&lt;title&gt;Clustering, simulation, and neural networks in real-world applications&lt;/title&gt;

Padgett, Mary Lou; Josephson, Eleanor M.; White, Charles R.; Duffield, Don W.

doi:10.1117/12.205160

Cited by 3 publications

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“…Interestingly enough, one ofthe more outstanding performance measures was identified this way. The original missile simulation [5] automatically scaled one of the output variables to show perturbations clearly. This automatically selected range stayed nearly the same for all the original simulations.…”

Section: Missile System Guance and Control Simulation Illustrationsmentioning

confidence: 99%

“…For now, it can be observed from Albisser's simulation [8,5] that the setpoint for an ideal patient is modeled over a long period of time by perturbing a sinusoidal function. Based on experience, this is reasonable, as patient moves from sick to well, active to inactive, stress levels change, and so forth.…”

Section: Missile System Guance and Control Simulation Illustrationsmentioning

confidence: 99%

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<title>Real-world applications of computational intelligence in aerospace sensing and controls: sparse, noisy, and subjective data</title>

Padgett

1996

SPIE Proceedings

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Many real-world problems in aerospace sensing and controls can be approached by adding to traditional analytic techniques more information found in sparse, noisy and subjective data. Such data frequently plays a part in the modeling methodology. but formalizing its role can lead to better control and documentation ofthe system, extending its future. The combination of computational intelligence (CI) tools with the traditional system can provide techniques for appropriate generalization (or lack of it) from sparse data. Noise to be modeled or interpreted may not obviously follow a familiar distribution such as uniform, exponential. Gaussian. Raleigh, Poisson or Weibull. Many subjective decisions are made in implementing challenging data as one of the familiar distributions or in determining an appropriate empirical distribution. These problems can be successfully addressed by careful combination of artificial neural systems, fuzzy or soft systems and evolutionary systems. Recommended methodology is illustrated by examples from missile system guidance and control simulations. Expert interpretation of problem scenarios is recorded and timed to provide direction for project extensions and enhanced data visualization. Commercial applications ofthese methodologies to aerospace industry decision support systems and to biomedical control applications is discussed and illustrated.

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Section: Missile System Guance and Control Simulation Illustrationsmentioning

confidence: 99%

Section: Missile System Guance and Control Simulation Illustrationsmentioning

confidence: 99%

<title>Real-world applications of computational intelligence in aerospace sensing and controls: sparse, noisy, and subjective data</title>

Padgett

1996

SPIE Proceedings

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A practical filter for conflicting or subjective data

Padgett

Proceedings of Third Australian and New Zealand Conference on Intelligent Information Systems. ANZIIS-95

<title>Clustering, simulation, and neural networks in real-world applications</title>

Cited by 3 publications

References 0 publications

<title>Real-world applications of computational intelligence in aerospace sensing and controls: sparse, noisy, and subjective data</title>

<title>Real-world applications of computational intelligence in aerospace sensing and controls: sparse, noisy, and subjective data</title>

A practical filter for conflicting or subjective data

Towards self organizing cache memories using neural networks

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