Organizing the fuzzy inference based on multilevel parallelism
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2018.3.09Keywords:
fuzzy inference, multilevel parallelism, speed-up, fuzzy Takagi-Sugeno systems, acceleration estimationAbstract
In this paper, a method for constructing hierarchical systems of fuzzy inference based on multilevel parallelism, in particular, second-level parallelism, is developed, theoretically substantiated and implemented. This approach is designed to accelerate the computation of hierarchical fuzzy systems having complex dependency graphs between blocks of fuzzy rules. The concept of multilevel parallelism is formulated and presented. The notion of the level of parallelism is introduced. The theorem is formulated and proved, and a method for theoretical estimation of the maximum possible acceleration for systems constructed on the basis of parallelism of the level n is developed. An approach to designing hierarchical fuzzy systems based on multilevel parallelism for NVIDIA graphics accelerators is developed. Using NVIDIA CUDA technology, an experimental software system was designed for hierarchical systems of fuzzy inference based on multilevel parallelism for systems having complex graphs of dependencies between blocks of fuzzy rules. Experimental estimates of the acceleration are obtained. Also, based on the developed method, theoretical estimates of the maximum possible acceleration are found. A comparative characteristic of the theoretical and experimental estimates of the acceleration of hierarchical fuzzy systems is given.References
Zadeh L.A. The concept of a linguistic variable and its application to approximate reasoning / L.A. Zadeh // Information Sciences. — 1975. — Vol. 8, N 8. — P. 199–249, 301–357.
Wang D. A survay of hierarchical fuzzy systems (invited paper) / D. Wang, X. Zeng, J.A. Keane // International journal of computational cognition. — 2006. — Vol. 4, N 1. — P.18–29.
Ershov S.V. Printsipy postroenija nechetkih mul'tiagentnyh sistem v raspredelennoj srede / S.V. Ershov // Komp'juternaja matematika. — 2009. — № 2. — C. 54–61.
Yager R.R. On a hierarchical structure for fuzzy modeling and control / R.R. Yager // IEEE Trans. Syst. Man Cybern. — 1993. — N 23. — P. 1189–1197.
Cordon O. Linguistic modeling by hierarchical systems of linguistic rules / O. Cordon, F. Herrera, I. Zwir // IEEE Trans. Fuzzy Syst. — 2002. — N 10. — P. 2–20.
Torra V.A. review of the construction of hierarchical fuzzy systems / V.A. Torra // Int. J. Intell. Syst. — 2002. — N 17. — P. 531–543.
Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH / A.V. Leonenkov. — SPb.: BHV, 2005. — 736 s.
Voevodin V.V. Parallel'nye vychislenija / V.V. Voevodin, Vl.V. Voevodin. — SPb.: BHV-Peterburg, 2002. — 608 s.
Ponomarenko R.M. Modeli paralel'nykh iyerarkhichnykh system dlja nechitkoho lohichnoho vyvedennja / R.M. Ponomarenko // Komp’juterna matematyka. — 2017. — № 2. — S. 37–45.
Yershov S.V. Metod pobudovy paralel'nykh system nechitkoho lohichnoho vyvedennja na osnovi hrafichnykh pryskorjuvachiv / S.V. Yershov, R.M. Ponomarenko // Problemy prohramuvannja. — 2017. — № 4. — S. 3–15.
Yershov S.V. Paralel'ni modeli bahatorivnevykh nechitkykh system Takahi-Suheno / S.V. Yershov, R.M. Ponomarenko // Problemy prohramuvannja. — 2016. — № 1. — S. 141–149.
Yershov S.V. Jarusno-paralel'na model' obchyslen' dlja lohichnoho vyvedennja u nechitkykh bahatorivnevykh systemakh / S.V. Yershov, R.M. Ponomarenko // Komp’juterna matematyka. — 2016. — № 1. — S. 28–36.
