Beyond integrated neuro-fuzzy systems: reviews, prospects, perspectives and directions
Ajith Abraham · 2001
Neuro-fuzzy computing, which provides efficient information processing capability by devising methodologies and algorithms for modeling uncertainty and imprecise information, forms at this juncture, a key component of soft computing. An integrated neuro-fuzzy system is simply a fuzzy inference system trained by a neural networklearning algorithm. The learning mechanism fine-tunes the underlying fuzzy inference system. Integrated neurofuzzy model makes use of the synergetic and complementary features of neural networks and fuzzy inference system and in most cases better results can be obtained rather than in a stand-alone mode. This paper presents some short fundamental concepts and modeling aspects of neuro-fuzzy systems emphasizing on Takagi Sugeno and Mamdani fuzzy inference system. Some short reviews of neuro-fuzzy models that have evolved in the past few years are discussed further. The paper concludes with an attempt to throw light on future research directions and some of the problems to be addressed that go beyond the current neuro-fuzzy algorithms.