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

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

Research output: Contribution to journalConference article

19 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)3861-3866
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume4
Publication statusPublished - 2000 Dec 3
EventICRA 2000: IEEE International Conference on Robotics and Automation - San Francisco, CA, USA
Duration: 2000 Apr 242000 Apr 28

Fingerprint

Autonomous underwater vehicles
Fuzzy inference
Fuzzy systems
Learning algorithms
Trajectories
Control systems
Data storage equipment
Computer simulation

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

@article{18d7a69366014e20bb08580df97c37c4,
title = "Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control",
abstract = "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.",
author = "Wang, {Jeen Shing} and Lee, {C. S.George} and Junku Yuh",
year = "2000",
month = "12",
day = "3",
language = "English",
volume = "4",
pages = "3861--3866",
journal = "Proceedings - IEEE International Conference on Robotics and Automation",
issn = "1050-4729",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control. / Wang, Jeen Shing; Lee, C. S.George; Yuh, Junku.

In: Proceedings - IEEE International Conference on Robotics and Automation, Vol. 4, 03.12.2000, p. 3861-3866.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control

AU - Wang, Jeen Shing

AU - Lee, C. S.George

AU - Yuh, Junku

PY - 2000/12/3

Y1 - 2000/12/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033726636&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033726636&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0033726636

VL - 4

SP - 3861

EP - 3866

JO - Proceedings - IEEE International Conference on Robotics and Automation

JF - Proceedings - IEEE International Conference on Robotics and Automation

SN - 1050-4729

ER -