Swarming Computational Techniques for the Influenza Disease System  

作　　者：Sakda Noinang Zulqurnain Sabir Gilder Cieza Altamirano Muhammad Asif Zahoor Raja Manuel Jesús Sànchez-Chero María-Verónica Seminario-Morales Wajaree Weera Thongchai Botmart 

机构地区：[1]Department of Mathematics Statistics and Computer,Faculty of Science,Ubon Ratchathani University,Ubon Ratchathani,34190,Thailand [2]Department of Mathematics and Statistics,Hazara University,Mansehra,21120,Pakistan [3]Universidad Nacional Autonoma de Chota,Cajamarca,06121,Peru [4]Future Technology Research Center,National Yunlin University of Science and Technology,123 University Road,Section 3,Douliou,Yunlin,64002,Taiwan [5]Universidad Nacional de Frontera,Sullana,Peru [6]Department of Mathematics,Faculty of Science,Khon Kaen University,Khon Kaen,40002,Thailand

出　　处：《Computers, Materials & Continua》2022年第12期4851-4868,共18页计算机、材料和连续体（英文）

基　　金：This research received funding support from the NSRF via the Program Man-agement Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640092).

摘　　要：The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected people,recovered individuals and cross-immune people.The solutions of the IDS are provided by using the artificial neural networks(ANNs)together with the swarming computational paradigm-based particle swarmoptimization(PSO)and interior-point scheme(IPA)that are the global and local search approaches.The ANNs-PSO-IPA has never been applied to solve the IDS.Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA.The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions(reference solutions).An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA.Furthermore,the reliability and competence of the proposed computing method are enhanced through the statistical performances.

关 键 词：DISEASE influenza model reference results particle swarm optimization artificial neural networks interior-point scheme statistical investigations 

分 类 号：R511.7[医药卫生—内科学]