  
pp. 348-358 ◽  
Author(s):  
Thomas S. Gruca ◽  
K. Ravi Kumar ◽  
D. Sudharshan
Keyword(s):  
Response Function ◽  
Equilibrium Analysis ◽  
Defensive Response ◽  
Function Model ◽  
Coupled Response

Microwave Measurement of the Solid-Propellant Pressure-Coupled Response Function

1980 ◽  
Vol 17 (6) ◽  
pp. 483-488 ◽  
Author(s):  
L.D. Strand ◽  
K.R. Magiawala ◽  
R.P. McNamara
Keyword(s):  
Response Function ◽  
Solid Propellant ◽  
Microwave Measurement ◽  
Coupled Response

The AgroEcoSystem (AgES) Response-Function Model Simulates Layered Soil-Water Dynamics in Semiarid Colorado: Sensitivity and Calibration

2015 ◽  
Vol 14 (8) ◽  
pp. vzj2014.09.0119 ◽  
Author(s):  
Timothy R. Green ◽  
Robert H. Erskine ◽  
Michael L. Coleman ◽  
Olaf David ◽  
James C. Ascough ◽  
...  
Keyword(s):  
Soil Water ◽  
Response Function ◽  
Layered Soil ◽  
Water Dynamics ◽  
Soil Water Dynamics ◽  
Function Model

Alternatives of Taguchi's Approach for Dynamic Robust Design Problems

1998 ◽  
Vol 05 (02) ◽  
pp. 115-131 ◽  
Author(s):  
Kwok-Leung Tsui
Keyword(s):  
Response Function ◽  
Robust Design ◽  
Dynamic Responses ◽  
Response Model ◽  
Function Model ◽  
Design Problems ◽  
Loss Model ◽  
Modeling Approaches ◽  
Noise Factors ◽  
Model Approach

Robust Design is an important method for improving product quality, manufacturability, and reliability at low cost. Most research in robust design has been focused on problems with static responses. This paper deals with the robust design problems with dynamic responses. The objective of the paper is to investigate and compare three modeling approaches: the loss model, the response function model, and the response model approaches. Taguchi16 proposes the loss model approach which models the loss measures as functions of the control factor effects. Miller and Wu10 propose the response function model approach which models the loss measures as functions of the effects of both control and noise factors. Tsui18 proposes the response model approach which directly models the response as a function of the effects of control, noise, and signal factors. In this paper, we identify and derive the relationships between the effect estimates of the three approaches and show that the loss model approach creates unnecessary biases for the factorial effect estimates and may lead to non-optimal solutions. The three modeling approaches are compared in a real example.

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