Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms: IEEE/Nagoya-University World Wisepersons Workshop, Nagoya, Japan, August 9 - 10, 1994. Selected Papers: Lecture Notes in Computer Science, cartea 1011
Editat de Takeshi Furuhashien Limba Engleză Paperback – 15 noi 1995
The combination of approaches based on fuzzy logic, neural networks and genetic algorithms are expected to open a new paradigm of machine learning for the realization of human-like information processing systems. The first six papers in this volume are devoted to the combination of fuzzy logic and neural networks; four papers are on how to combine fuzzy logic and genetic algorithms. Four papers investigate challenging applications of fuzzy systems and of fuzzy-genetic algorithms.
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Specificații
ISBN-13: 9783540606079
ISBN-10: 3540606076
Pagini: 240
Ilustrații: VIII, 232 p.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.34 kg
Ediția:1995
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540606076
Pagini: 240
Ilustrații: VIII, 232 p.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.34 kg
Ediția:1995
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Fuzzy associative memory system and its application to multi-modal interface.- Hybrid connectionist fuzzy systems for speech recognition and the use of connectionist production systems.- Fuzzy gaussian potential neural networks using a functional reasoning.- Recurrent fuzzy logic using neural network.- Information aggregating networks based on extended Sugeno's fuzzy integral.- A neuro-fuzzy architecture for high performance classification.- Investigation of stability and robustness of a fuzzy traction control system.- Knowledge-based rules for control of the sake (Ginjoshu) making process and their application in fuzzy control.- A framework for studying the effects of dynamic crossover, mutation, and population sizing in genetic algorithms.- Unsupervised/supervised learning for RBF-fuzzy system.- Genetic algorithms for the development of fuzzy controllers for mobile robots.- A new approach to genetic based machine learning and an efficient finding of fuzzy rules.- A neuro-money recognition using optimized masks by GA.- Genetic-fuzzy systems for financial decision making.