Pengembangan jaringan saraf tiruan dengan fungsi basis radial (FBR) fuzzy dan aplikasinya = Development of neural network with radial basis function (RBF) fuzzy and its application

Main Authors: Yoan Elviralita, author, Add author: Benyamin Kusumoputro, supervisor, Add author: Wahidin Wahab, examiner, Add author: Feri Yusivar, examiner, Add author: Aries Subiantoro, examiner
Format: Masters Bachelors
Terbitan: Universitas Indonesia , 2011
Subjects:
Online Access: https://lib.ui.ac.id/detail?id=20292057
ctrlnum 20292057
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><type>Thesis:Masters</type><title>Pengembangan jaringan saraf tiruan dengan fungsi basis radial (FBR) fuzzy dan aplikasinya = Development of neural network with radial basis function (RBF) fuzzy and its application</title><creator>Yoan Elviralita, author</creator><creator>Add author: Benyamin Kusumoputro, supervisor</creator><creator>Add author: Wahidin Wahab, examiner</creator><creator>Add author: Feri Yusivar, examiner</creator><creator>Add author: Aries Subiantoro, examiner</creator><publisher>Universitas Indonesia</publisher><date>2011</date><subject>Neural Networks in Applications</subject><description>Dalam beberapa tahun ini, telah banyak penelitian yang berhubungan dengan pengenalan pola dilakukan untuk mengindentifikasi berbagai macam bentuk pola. Tesis ini membahas pengembangan jaringan saraf tiruan fungsi basis radial fuzzy. Dalam penelitian ini dilakukan dua percobaan, yaitu jaringan saraf fungsi basis radial fuzzy menggunakan SOM dan jaringan saraf fungsi basis radial fuzzy tanpa SOM. Hasil yang dicapai dari recognition rate menunjukkan jaringan saraf fungsi basis radial fuzzy menggunakan SOM memberikan performa yang baik. Jaringan saraf ini diharapkan dapat dikembangkan oleh peneliti-peneliti yang lain untuk kemajuan keilmuan dalam segala bidang. &lt;hr&gt; In recent years, has been much research related to pattern recognition performed to identify various forms of patterns. This thesis discusses the development of artificial neural networks fuzzy radial basis functions. In this study conducted two experiments, namely radial basis function neural network fuzzy neural network using the SOM and fuzzy radial basis function without SOM. The result of recognition rate shows the radial basis function neural networks using a fuzzy SOM gives a good performance. Neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.</description><identifier>https://lib.ui.ac.id/detail?id=20292057</identifier><recordID>20292057</recordID></dc>
format Thesis:Masters
Thesis
Thesis:Bachelors
author Yoan Elviralita, author
Add author: Benyamin Kusumoputro, supervisor
Add author: Wahidin Wahab, examiner
Add author: Feri Yusivar, examiner
Add author: Aries Subiantoro, examiner
title Pengembangan jaringan saraf tiruan dengan fungsi basis radial (FBR) fuzzy dan aplikasinya = Development of neural network with radial basis function (RBF) fuzzy and its application
publisher Universitas Indonesia
publishDate 2011
topic Neural Networks in Applications
url https://lib.ui.ac.id/detail?id=20292057
contents Dalam beberapa tahun ini, telah banyak penelitian yang berhubungan dengan pengenalan pola dilakukan untuk mengindentifikasi berbagai macam bentuk pola. Tesis ini membahas pengembangan jaringan saraf tiruan fungsi basis radial fuzzy. Dalam penelitian ini dilakukan dua percobaan, yaitu jaringan saraf fungsi basis radial fuzzy menggunakan SOM dan jaringan saraf fungsi basis radial fuzzy tanpa SOM. Hasil yang dicapai dari recognition rate menunjukkan jaringan saraf fungsi basis radial fuzzy menggunakan SOM memberikan performa yang baik. Jaringan saraf ini diharapkan dapat dikembangkan oleh peneliti-peneliti yang lain untuk kemajuan keilmuan dalam segala bidang. <hr> In recent years, has been much research related to pattern recognition performed to identify various forms of patterns. This thesis discusses the development of artificial neural networks fuzzy radial basis functions. In this study conducted two experiments, namely radial basis function neural network fuzzy neural network using the SOM and fuzzy radial basis function without SOM. The result of recognition rate shows the radial basis function neural networks using a fuzzy SOM gives a good performance. Neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.
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