TY  - JOUR
A1  - Mosavi, Amir Hosein
A1  - Shokri, Manouchehr
A1  - Mansor, Zulkefli
A1  - Qasem, Sultan Noman
A1  - Band, Shahab S.
A1  - Mohammadzadeh, Ardashir
T1  - Machine Learning for Modeling the Singular Multi-Pantograph Equations
JF  - Entropy
N2  - In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
KW  - Fuzzy-Regelung
KW  - square root cubature calman filter
KW  - statistical analysis
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43436
UR  - https://www.mdpi.com/1099-4300/22/9/1041
VL  - 2020
IS  - volume 22, issue 9, article 1041
SP  - 1
EP  - 18
PB  - MDPI
CY  - Basel
ER  -