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Title:RAZPOZNAVANJE ZNAKOV S HOPFIELDOVO MREŽO
Authors:ID Višić, Marko (Author)
ID Guid, Nikola (Mentor) More about this mentor... New window
ID Strnad, Damjan (Comentor)
Files:.pdf UNI_Visic_Marko_2009.pdf (670,72 KB)
MD5: B12348B88FDE6253CB404D1581A1923C
PID: 20.500.12556/dkum/b1f95706-37a5-41eb-90ea-3e4f7e49eb4c
 
Language:Slovenian
Work type:Undergraduate thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo obdelali delovanje Hopfieldove nevronske mreže pri razpoznavanju znakov, ki so predstavljeni kot dvodimenzionalni binarni vzorci. V začetku smo predstavili osnove nevronskih mrež, nato pa smo opisali še zgradbo in delovanje Hopfieldove nevronske mreže. Predstavili smo problematiko lažnih stanj ter opisali metodo razlikovanja med lažnimi atraktorji in naučenimi stanji mreže. Podrobneje smo opisali programsko rešitev Hopfieldovega algoritma, ki smo jo razvili, nato pa smo predstavili rezultate poskusov, ki smo jih opravili s pomočjo implementacije.
Keywords:umetna inteligenca, Hopfieldova nevronska mreža, lažna stanja, Hebbovo učenje
Place of publishing:Maribor
Publisher:[M. Višić]
Year of publishing:2009
PID:20.500.12556/DKUM-10889 New window
UDC:004.89:004.92(043.2)
COBISS.SI-ID:13663510 New window
NUK URN:URN:SI:UM:DK:LTUNGRCK
Publication date in DKUM:23.06.2009
Views:2580
Downloads:273
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:CHARACTER RECOGNITION WITH HOPFIELD NETWORK
Abstract:In this diploma thesis we have studied Hopfield neural network operation and recognition of characters, which are presented as twodimensional binary patterns. First, we have described the basics of neural networks in general, after which we have also considered the structure and operation of the Hopfield neural network. Further, we have presented the problem of false attractors and the method of distinguishing between false attractors and learned states. We have also presented our implementation of the Hopfield neural net algorithm and the results of experiments, conducted with the help of our program.
Keywords:artificial intelligence, Hopfield neural network, false attractors, Hebbian learning


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