SavedAdd your journal

Journals / Mühendislik Bilimleri Dergisi / 2008 / Cilt: 14 - Sayı: 2

A high performance optimization technique for pole balancing problem

Kutup dengeleme problemi için yüksek başarımlı bir optimizasyon tekniği

BAHADIR KARASULU
SERKAN BALLI
MUSTAFA SERDAR KORUKOĞLU
AYBARS UĞUR
Pages
175–183
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Success of method based on results is discussed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:High performance computing techniques can be used effectively for solution of the complex scientific problems.
📊
Key Findings & Data:In this study, a solution is developed for pole balancing problem using Artificial Neural Network (ANN) and high performance computation technique.
💡
Conclusion & Impact:Success of method based on results is discussed.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

Özet

Karmaşık bilimsel problemlerin etkin olarak çözümlenmesinde yüksek başarımlı hesaplama teknikleri kullanılmaktadır. Kutup dengeleme problemi, yapay zeka alanları içerisinde önemli yer tutan robotbilim dalının en temel ölçüm araçlarından biridir. Bu çalışmada kutup dengeleme problemi, Yapay Sinir Ağı (YSA) ve yüksek başarımlı hesaplama tekniği kullanılarak çözülmüştür. Kutbu (çubuğu) dengede tutmayı sağlayan kuvvetin bulunmasında kullanılan destekleyici öğrenme yöntemini temel alan algoritma paralel ortama aktarılmıştır. Gerçekleştirimde C programlama dili ve paralel hesaplama tekniği için Mesaj Geçme Arayüzü kullanılmıştır. Bir YSA modeli olan Öz-örgütlemeli Harita Ağı’na ait yapay sinir hücre düğümleri ve ağırlıkları her biri dört çekirdekli altı adet (toplamda yirmi dört) işlemciye sahip bir sunucu bilgisayardaki işlemcilere dağıtılarak, farklı sinir hücre sayıları için performans değerleri elde edilmiştir. Yöntemin başarısı sonuçlar üzerinden tartışılmıştır.

🔖

Cite This Article

KARASULU, B., BALLI, S., KORUKOĞLU, M. S., UĞUR, A. (2008). A high performance optimization technique for pole balancing problem. Mühendislik Bilimleri Dergisi, Cilt: 14 - Sayı: 2, 175–183. https://ajindex.com/articles/289095

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Amdahl, G.M. 1967. Validity of single-processor approach to achieving large-scale computing capability, proceedings of AFIPS conference, reston, VA. p. 483-485.
  2. El-Rewini, H. and Abd-El-Barr, M. 2005. Advanced Computer Architecture and Parallel Processing, Wiley-Interscience, John Wiley and Sons Inc. Flynn, M. 1972. Some computer organizations and their effectiveness, IEEE Trans. Comput., Vol. C-21, p. 948. Gomez, F. and Miikkulainen, R. 1998. 2-D pole balancing with recurrent evolutionary networks. In Proceedings of the International Conference on Artificial Neural Networks (ICANN-98), Skovde, Sweden), 425–430.
  3. Grama, A., Gupta A., Karypis G. and Kumar, V. 2003.
  4. Introduction to Parallel Computing, Second Edition,
  5. Addison Wesley Publishing, ISBN 0-201- 64865-2, 856, 2003.
  6. Grounds, M. and Kudenko, D. 2006. Parallel reinforcement learning by merging function approximations. Department of Computer Science University of York. Karasulu, B. ve Uğur, A. 2007. Özörgütlemeli yapay sinir ağı modelinin kullanıldığı kutup dengeleme problemi için paralel hesaplama tekniği ile bir başarım eniyileştirme yöntemi, akademik bilişim 2007, Bildiri No: 25, Dumlupınar Üniversitesi, Kütahya, 31 Ocak - 2 Şubat 2007.
  7. Kohonen, T. 1996.
  8. The Speedy Som, Technical Report a33, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland. Kohonen, T. 1997.
  9. Self-Organizing Maps, Series in Information Sciences, Vol. 30, Second Edition Springer, Heidelberg, ISBN 3-540-62017-6, 1997.
  10. Kretchmar, R. M. 2002. Parallel reinforcement learning. In proceedings of the 6th World Conference on Systemics, Cybernetics, and Informatics (SCI2002). LAM-MPI Takımı websitesi.(Çevrimiçi: http://www.lam-mpi.org).
  11. Stanley, O.K. and Miikkulainen, R. 2002. Efficient reinforcement learning through evolving neural network topologies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002).
  12. Pardoe, D., Ryoo, M. and Miikkulainen, R. 2005. Evolving neural network ensembles for control problems, in proceedings of the genetic and evolutionary computation conference (GECCO-2005).
  13. Rauber, A., Tomsich, P. and Merkl, D. 2000. parSOM: A parallel implementation of the self- organizing map exploiting cacheeffects: making the som fit for interactive high performance data analysis, p. 6177, IEEE-INNS-ENNS Int. Joint Conference on Neural Networks (IJCNN'00), Vol 6. Sutton, R. S. 1992. Reinforcement learning architectures. Proceedings ISKIT'92 International Symposium