Title :
Design of unsupervised classifier
Author :
Sayeh, M.R. ; Ragu, A. ; Szu, H.H.
Author_Institution :
Dept. of Electr. Eng., Southern Illinois Univ., Carbondale, IL, USA
Abstract :
Reports a feedback unsupervised classifier formulated by differential equations with no external control and few tuning parameters. This classifier is called the Lyapunov associative memory, in order to emphasize the importance of the Lyapunov (energy) function in the design of the associative memory. A vigilance parameter is built in to the dynamics of the classifier. Its architecture consists of two modules: the learning and recall modules. The learning module shapes the recall module, energy function with the arrival of new input information. The classifier was tested with an analog input pattern used by the ART-2 (adaptive resonance theory) model
Keywords :
Lyapunov methods; classification; content-addressable storage; differential equations; feedback; learning systems; neural nets; pattern recognition; ART-2; Lyapunov associative memory; adaptive resonance theory; analog input pattern; differential equations; dynamics; energy function; feedback unsupervised classifier; learning module; recall module; vigilance parameter; Associative memory; Equations; Feedback; Magnesium compounds; Shape; Silver; Springs; Stability; Subspace constraints; Testing;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155369