Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control

Jeen Shing Wang, C. S.George Lee, Junku Yuh

研究成果: Conference article同行評審

21 引文 斯高帕斯(Scopus)

摘要

This paper presents a systematic approach for developing a concise self-adaptive neuro-fuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning the control knowledge for autonomous underwater vehicles (AUV). The multi-layered structure of SANFIS incorporates fuzzy basis functions for better function approximations. Based on the need of different applications, we investigate three SANFIS structures with three different types of fuzzy IF-THEN-rule-based models and cast the rule formation problem as a clustering problem. A recursive least squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the learning process. Thus, incorporating an on-line clustering technique, a fast hybrid learning procedure and rule examination, the SANFIS is capable of self-organizing and self-adapting its internal structure for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.

原文English
頁(從 - 到)3861-3866
頁數6
期刊Proceedings - IEEE International Conference on Robotics and Automation
4
出版狀態Published - 2000 十二月 3
事件ICRA 2000: IEEE International Conference on Robotics and Automation - San Francisco, CA, USA
持續時間: 2000 四月 242000 四月 28

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 人工智慧
  • 電氣與電子工程

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