Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations
Chao Ma, Ardashir Mohammadzadeh, Hamza Turabieh, Majdi Mafarja, Shahab S. Band, Amir Mosavi
Abstract
In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.