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A design of predictive computational network for transmission model of Lassa fever in Nigeria

RESULTS IN PHYSICS 2022年第0期39卷

作者： Shoaib, Muhammad Tabassum, Rafia Raja, Muhammad Asif Zahoor Nisar, Kottakkaran Sooppy Alqahtani, Mohammed S. Abbas, Mohamed COMSATS Univ Islamabad Dept Math Attock Campus Islamabad Pakistan Natl Yunlin Univ Sci & Technol 123 Univ Rd Yunlin 64002 Taiwan Prince Sattam bin Abdulaziz Univ Coll Arts & Sci Dept Math Wadi Aldawaser 11991 Saudi Arabia King Khalid Univ Coll Appl Med Sci Radiol Sci Dept Abha 61421 Saudi Arabia Univ Leicester Space Res Ctr BioImaging Unit Michael Atiyah Bldg Leicester LE1 7RH England King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia Delta Univ Sci & Technol Coll Engn Comp & Commun Dept Gamasa 35712 Egypt

This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible (Sh), exposed (Eh), infectious (Ih) and recovered (Rh) humans and rodent population i.e. susceptible (Sr) and infectious (Ir) rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.

关键词： Lassa fever Genetic algorithm Adam method Neural Networks sequential quadratic programming

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Optimization for far-distance and fuel-limited cooperative rendezvous between two coplanar spacecraft based on Lambert method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2020年第7期234卷 1301-1310页

作者： Wang, Zhanwen Dong, Yuming Feng, Weiming Zhao, Junfeng Shandong Univ Dept Engn Mech Jinan 250062 Peoples R China

Concentrating on far-distance and fuel cooperative rendezvous between two coplanar spacecraft under impulse thrust, this paper presents fuel-optimal results obtained by optimization algorithms numerically. Former research works have formulated optimization models of multiple-impulse orbit transfer and cooperative rendezvous under continuous thrust. In this paper, optimization models of cooperative rendezvous under impulse thrust are formulated based on these former researches. The process of cooperative rendezvous is simplified by introducing a hypothetical spacecraft. The degradation from cooperative rendezvous to active-passive rendezvous is prevented by revising objective functions. Quantum-behaved particle swarm optimization and sequential quadratic programming are combined to solve a practical problem, which is used to illustrate advantages of cooperative rendezvous when fuel consumption of one spacecraft is limited.

关键词： Cooperative rendezvous trajectory optimization Lambert's problem quantum-behaved particle swarm optimization sequential quadratic programming far-distance orbit maneuver

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Motion mitigation of hinged floating structures by adaptive control

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OCEAN ENGINEERING 2020年 216卷 107700-107700页

作者： Xia, Shuyan Xu, Daolin Zhang, Haicheng Shi, Qijia Wen, Guilin Wu, Yousheng Guangzhou Univ Ctr Res Leading Technol Special Equipment Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China China Ship Sci Res Ctr Wuxi 214082 Jiangsu Peoples R China

System stability has been a crucial issue in design of very large floating structures. Large responses of floating structures not only deteriorate operation conditions but also seriously affect the safety, service life and maintenance cost of structures. In this paper, an idea of using hinge connection in control is proposed for stabilizing a five-modular floating platform. It leads to a great simplification in the deployment of thrusters on semi-submersible modules and only the control of heave, roll and pitch motions needs to be concerned. The motion equation of the floating structure is formulated in conjunction with a Proportional-Derivative (PD) control algorithm and an adaptive estimator for uncertain loads based on Radial Basis Function neural network (RBFnn). sequential quadratic programming (SQP) is used to optimally distribute control thrusts. Numerical simulations verify the effectiveness of the proposed control method.

关键词： Motion control Floating structures Hinge connection Uncertain wave Adaptive estimator sequential quadratic programming

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Adaptive non-linear coordinated optimal dynamic platoon control of connected autonomous distributed electric vehicles on curved roads

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第12期14卷 1626-1637页

作者： Guo, Jinghua Jingyao, Wang Li, Keqiang Luo, Yugong Xiamen Univ Sch Aerosp Engn Xiamen Fujian Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

A novel adaptive coordinated platoon control of connected autonomous distributed electric vehicles (CADEVs) on curved roads is proposed to increase traffic security. The nonlinear vehicle dynamic model that can precisely reflect the driving behaviours of CADEVs on curved roads is deduced by the Newton-Euler method. Owing to the fact that CADEVs have the strong coupled, uncertain non-linear and over-actuated features, a novel disturbance observer-based adaptive coordinated optimal dynamic platoon control strategy is presented to supervise the lateral and longitudinal coupled motions of CADEVs on curved roads, in which the switching gains of backstepping sliding mode control term are precisely adjusted by the neural-network technique, and the uniform ultimate boundedness of closed-loop high-level control system is guaranteed through the Lyapunov stability theory. Then, a sequential quadratic programming (SQP) tire distributor is the basic component of the low-level control law, which can realise the dynamic control distribution of over-actuated tire actuators of CADEVs. Finally, the results manifest the effectiveness of the proposed adaptive coordinated platoon control strategy.

