A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control

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

Research output: Contribution to journalArticlepeer-review

82 Scopus citations


This study presents a functional-link-based neurofuzzy 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. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
Original languageEnglish
Pages (from-to)1362-1378
Number of pages17
JournalIEEE Transactions on Fuzzy Systems
Issue number5
StatePublished - Oct 2008


  • Entropy
  • functional link neural networks (FLNNs)
  • neurofuzzy networks (NFNs)
  • nonlinear system control
  • Online Learning

Fingerprint Dive into the research topics of 'A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control'. Together they form a unique fingerprint.

Cite this