• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Learning algorithms for neural networks and development of neural-network-based active vibration absorbers.
【24h】

Learning algorithms for neural networks and development of neural-network-based active vibration absorbers.

机译：神经网络的学习算法和基于神经网络的主动减振器的开发。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This thesis deals with the development of learning algorithms for recurrent and multilayer neural networks and application of neural networks to the control of vibration in rotordynamic systems. These learning algorithms are based on the concept of terminal attractors and the attractive condition used in the sliding mode control theory. Terminal attractors, which are based on the violation of the Lipschitz condition, represent singular solutions of dynamical systems. The fact that the system can reach singular solutions (or the desired solutions) in a finite time is utilized to enhance the learning rates of neural networks. The derivations of these new learning algorithms are formulated for both recurrent and multilayer neural networks. An inverse kinematic problem associated with a two-link robot manipulator is chosen as an example to verify the usefulness of new learning algorithms. Simulation results for both neural networks are presented.; A neural-network-based active vibration absorber has been developed to optimally suppress rotor vibrations caused by rotor unbalance. The unique feature of this new vibration absorber is its ability to optimally control vibration at different rotor speeds. Numerical examples dealing with a single-degree-of-freedom spring-mass system and a multi-degree-of-freedom rigid rotor supported by magnetic bearings are presented to verify the advantages of this novel neural-network-based active vibration absorber.
机译：本文研究了递归和多层神经网络学习算法的发展，以及神经网络在转子动力系统振动控制中的应用。这些学习算法基于终端吸引子的概念和滑模控制理论中使用的吸引条件。基于违反Lipschitz条件的末端吸引子表示动力系统的奇异解。系统可以在有限时间内达到奇异解（或所需解）的事实被用于提高神经网络的学习率。这些新的学习算法的派生公式化了递归和多层神经网络。以与双链接机器人操纵器相关的逆运动学问题为例，以验证新学习算法的有效性。给出了两个神经网络的仿真结果。已经开发了基于神经网络的主动减振器，以最佳地抑制由转子不平衡引起的转子振动。这种新型减震器的独特之处在于它能够在不同的转子速度下最佳地控制振动。给出了处理单自由度弹簧质量系统和磁轴承支撑的多自由度刚性转子的数值示例，以验证这种基于神经网络的新型主动减振器的优势。

著录项

  • 作者

    Ma, Rwei-Ping.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Mechanical.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1993
  • 页码 161 p.
  • 总页数 161
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;人工智能理论;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号