关键词： Lyapunov methods nonlinear control systems stability adaptive control electric vehicles control nonlinearities vehicle dynamics quadratic programming closed loop systems neurocontrollers observers variable structure systems uncertain systems actuators road vehicles tyres motion control optimal control mobile robots adaptive nonlinear coordinated optimal dynamic platoon control connected autonomous distributed electric vehicles curved roads CADEVs nonlinear vehicle dynamic model disturbance observer-based adaptive coordinated optimal dynamic platoon control strategy lateral coupled motions longitudinal coupled motions backstepping sliding mode control term dynamic control distribution closed-loop high-level control system Lyapunov stability theory sequential quadratic programming tire actuators neural-network technique

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FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane-Emden system

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COMPUTATIONAL & APPLIED MATHEMATICS 2020年第4期39卷 1-18页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Shoaib, Muhammad Aguilar, J. F. Gomez Hazara Univ Dept Math & Stat Mansehra Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan COMSATS Univ Islamabad Dept Math Attock Campus Attock 43600 Pakistan CONACyT Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico

In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNEICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the mean-squared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver.

关键词： Multi-fractional Lane– Emden model Multi-singular systems Artificial neural networks Meyer wavelet neural networks sequential quadratic programming Genetic algorithms

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An adaptive hierarchical optimization approach for the minimum compliance design of variable stiffness laminates using lamination parameters

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THIN-WALLED STRUCTURES 2020年 157卷 107068-107068页

作者： Zeng, Jiani Huang, Zhengdong Fan, Kuan Wu, Wenbo Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Hubei Peoples R China

This paper presents an adaptive hierarchical optimization approach to optimize the lamination parameter distribution of variable-stiffness laminated plate with truncated hierarchical B-Splines. Traditional methods use a uniform grid to design the distribution though its variation rate could change dramatically from point to point. As a consequence, the dense grid incurs a high computation cost while the rough one loses the design freedom. The main motivation of this research is to resolve this problem by performing a hierarchy of the optimizations respectively on different level grids of the hierarchical B-Spline for a non-uniform representation of lamination parameters. At the end of each level optimization, some elements in the multi-level grid are selected to be refined further according to a refinement rule. The hierarchical mesh consisting of newly generated elements and unrefined ones, is used to represent the solution, and its corresponding hierarchical B-Spline expression is updated with a new optimization step. Based on the sensitivity analysis approach proposed for the adaptive optimization problem, the sequential quadratic programming algorithm is implemented for the optimizations at individual levels. Finally, three examples of compliance minimization are chosen to validate the efficiency and accuracy of the proposed method.

关键词： Variable-stiffness laminate Lamination parameter Hierarchical optimization Truncated hierarchical B-Splines sequential quadratic programming

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Heuristic computing technique for numerical solutions of nonlinear fourth order Emden-Fowler equation

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年 178卷 534-548页

作者： Sabir, Zulqurnain Saoud, Sahar Raja, Muhammad Asif Zahoor Wahab, Hafiz Abdul Arbi, Adnene Hazara Univ Dept Math & Stat Mansehra Pakistan Ibn Zohr Univ Fac Appl Sci Agadir Dept Math Agadir Morocco Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan Natl Sch Adv Sci & Technol Borj Cedria Dept Adv Sci & Technol Borj Cedria Tunisia Univ Carthage Tunisia Polytech Sch Lab Engn Math LR01ES13 Tunis Tunisia

The present study presents numerical solution of the fourth order, singular, nonlinear Emden-Fowler equation integrated computational intelligence by exploiting the efficiency of feedforward artificial neural networks (ANNs), global optimization strength of genetic algorithms (GAs) and rapid local search of sequential quadratic programming (SQP). ANN is applied to discretize the nonlinear Emden-Fowler differential equation and consequentially construct a fitness/merit function on mean squared error sense. The optimization of the decision variables of ANN to solve the multi-singular, fourth order, nonlinear Emden-Fowler equation is performed by integrating capability GAs and SQP. The proficiency of the presented approach is certified on precision, convergence and stability indices, which are further enhanced through effectiveness, significance and reliability measures on statistical data for solving three different cases of the singular systems. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Nonlinear Emden-Fowler equation Multi-singular Feedforward artificial neural networks Statistical analysis Genetic algorithms Integrated computational intelligence sequential quadratic programming

