Bahadır KARASULU, Serkan BALLI, Serdar KORUKOĞLU, Aybars UĞUR

KUTUP DENGELEME PROBLEMi iÇiN YÜKSEK BASARIMLI BiR OPTiMiZASYON TEKNiGi

Karmasık bilimsel problemlerin etkin olarak çözümlenmesinde yüksek basarımlı hesaplama teknikleri kullanılmaktadır. Kutup dengeleme problemi, yapay zeka alanları içerisinde önemli yer tutan robotbilim dalının en temel ölçüm araçlarından biridir. Bu çalısmada kutup dengeleme problemi, Yapay Sinir Agı (YSA) ve yüksek basarımlı hesaplama teknigi kullanılarak çözülmüstür. Kutbu (çubugu) dengede tutmayı saglayan kuvvetin bulunmasında kullanılan destekleyici ögrenme yöntemini temel alan algoritma paralel ortama aktarılmıstır. Gerçeklestirimde C programlama dili ve paralel hesaplama teknigi için Mesaj Geçme Arayüzü kullanılmıstır. Bir YSA modeli olan Öz-örgütlemeli Harita Agı'na ait yapay sinir hücre dügümleri ve agırlıkları her biri dört çekirdekli altı adet (toplamda yirmi dört) islemciye sahip bir sunucu bilgisayardaki islemcilere dagıtılarak, farklı sinir hücre sayıları için performans degerleri elde edilmistir. Yöntemin basarısı sonuçlar üzerinden tartısılmıstır.

Anahtar Kelimeler:

Yapay zeka, Kutup dengeleme problemi, Paralel hesaplama, Yapay sinir agları, Ögrenme algoritmaları.

A HIGH PERFORMANCE OPTIMIZATION TECHNIQUE FOR POLE BALANCING PROBLEM

High performance computing techniques can be used effectively for solution of the complex scientific problems. Pole balancing problem is a basic benchmark tool of robotic field, which is an important field of Artificial Intelligence research areas. In this study, a solution is developed for pole balancing problem using Artificial Neural Network (ANN) and high performance computation technique. Algorithm, that basis of the Reinforcement Learning method which is used to find the force of pole's balance, is transfered to parallel environment. In Implementation, C is preferred as programming language and Message Passing Interface (MPI) is used for parallel computation technique. Self–Organizing Map (SOM) ANN model's neurons (artificial neural nodes) and their weights are distributed to six processors of a server computer which equipped with each quad core processor (total 24 processors). In this way, performance values are obtained for different number of artificial neural nodes. Success of method based on results is discussed.

Keywords:

Artificial Intelligence, Pole balancing problem, Parallel computing, Artificial neural network, Learning algorithms.,

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