DocumentCode :
3035214
Title :
Mathematical formulation of cognitive and learning processes in neural networks
Author :
DeFigueiredo, Rui J P
Author_Institution :
Lab. for Intelligent Sensors & Syst., California Univ., Irvine, CA, USA
fYear :
1990
fDate :
4-7 Nov 1990
Firstpage :
317
Lastpage :
319
Abstract :
Recent results in modeling the processes of recognition of complex patterns and learning performed by an artificial neural network as a nonlinear mapping from a data vector space into a space of binary strings are presented. By the construction of a suitable nonlinear functional space for this mapping. an optimal solution in terms of a closed-form description of the neural net model can be obtained. A learning algorithm for this model, which is aimed at reducing the redundancy and complexity of the net by the extraction of a minimal set of prototypes from the training set, is described
Keywords :
cognitive systems; learning systems; neural nets; pattern recognition; binary strings; closed-form description; cognitive processes; data vector space; learning processes; neural networks; nonlinear mapping; pattern recognition; Artificial neural networks; Chemical lasers; Encoding; Intelligent networks; Intelligent sensors; Laboratories; Neural networks; Nonlinear systems; Pattern classification; Tree graphs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1990. Conference Proceedings., IEEE International Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
0-87942-597-0
Type :
conf
DOI :
10.1109/ICSMC.1990.142118
Filename :
142118
Link To Document :
بازگشت