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An advanced heuristic approach for a nonlinear mathematical based medical smoking model

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RESULTS IN PHYSICS 2022年第0期32卷 105137-105137页

作者： Saeed, Tareq Sabir, Zulqurnain Alhodaly, Mohammed Sh. Alsulami, Hamed H. Sanchez, Yolana Guerrero Hazara Univ Dept Math & Stat Mansehra Pakistan King Abdulaziz Univ Fac Sci Dept Math Nonlinear Anal & Appl Math NAAM Res Grp POB 80203 Jeddah 21589 Saudi Arabia Univ Murcia Dept Anat & Psicobiol Murcia 30001 Spain

The present study is related to solve the nonlinear dynamics of a smoke model using artificial neural networks (ANNs) under the optimization procedures of global heuristic and local search scheme. The genetic algorithm (GA) and sequential quadratic programming (SQP), i.e., GA-SQP used as global-local search approaches. The smoke nonlinear medical model depends upon four categories named as potential smokers, temporary smokers, smokers and permanent smokers. For solving these categories of the smoke system, an error based objective function is designed using these nonlinear equations and the initial conditions of the model. The performance through optimization of the objective function is testified using the ANNs and the hybrid combination of the GASQP for solving the nonlinear dynamics of the smoke system. To check the perfection of the proposed stochastic approach, the obtained results through the hybrid of GA-SQP are compared with the Adams scheme. Moreover, the designed scheme through statistical performances using different operators authenticates the reliability and stability to solve the nonlinear smoke model.

关键词： Nonlinear smoke system Artificial neural networks Adams results sequential quadratic programming Genetic algorithms Statistical analysis

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A Simply Structured Thin Carbon-Fiber-Reinforced Material for Highly Efficient Microwave Absorption

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2020年第1期68卷 341-350页

作者： Li, Changyou Zong, Yali Zhang, Ruonan Northwestern Polytech Univ Dept Elect Engn Xian 710129 Peoples R China Northwestern Polytech Univ Dept Commun Engn Xian 710129 Peoples R China

A thin laminated composite is proposed in this article for highly efficient microwave absorption at both high frequency and low frequency. It is composed of two layers of carbon-fiber-reinforced planar structure. In each layer, periodically arranged fiber array with the axes of the fibers parallel to each other is embedded into a ceramic matrix which is used to bind the fibers together and to provide necessary mechanical or chemical properties. Stacking up two layers of the planar structure with the fibers in different layers orientated into different directions produces a lamina which can be further parallely stacked up to provide the whole laminated material. The influence of the structural and electromagnetic parameters on the microwave absorption efficiency is first studied in detail with scattering matrix and boundary-mode matching method. Then the genetic algorithm and the sequential quadratic programming methods are used to form a two-step strategy based on the mode-matching method to optimize the structural and electromagnetic parameters for a highly efficient microwave absorption in both high-frequency and low-frequency bands. Numerical examples are also given to demonstrate the high absorption efficiency of the designed laminated material.

关键词： Laminates Absorption Electromagnetics Microwave theory and techniques Ceramics Electromagnetic scattering High frequency Boundary matching method carbon-fiber-reinforced laminate genetic algorithm microwave absorbing material sequential quadratic programming

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Receding horizon optimal control of HEVs with on-board prediction of driver's power demand

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第12期14卷 1534-1545页

作者： Zhang, Bo Xu, Fuguo Shen, Tielong Sophia Univ Dept Engn & Appl Sci Tokyo 1028554 Japan

To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.

关键词： optimal control energy management systems regression analysis hybrid electric vehicles quadratic programming fuel economy predictive control optimisation Gaussian processes learning (artificial intelligence) power system simulation control engineering computing power engineering computing power system control parallel hybrid electric vehicle real-time optimisation strategy learning-based method driver power demand energy management problem model-based predictive optimisation vehicle-to-vehicle data vehicle-to-infrastructure data receding horizon control traffic-in-the-loop environment on-board prediction receding horizon optimal control parallel HEV RHC finite-horizon optimisation powertrain control cosimulation Gaussian process regression sequential quadratic programming

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