Using ga-based intelligent control means to enhance human-machine interfaces

D. W. Repperger, Ling Rothrock

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A GA (genetic algorithm) search procedure was employed to explore a best set of sensory feedback parameters in designing ahuman-machine interface for improved performance. The optimization concerned two objective functions of interest, which incorporated tradeoffs between speed and accuracy in tracking. APareto-optimal front was calculated involving the two cost functions selected. This approach differs from the traditional minimum of anon-convex cost function (scalaz) describing the desired closed loop performance. Also, this methodology used a parsimonious experimental design method. By making a few runs with a limited number of subjects, a response model was first developed. This model was then simulated and a complex vector response surface was generated by the performance variables of interest. The GA seazch procedure was then used to locate the minimum of this response surface. Finally, in a post hoc experimental study to confirm that the selected design parameters were the best from the class selected, seven human subjects were evaluated at the most favorable experimental design pazameters and compared to alternative conditions.

Original languageEnglish (US)
Pages (from-to)123-140
Number of pages18
JournalIntelligent Automation and Soft Computing
Volume11
Issue number2
DOIs
StatePublished - Jan 1 2005

Fingerprint

Human-machine Interface
Intelligent Control
Intelligent control
Cost functions
Design of experiments
Genetic algorithms
Response Surface
Sensory feedback
Experimental design
Cost Function
Genetic Algorithm
Parameter Design
Closed-loop
Convex function
Design Method
Experimental Study
Objective function
Trade-offs
Optimization
Methodology

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

Cite this

@article{64010dce7cb04c87a6dd71e5bac4de46,
title = "Using ga-based intelligent control means to enhance human-machine interfaces",
abstract = "A GA (genetic algorithm) search procedure was employed to explore a best set of sensory feedback parameters in designing ahuman-machine interface for improved performance. The optimization concerned two objective functions of interest, which incorporated tradeoffs between speed and accuracy in tracking. APareto-optimal front was calculated involving the two cost functions selected. This approach differs from the traditional minimum of anon-convex cost function (scalaz) describing the desired closed loop performance. Also, this methodology used a parsimonious experimental design method. By making a few runs with a limited number of subjects, a response model was first developed. This model was then simulated and a complex vector response surface was generated by the performance variables of interest. The GA seazch procedure was then used to locate the minimum of this response surface. Finally, in a post hoc experimental study to confirm that the selected design parameters were the best from the class selected, seven human subjects were evaluated at the most favorable experimental design pazameters and compared to alternative conditions.",
author = "Repperger, {D. W.} and Ling Rothrock",
year = "2005",
month = "1",
day = "1",
doi = "10.1080/10798587.2005.10642899",
language = "English (US)",
volume = "11",
pages = "123--140",
journal = "Intelligent Automation and Soft Computing",
issn = "1079-8587",
publisher = "AutoSoft Press",
number = "2",

}

Using ga-based intelligent control means to enhance human-machine interfaces. / Repperger, D. W.; Rothrock, Ling.

In: Intelligent Automation and Soft Computing, Vol. 11, No. 2, 01.01.2005, p. 123-140.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Using ga-based intelligent control means to enhance human-machine interfaces

AU - Repperger, D. W.

AU - Rothrock, Ling

PY - 2005/1/1

Y1 - 2005/1/1

N2 - A GA (genetic algorithm) search procedure was employed to explore a best set of sensory feedback parameters in designing ahuman-machine interface for improved performance. The optimization concerned two objective functions of interest, which incorporated tradeoffs between speed and accuracy in tracking. APareto-optimal front was calculated involving the two cost functions selected. This approach differs from the traditional minimum of anon-convex cost function (scalaz) describing the desired closed loop performance. Also, this methodology used a parsimonious experimental design method. By making a few runs with a limited number of subjects, a response model was first developed. This model was then simulated and a complex vector response surface was generated by the performance variables of interest. The GA seazch procedure was then used to locate the minimum of this response surface. Finally, in a post hoc experimental study to confirm that the selected design parameters were the best from the class selected, seven human subjects were evaluated at the most favorable experimental design pazameters and compared to alternative conditions.

AB - A GA (genetic algorithm) search procedure was employed to explore a best set of sensory feedback parameters in designing ahuman-machine interface for improved performance. The optimization concerned two objective functions of interest, which incorporated tradeoffs between speed and accuracy in tracking. APareto-optimal front was calculated involving the two cost functions selected. This approach differs from the traditional minimum of anon-convex cost function (scalaz) describing the desired closed loop performance. Also, this methodology used a parsimonious experimental design method. By making a few runs with a limited number of subjects, a response model was first developed. This model was then simulated and a complex vector response surface was generated by the performance variables of interest. The GA seazch procedure was then used to locate the minimum of this response surface. Finally, in a post hoc experimental study to confirm that the selected design parameters were the best from the class selected, seven human subjects were evaluated at the most favorable experimental design pazameters and compared to alternative conditions.

UR - http://www.scopus.com/inward/record.url?scp=17444390909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=17444390909&partnerID=8YFLogxK

U2 - 10.1080/10798587.2005.10642899

DO - 10.1080/10798587.2005.10642899

M3 - Article

VL - 11

SP - 123

EP - 140

JO - Intelligent Automation and Soft Computing

JF - Intelligent Automation and Soft Computing

SN - 1079-8587

IS - 2

ER -