Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
| dc.creator | Abraham, Ajith | |
| dc.date | 2004-05-05 | |
| dc.date.accessioned | 2026-07-07T03:21:11Z | |
| dc.date.available | 2026-07-07T03:21:11Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/cs/0405011 | |
| dc.identifier | http://arxiv.org/abs/cs/0405011 | |
| dc.identifier | Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Lecture Notes in Computer Science. Volume. 2084, Springer Verlag Germany, Jose Mira and Alberto Prieto (Eds.), ISBN 3540422358, Spain, pp. 269-276, 2001 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32100 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | I.2.0 | |
| dc.title | Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques | |
| dc.type | text |