NEURAL-NETWORK DESIGNS FOR PARTIALLY KNOWN DYNAMIC-SYSTEMS

被引：1

作者：

MISTRY, SI

NAIR, SS

机构：

[1] Mechanical and Aerospace Engineering, University of Missouri-Columbia, Columbia, MO

来源：

JOURNAL OF ENGINEERING FOR INDUSTRY-TRANSACTIONS OF THE ASME | 1994年 / 116卷 / 03期

关键词：

D O I：

10.1115/1.2901960

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 [机械工程];

摘要：

Algorithms are investigated for system identification and control using neural networks and validated using on-line hardware implementation. Such algorithms require very little knowledge about the system which, together with their capability of learning, make them attractive for the modeling and control of nonlinear partially known dynamic systems. An implementation architecture for neural dynamic back propagation suitable for application to other machine tools and manufacturing processes, and a network training scheme with more general features are proposed. © 1994 by ASME.

引用

页码：407 / 409

页数：3

共 6 条

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[2]

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