Title :
Fuzzy LAPART supervised learning through inferencing for stable category recognition
Author :
Han, Gabsoo ; Ham, Fredric M. ; Fausett, Laurene V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Inst. of Technol., Melbourne, FL, USA
Abstract :
Fuzzy LAPART (laterally primed adaptive resonance theory), a neural network architecture for supervised learning through logical inferencing, is introduced with fast and slow learning algorithms and match tracking capability. Based on the original architecture developed by Healy, et al., the enhanced architecture consists of interconnected fuzzy adaptive resonance theory (fuzzy ART) modules originated by Carpenter, et al. The interconnections enable fuzzy LAPART to infer one pattern class from another to form a predictive pattern class. Slow learning capability has been incorporated into the neural network with fast commit and slow recode options. The problem of separation of spirals is used to perform benchmark tests for fuzzy LAPART. Also, based on fuzzy set theory, geometric interpretations are presented in 2 and 3 dimensional spaces using fuzzy LAPART. Performance results for both test cases are compared to results obtained from a counterpropagation clustering network
Keywords :
ART neural nets; fuzzy neural nets; fuzzy set theory; inference mechanisms; learning (artificial intelligence); pattern recognition; fuzzy laterally primed adaptive resonance theory; fuzzy set theory; geometric interpretations; interconnections; learning algorithms; logical inferencing; match tracking; neural network architecture; predictive pattern; supervised learning; Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Inference algorithms; Neural networks; Resonance; Spirals; Subspace constraints; Supervised learning; Testing;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343691
