Hybrid Computer Methods for Direct Functional Optimization

Andrews, Michael; Korn, Granino A.

doi:10.1109/t-c.1975.224101

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Optimization Technique2

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1978

1982

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Cited by 2 publications

(2 citation statements)

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“…The unknown parameters are perturbed in the following manner: (5) where \(j+ 1) is the new value of the /th parameter; X*is the "best-to-date" value of the/th parameter, that is, the value of X, when the minimum-to-date value of the / vector was calculated; and 6X/G/+ 1) is the random perturbation for the /th parameter. This is equivalent to the perturbation scheme shown in the following.…”

Section: Optimization Techniquementioning

confidence: 99%

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Optimal Digital Simulation of Aircraft via Random Search Techniques

1978

Journal of Guidance, Control, and Dynamics

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This paper discusses a technique for the development of a discrete time integration operator to be used in the simulation process. The integration operator can be optimized for a particular system subjected to a set of specified inputs. The class of systems being investigated are those which can be represented by a set of state equations. A discrete time integration operator with certain free parameters is hypothesized. An adaptive random search optimization (ARSO) technique is used to find the optimum values for these parameters. Examples are presented to show the effectiveness of this technique.

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Section: Optimization Techniquementioning

confidence: 99%

“…Stability considerations may place constraints on the parameter calculated in Eq. (5). If a particular perturbation places a value outside its limit for stability, the value may be moved deterministically inside the limit or another random perturbation may be tried.…”

Section: Optimization Techniquementioning

confidence: 99%