Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
  Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS)
have attracted the growing interest of researchers in various scientific and
engineering areas due to the growing need of adaptive intelligent systems to
solve the real world problems. ANN learns from scratch by adjusting the
interconnections between layers. FIS is a popular computing framework based on
the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The
advantages of a combination of ANN and FIS are obvious. There are several
approaches to integrate ANN and FIS and very often it depends on the
application. We broadly classify the integration of ANN and FIS into three
categories namely concurrent model, cooperative model and fully fused model.
This paper starts with a discussion of the features of each model and
generalize the advantages and deficiencies of each model. We further focus the
review on the different types of fused neuro-fuzzy systems and citing the
advantages and disadvantages of each model.
