• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Informatics in Control, Automation and Robotics >Implementation of evolving fuzzy models of a nonlinear process
【24h】

Implementation of evolving fuzzy models of a nonlinear process

机译：非线性过程演化模糊模型的实现

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents details on the implementation of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of a nonlinear process represented by the pendulum dynamics in the framework of the representative pendulum-crane systems. The pendulum angle is the output variable of the TSK fuzzy models that are obtained by online identification. The rule bases and the parameters of the TSK fuzzy models are continuously evolved by an online identification algorithm (OIA) that adds new rules with more summarization power and modifies the existing rules and parameters. The OIA is associated with an input selection algorithm that guides the modelling in terms of ranking the inputs according to their importance factors. Three TSK fuzzy models evolved by the OIA are exemplified. The performance of the new evolving TSK fuzzy models is illustrated by experimental results conducted on pendulum-crane laboratory equipment.
机译：本文详细介绍了在有代表性的钟摆系统框架下，以摆线动力学为代表的非线性过程的发展的Takagi-Sugeno-Kang（TSK）模糊模型的实现。摆角是通过在线识别获得的TSK模糊模型的输出变量。通过在线识别算法（OIA）不断完善TSK模糊模型的规则库和参数，该算法添加了具有更强摘要能力的新规则并修改了现有规则和参数。 OIA与输入选择算法相关联，该算法根据输入的重要性因子对输入进行排序，从而指导建模。举例说明了由OIA演化的三个TSK模糊模型。在摆式起重机实验室设备上进行的实验结果说明了新发展的TSK模糊模型的性能。

著录项

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