Shape estimation from incomplete measurements: A neural-net approach

被引：42

作者：

Bruno, R. ^{[1
]}

Toomarian, N. ^{[1
]}

Salama, M. ^{[1
]}

机构：

[1] Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States

来源：

Smart Materials and Structures | 1994年 / 3卷 / 02期

关键词：

D O I：

10.1088/0964-1726/3/2/002

中图分类号：

TG156 [热处理工艺];

学科分类号：

摘要：

Accurate estimation of the shape of precision space structures is the key to the success of spaceborne optical systems. In this paper, a combined simulated annealing and neural-network approach is proposed whereby one can infer the current deformed state of the structure from a limited number of on-board measurements. The approach is especially effective when most of the computations must be done on-ground or off-line, and only minimal calculations are allowed for near real-time on-board processing. It is shown that the performance of the network and its ability to estimate the shape accurately is highly dependent upon the off-line training and tuning of the model to a specific family of expected disturbances. Details of the methodology and results of numerical simulations are given for various on-board estimation scenarios. © 1994 IOP Publishing Ltd.

引用

页码：92 / 97