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.
|頁（從 - 到）||1608-1618|
|期刊||Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference|
|出版狀態||Published - 1994 十二月 1|
|事件||Proceedings of the 35th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Part 1 (of 5) - Hilton Head, SC, USA|
持續時間: 1994 四月 18 → 1994 四月 20
All Science Journal Classification (ASJC) codes