Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks

Chin-Teng Lin, Cheng-Hung Chen, Cheng-Jian Lin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This study presents a functional-link-based neuro-fuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
Original languageEnglish
Title of host publicationRecent Advances in Intelligent Control Systems
PublisherSpringer London
Number of pages27
ISBN (Electronic)978-1-84882-548-2
ISBN (Print)978-1-84882-547-5
StatePublished - 2009


  • Root Mean Square Error
  • Membership Function
  • Fuzzy System
  • Fuzzy Rule
  • Fuzzy Controller

Fingerprint Dive into the research topics of 'Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks'. Together they form a unique fingerprint.

Cite this