Cerebral Quotient of Neuro Fuzzy Techniques – Hype or Hallelujah?

Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A neuro-fuzzy system is simply a fuzzy inference system trained by a neural network learning algorithm. The learning mechanism fine-tunes the underlying fuzzy inference system. This article starts with some basic theoretical aspects of ANN and FIS and some of the popular neuro-fuzzy modeling techniques. We further discuss some of the application areas where we have already implemented neuro-fuzzy systems. Empirical results show that neuro-fuzzy systems are efficient in terms of better performance time and lower error rates while compared to pure neural network approach.