Optimization of space structures by neural dynamics

Hojjat Adeli, Hyo Seon Park

Research output: Contribution to journalArticle

97 Citations (Scopus)

Abstract

A nonlinear neural dynamics model is presented as a new structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. A pseudo-objective function is formulated for the optimization problem in the form of a Lyapunov function to ensure the global convergence and the stability of the neural dynamic system by adopting an exterior penalty function method. The topology of the neural dynamics model consists of one variable layer and multi-constraint layers. The number of constraint layers corresponds to the number of loading conditions in the structural optimization problem. Design sensitivity coefficients calculated by the adjoint variable method are included in the inhibitory connections from the constraint layers to the variable layer. Optimum weights and design solutions are presented for four example structures and compared with those reported in the literature.

Original languageEnglish
Pages (from-to)769-781
Number of pages13
JournalNeural Networks
Volume8
Issue number5
DOIs
Publication statusPublished - 1995 Jan 1

Fingerprint

Trusses
Structural optimization
Weights and Measures
Nonlinear Dynamics
Dynamic models
Lyapunov functions
Dynamical systems
Topology

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Adeli, Hojjat ; Park, Hyo Seon. / Optimization of space structures by neural dynamics. In: Neural Networks. 1995 ; Vol. 8, No. 5. pp. 769-781.
@article{604504d145d643da87a0829aff6d0c12,
title = "Optimization of space structures by neural dynamics",
abstract = "A nonlinear neural dynamics model is presented as a new structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. A pseudo-objective function is formulated for the optimization problem in the form of a Lyapunov function to ensure the global convergence and the stability of the neural dynamic system by adopting an exterior penalty function method. The topology of the neural dynamics model consists of one variable layer and multi-constraint layers. The number of constraint layers corresponds to the number of loading conditions in the structural optimization problem. Design sensitivity coefficients calculated by the adjoint variable method are included in the inhibitory connections from the constraint layers to the variable layer. Optimum weights and design solutions are presented for four example structures and compared with those reported in the literature.",
author = "Hojjat Adeli and Park, {Hyo Seon}",
year = "1995",
month = "1",
day = "1",
doi = "10.1016/0893-6080(95)00026-V",
language = "English",
volume = "8",
pages = "769--781",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Limited",
number = "5",

}

Optimization of space structures by neural dynamics. / Adeli, Hojjat; Park, Hyo Seon.

In: Neural Networks, Vol. 8, No. 5, 01.01.1995, p. 769-781.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimization of space structures by neural dynamics

AU - Adeli, Hojjat

AU - Park, Hyo Seon

PY - 1995/1/1

Y1 - 1995/1/1

N2 - A nonlinear neural dynamics model is presented as a new structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. A pseudo-objective function is formulated for the optimization problem in the form of a Lyapunov function to ensure the global convergence and the stability of the neural dynamic system by adopting an exterior penalty function method. The topology of the neural dynamics model consists of one variable layer and multi-constraint layers. The number of constraint layers corresponds to the number of loading conditions in the structural optimization problem. Design sensitivity coefficients calculated by the adjoint variable method are included in the inhibitory connections from the constraint layers to the variable layer. Optimum weights and design solutions are presented for four example structures and compared with those reported in the literature.

AB - A nonlinear neural dynamics model is presented as a new structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. A pseudo-objective function is formulated for the optimization problem in the form of a Lyapunov function to ensure the global convergence and the stability of the neural dynamic system by adopting an exterior penalty function method. The topology of the neural dynamics model consists of one variable layer and multi-constraint layers. The number of constraint layers corresponds to the number of loading conditions in the structural optimization problem. Design sensitivity coefficients calculated by the adjoint variable method are included in the inhibitory connections from the constraint layers to the variable layer. Optimum weights and design solutions are presented for four example structures and compared with those reported in the literature.

UR - http://www.scopus.com/inward/record.url?scp=0028866718&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028866718&partnerID=8YFLogxK

U2 - 10.1016/0893-6080(95)00026-V

DO - 10.1016/0893-6080(95)00026-V

M3 - Article

VL - 8

SP - 769

EP - 781

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

IS - 5

ER -