Title :
Multiperceptron Architecture Based on the Potts Model for Pattern Identification
Author :
Kryzhanovsky, Vladimir
Author_Institution :
Center of Opt.-Neural Technol., Russian Acad. of Sci., Moscow, Russia
Abstract :
A multiperceptron architecture based on the Potts model is presented. It is shown that the storage capacity of this architecture grows linearly with the increase of the number of perceptrons. The combination of perceptrons is useful when one perceptron is unable to solve an identification task. The method can be applied for q-ary or binary patterns.
Keywords :
Potts model; multilayer perceptrons; Potts model; multiperceptron architecture; pattern identification; Artificial intelligence; Associative memory; Character recognition; Computational intelligence; Computer architecture; Neural networks; Neurons; Pattern analysis; Region 8; Signal mapping; Perceptron; Potts model; pattern recognition;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.472
