Structural modeling and control using neural networks

Jyh Ching Juang, Chi Yuan Chiang, Hussein M. Youssef

Research output: Contribution to journalConference articlepeer-review


A context sensitive neural network is devised for structural system modeling and control. This neural networks attempts to blend existing structural modeling formulation into the neural network framework. One innovation of the neural network is to represent each structural vibration mode using a neuron. Thus, when the neural system converges, the modal data are identified. Another neural network paradigm that is identified for structural modeling is the adaptive vector quantization technique. It is shown that this learning scheme is able to resolve closely-spaced modes for large space structures. The context sensitive neural networks also allow the injection of robust control design in the context of neural network.

Original languageEnglish
Pages (from-to)1608-1618
Number of pages11
JournalCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Publication statusPublished - 1994 Dec 1
EventProceedings of the 35th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Part 1 (of 5) - Hilton Head, SC, USA
Duration: 1994 Apr 181994 Apr 20

All Science Journal Classification (ASJC) codes

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering


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