Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

Ma, Chao; Mohammadzadeh, Ardashir; Turabieh, Hamza; Mafarja, Majdi; Band, Shahab S.; Mosavi, Amir

doi:10.1109/access.2020.3044548

Cited by 30 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, stochastic design of LMBNNs has never been explored to solve the nonlinear host-vector-predator model. Few well-known applications of the numerical stochastic solvers are COVID-19 system [24], nonlinear higher order system [25], Thomas-Fermi equation [26], differential form of the fractional models [27], dengue fever nonlinear system [28], periodic singular models [29], a multisingular system [30], and functional models [31][32][33]. These motivate submissions impressed the authors to solve the nonlinear host-vector-predator model using a robust, consistent, precise, and reliable platform through the LMBNN operators.…”

Section: Introductionmentioning

confidence: 99%

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Sabir

Umar

Shah

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

show abstract

Section: Introductionmentioning

confidence: 99%

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Sabir

Umar

Shah

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…From this perspective, the overall performance of the controller will be improved. On the other side, since the learning performance can be improved by the reinforcement signal, an optimal or near optimal control action may be resulted in the sense of (20). Now, by employing Assumptions 1-2 and Assumption 4, consider the following relationship:…”

Section: Controller Designmentioning

confidence: 99%

Robust state and output feedback prescribed performance interval type‐3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

Elhaki

Shojaei

Mohammadzadeh

2022

IET Control Theory & Appl

Self Cite

View full text Add to dashboard Cite

This paper presents a novel adaptive reinforcement learning control method with interval type-3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high-performance closed-loop system, a high-gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov-based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques.

show abstract

“…They evaluated the stability and robustness of the proposed method. A new optimized version of T3FLS using an unscented Kalman filter (UKF) was used to solve singular multi-pantograph differential equations (SMDEs) [30]. Non-singleton T3FLS, fractional-order and an IT3FLS controller were proposed for control of gyroscopic MEMS [31].…”

Section: Introductionmentioning

confidence: 99%

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

show abstract

Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

Cited by 30 publications

References 39 publications

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Robust state and output feedback prescribed performance interval type‐3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

Contact Info

Product

Resources

About