PATTERN RECOGNITION WITH BACKPROPAGATION ALGORITHM

Kurniawan, Ferdinandus Hanry (2016) PATTERN RECOGNITION WITH BACKPROPAGATION ALGORITHM. Other thesis, Fakultas Ilmu Komputer UNIKA Soegijapranata.

[img] Text (COVER)
12.02.0043 Ferdinandus Hanry Kurniawan COVER.pdf

Download (276kB)
[img] Text (BAB I)
12.02.0043 Ferdinandus Hanry Kurniawan BAB I.pdf
Restricted to Registered users only

Download (33kB)
[img] Text (BAB II)
12.02.0043 Ferdinandus Hanry Kurniawan BAB II.pdf
Restricted to Registered users only

Download (207kB)
[img] Text (BAB III)
12.02.0043 Ferdinandus Hanry Kurniawan BAB III.pdf
Restricted to Registered users only

Download (90kB)
[img] Text (BAB IV)
12.02.0043 Ferdinandus Hanry Kurniawan BAB IV.pdf
Restricted to Registered users only

Download (108kB)
[img] Text (BAB V)
12.02.0043 Ferdinandus Hanry Kurniawan BAB V.pdf
Restricted to Registered users only

Download (523kB)
[img] Text (BAB VI)
12.02.0043 Ferdinandus Hanry Kurniawan BAB VI.pdf
Restricted to Registered users only

Download (28kB)
[img] Text (DAFTAR PUSTAKA)
12.02.0043 Ferdinandus Hanry Kurniawan DAFTAR PUSTAKA.pdf

Download (29kB)

Abstract

Complex pattern recognition is a difficult problem to solve because in the real v&rid patterns / images to be recognized has noise. The unavailability of good mathematical calculation that can produce the desired translation, nauseating to be able to resolve a problem of pattern recognition. Imagine if you have to match in the database one by one that there is also troublesome, as resolved by adding a large number of programs and inefficient. Therefore to solve the problem, back propagation algorithm is the best answer to a complex pattern recognition. Backpropagation algorithm is implement at Pattern Recognition Application. Backpropagation v\orks far faster than earlier approaches to learning, making it possible to use neural nets to solve problems v\ftich had previously been insoluble. Today, the backpropagation algorithm is the workhorse of learning in neural networks. This algorithm do a tv\o-stage calculation. First forward propagation to calculate the error betmen the actual output and the target Second, backmrd propagation, vtiich propagates the error back to fix the v&ights on all existing neurons. By using the back propagation algorithm, pattern recognition problems more easily and achieved results better than using matching algorithm. Because the character recognition using the data containing noise and sizeable

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: Mr Ign. Setya Dwiana
Date Deposited: 31 May 2016 06:22
Last Modified: 31 May 2016 06:22
URI: http://repository.unika.ac.id/id/eprint/9719

Actions (login required)

View Item View Item