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Self Organizing Map

Self Organizing Map. SOM. Jaringan Self- Organizing Map merupakan salah satu model jaringan saraf tiruan yang menggunakan metode pembelajaran tanpa supervisi (unsupervised learning) Salah satu keunggulan dari algoritma Self-

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Self Organizing Map

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  1. Self Organizing Map

  2. SOM • Jaringan Self- Organizing Map merupakansalahsatu model jaringansaraftiruan yang menggunakanmetodepembelajarantanpasupervisi (unsupervised learning) • Salahsatukeunggulandarialgoritma Self- Organizing Map adalahmampuuntukmemetakandataberdimensitinggikedalambentukpetaberdimensirendah. • Prosespemetaanterjadiapabilasebuahpola berdimensibebasdiproyeksikandariruang masukankeposisipada array berdimensisatuatau dua

  3. Algoritma Diberikansuatu data set yang akandibuat SOM nya • Inisialisasisejumlah weight dengannilaiacak • Untuksetiapsample input xpada dataset, • cari weight yang paling mendekatidengan Euclidian distance, disebut BMU(Best Matching Unit) • update weight tsbdenganrumus • Lakukaniterasiuntukmeng-update weight sampaimencapainilaiiterasi yang telahditentukandanmengurangilajupembelajaran . Ulangidarilangkah 2

  4. A map of the world where countries have been colored with the color describing their poverty type (the color was obtained with the SOM in the previous figure):

  5. Matlab net = newsom(PR,[d1,d2,...],tfcn,dfcn,olr,osteps,tlr,tns) • PR - Rx2 matrix of min and max values for R input elements. • Di - Size of ith layer dimension, defaults = [5 8]. • TFCN - Topology function, default = 'hextop'. • DFCN - Distance function, default = 'linkdist'. • OLR - Ordering phase learning rate, default = 0.9. • OSTEPS - Ordering phase steps, default = 1000. • TLR - Tuning phase learning rate, default = 0.02; TND - Tuning phase neighborhood distance, default = 1.

  6. Akan digunakanpelatihan SOM dengan data gempa (datagempa.xls) • Dalammatlabgunakanfungsisom (newsom(parameter,)) • Lihat source code som.m

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