Self-organizing decentralized fuzzy neural net controller

Zong Mu Yeh*, Hung Pin Chen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


This paper presents a self-organizing decentralized learning controller using fuzzy control and neurocontrol for large-scale nonlinear systems. A new on-line unsupervised learning method which is based on a performance index of sliding mode control is used to train the fuzzy neural net controller to obtain control actions. To overcome the interactions between the subsystems, a learning algorithm is adopted to modify the control input to improve the system performance. The effectiveness and the performance of the proposed approach are illustrated by the simulation results of a two-inverted pendulum system and a two-link manipulator. The attractive features also include a smaller residual error and robustness against nonlinear interactions.

Original languageEnglish
Number of pages8
Publication statusPublished - 1995
EventProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) - Yokohama, Jpn
Duration: 1995 Mar 201995 Mar 24


ConferenceProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5)
CityYokohama, Jpn

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Self-organizing decentralized fuzzy neural net controller'. Together they form a unique fingerprint.

Cite this