Neural Networks and Fuzzy Systems [electronic resource] : Theory and Applications /

Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial optimization problems, multilayered neural networks for pattern classification and function approximation, fuzzy systems that have the same functions as multilayered networks, and composite systems that have been successfully applied to real world problems. The author also includes representative neural network models such as the Kohonen network and radial basis function network. New fuzzy systems with learning capabilities are also covered. The advantages and disadvantages of neural networks and fuzzy systems are examined. The performance of these two systems in license plate recognition, a water purification plant, blood cell classification, and other real world problems is compared.

Bibliographic Details

Main Authors:	Abe, Shigeo. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Boston, MA : Springer US : Imprint: Springer, 1997
Subjects:	Computer science., Artificial intelligence., Mathematical logic., Statistical physics., Dynamical systems., Computer Science., Artificial Intelligence (incl. Robotics)., Statistical Physics, Dynamical Systems and Complexity., Mathematical Logic and Foundations.,
Online Access:	http://dx.doi.org/10.1007/978-1-4615-6253-5
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