“P” coordinator scheme and interaction prediction principle in hierarchical structure of ANN

• Autorzy: Płaczek S.
• Języki publikacji: EN

Abstrakty

EN

When implementing the hierarchical structure [4][5] of the learning algorithm of an Artificial Neural Network (ANN), two very important questions have to be solved. The first one is connected with the selection of the broad coordination principle. In [1], three different principles are described. They vary with regard to the degree of freedom for the first-level tasks. The second problem is connected with the coordinator structure or, in other words, the coordination algorithm. In the regulation theory, the process of finding the coordinator structure is known as the feedback principle. The simplest regulator structure (scheme) is known as the proportional regulator – “P” regulator. In the article, the regulator structure and its parameters are analysed as well as their impact on the learning process quality.

Słowa kluczowe

EN

Artificial Neural Network (ANN); hierarchy; decomposition; coordination; coordination principle; P-regulator; feedback principle

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Computer Applications in Electrical Engineering
• Rocznik: 2015
• Tom: Vol. 13
• Strony: 319--329
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Płaczek S.

• Vistula University 02-787 Warszawa, ul. Stokłosy 3

Bibliografia

• [1] M.D. Mesarocic, D. Macko, and Y. Takahara, Theory of hierarchical multilevel systems, Academic Press, New York and London 1970.
• [2] S. Placzek, "Decomposition and the principle of interaction prediction in hierarchical structure of learning algorithm of ANN", Poznań University of Technology, Academic Journal, Electrical Engineering no 84, Poznań 2015.
• [3] Ch. M. Bishop, Pattern Recognition and Machine Learning, Springer, Science + Business Media, LLC 2006.
• [4] W. Findeisen, J. Szymanowski, A. Wierzbicki, Teoria i metody obliczeniowe optymalizacji. Państwowe Wydawnictwo Naukowe, Warsaw 1977.
• [5] Zeng-Guang Hou, Madan M. Gupta, Peter N. Nikiforuk, Min Tan, and Long Cheng, "A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems", IEEE Transactions on Neural Networks, Vol. 18, No. 2, March 2007.
• [6] S. Płaczek, "A two-level on-line learning algorithm of Artificial Neural Network with forward connections". Proc. Science and Information Conference 2014, London, ISBN: 978-0-9893193-1-7.
• [7] Bubnicki Z., Teoria i algorytmy sterowania, Wydawnictwo Naukowe PWN, Warszawa 2005.