Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system.
We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.
Radwan
Elsayed
E
Department of Control and System Engineering, Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama 225-8502, Japan. radwan@intlab.toin.ac.jp
Tazaki
Eiichiro
E
D000465:Algorithms; D001185:Artificial Intelligence; D003662:Decision Theory; D005823:Genetics / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D009474:Neurons