TY  - CHAP
AU  - Azar
AU  - Ahmad Taher
ED  - Ahmad Taher Azar
Y1  - 2010-02-01
PY  - 2010
T1  - Adaptive Neuro-Fuzzy Systems
N2  - While several books are available today that address the mathematical and philosophical foundations of fuzzy logic, none, unfortunately, provides the practicing knowledge engineer, system analyst, and project manager with specific, practical information about fuzzy system modeling. Those few books that include applications and case studies concentrate almost exclusively on engineering problems: pendulum balancing, truck backeruppers, cement kilns, antilock braking systems, image pattern recognition, and digital signal processing. Yet the application of fuzzy logic to engineering problems represents only a fraction of its real potential. As a method of encoding and using human knowledge in a form that is very close to the way experts think about difficult, complex problems, fuzzy systems provide the facilities necessary to break through the computational bottlenecks associated with traditional decision support and expert systems. Additionally, fuzzy systems provide a rich and robust method of building systems that include multiple conflicting, cooperating, and collaborating experts (a capability that generally eludes not only symbolic expert system users but analysts who have turned to such related technologies as neural networks and genetic algorithms). Yet the application of fuzzy logic in the areas of decision support, medical systems, database analysis and mining has been largely ignored by both the commercial vendors of decision support products and the knowledge engineers who use them.
BT  - Fuzzy Systems
SP  - Ch. 5
UR  - https://doi.org/10.5772/7220
DO  - 10.5772/7220
SN  - 
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-20
ER  -