TAKAGI-SUGENO TIPIDAGI KOMPAKT VA ANIQ NORAVSHAN TIZIMLARNI YARATISH METODIKASI
Keywords:
Odatda, bunday modellashtirish Takagi-Sugeno tipidagi noravshan tizimlar yordamida amalga oshiriladiAbstract
Noravshan modellashtirish murakkab nochiziq tizimlarda "kirish-chiqish" bog‘liqligini baholashning usullaridan biri hisoblanadi
Downloads
References
И.А. Ходашинский, К.С. Сарин Методика построения компактных и точных нечетких систем типа Такаги–Сугено
Takagi T. Fuzzy identification of systems and its application to modeling and control / T. Takagi, M. Sugeno // IEEE Transaction Systems, Man and Cybernetics. – 1985. – Vol. 15, No. 1. – P. 116–132.
Tron E. Mathematical modeling of observed natural behavior: a fuzzy logic approach / E. Tron, M. Margaliot // Fuzzy Sets and Systems. – 2004. – Vol. 146. – P. 437–450.
Ходашинский И.А. Идентификация нечетких сис-тем: методы и алгоритмы// Проблемы управления. – 2009. –№4. – С. 15–23.
Abonyi J. Cluster Analysis for Data Mining and System Identification / J. Abonyi, B. Feil. – Birkhäuser, 2000. – 319 p.
Yager R. Generation of fuzzy rules by mountain method / R. Yager, D. Filev // Journal of Intelligent & Fuzzy Systems. – 1994. – Vol. 2. – P. 209–219.
Sadrabadi M.R. Identification of the linear parts of nonlinear systems for fuzzy modeling / M.R. Sadrabadi, M.H.F. Zarandi // Applied Soft Computing. – 2011. – Vol. 11. – P. 807–819.
T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm / C. Li, J. Zhou, Q. Li, X. Xiang, X. An // Engineering Applications of Artificial Intelligence. – 2009. – Vol. 22. – P. 646–653.
Soltani M. A novel fuzzy c–regression model algorithm using a new error measure and particle swarm optimization / M. Soltani, A. Chaari, F. Ben Hmida // International Journal of Applied Mathematics and Computer Science. – 2012. – Vol. 22, No. 3. – P. 617–628.
Handbook of Metaheuristics / Editors M. Gendreau, J.-Y. Potvin. – Springer, 2010. – 669 p.
Ishibuchi H. Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design / H. Ishibuchi, Y. Nojima // Knowledge-Based Systems. – 2013. – Vol. 54. – P. 22–31.
Nguyen C.H. A discussion on interpretability of linguistic rule based systems and its application to solve regression problems / C.H. Nguyen, V.T. Hoang, V.L. Nguyen // Knowledge-Based Systems. – 2015. – Vol. 88. – P. 107–133.
Gorzalczany M.B. A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability / M.B. Gorzalczany, Rudzinski F. // Applied Soft Computing. – 2016. – Vol. 40. –P. 206–220.
Galende-Hernandez M. Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection / M. Galende-Hernandez, G.I. Sainz-Palmero, M.J. Fuente-Aparicio // Soft Computing. – 2012. – Vol. 16. – P. 451–470.
Pulkkinen P. A Dynamically Constrained Multiobjective Genetic FuzzySystem for Regression Problems / P. Pulkkinen, H. Koivisto // IEEE Transaction on Fuzzy Systems. – 2010. – Vol. 18, No. 1. – P. 161–177.
Alcala R. A Fast and Scalable Multiobjective Genetic Fuzzy System for Linguistic Fuzzy Modeling in HighDimensional Regression Problems / R. Alcala, M.J. Gacto, F. Herrera // IEEE Transaction on Fuzzy Systems. – 2011. – Vol. 19, No 4. – P. 666–681.
Antonelli M. An efficient multi-objective evolutionary fuzzy system for regression problems / M. Antonelli, P. Ducange, F. Marcelloni // International Journal of Approximate Reasoning. – 2013. – Vol. 54, No 9. – P. 1434–1451.
Горбунов И.В. Методы построения трехкритери-альных Парето-оптимальных нечетких классификаторов/ И.В. Горбунов, И.А. Ходашинский// Искусственный ин-теллект и принятие решений. – 2015. – №2. – С. 75–87.
Ходашинский И.А. Алгоритмы поиска компро-мисса между точностью и сложностью при построении нечётких аппроксиматоров/ И.А. Ходашинский, И.В. Гор-бунов// Автометрия. – 2013. – Т. 49, №6. – С. 51–61.