ANFIS: General description for modeling dynamic objects

M. Alakhras, M. Oussalah, M. Hussein

Research output: Contribution to journalConference articlepeer-review


This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.

Original languageEnglish
JournalProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Publication statusPublished - 2016
Event12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015 - Marrakech, Morocco
Duration: Nov 17 2015Nov 20 2015


  • Adaptive Systems (ANFIS)
  • Neuro-Fuzzy (NF), Modeling of Nonlinear Objects
  • Soft Computing (sq)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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