一种新的径向基方法求解SIRC流行病延迟微分模型

IF 1.7 4区数学 Q2 MATHEMATICS, APPLIED

International Journal of Computer Mathematics Pub Date : 2023-08-15 DOI:10.1080/00207160.2023.2248286

Z. Sabir, D. Baleanu, F. Mallawi, M. Z. Ullah

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引用次数: 0

摘要

本工作的目的是构建一个可靠的随机框架来求解SIRC延迟差分流行病系统，即基于冠状病毒动力学的SIRC- ddes。提出了利用贝叶斯正则化神经网络(RB- brnn)优化径向基(RB)传递函数的设计方法。SIRC-DDES分为易感、感染、恢复和交叉免疫。利用所得结果和参考结果的性能，对三种SIRC-DDES进行了RB-BRNN的准确性检验。利用参考溶液的训练、测试和验证性能，减小了均方误差。绝对误差在10−07 ~ 10−08之间的小值，以及基于误差直方图值、状态转移调查、相关和回归测试的不同统计算子性能，也证明了所提出技术的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A novel radial basis procedure for the SIRC epidemic delay differential model

The purpose of this work is to construct a reliable stochastic framework for solving the SIRC delay differential epidemic system, i.e. SIRC-DDES that is based on the coronavirus dynamics. The design of radial basis (RB) transfer function with the optimization of Bayesian regularization neural network (RB-BRNN) is presented to solve the SIRC-DDES. The SIRC-DDES is classified into susceptible , infected , recovered and cross-immune . The exactness of the RB-BRNN is performed for three cases of SIRC-DDES by using the performances of the obtained and reference results. The mean square error is reduced by using the training, testing and substantiation performances with the reference solutions. The small values of the absolute error around 10−07 to 10−08 and different statistical operator performances based on the error histogram values, transitions of state investigations, correlation and regression tests also approve the accuracy of the proposed technique.

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来源期刊

International Journal of Computer Mathematics 数学-应用数学

CiteScore

3.60

自引率

0.00%

发文量

审稿时长

5 months

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