论文标题
COVID-19基于改进的内部搜索算法和多层馈电神经网络的预测
COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network
论文作者
论文摘要
Covid-19是一种新颖的冠状病毒，于2019年12月在中国武汉内出现。随着全球各地的严重动态爆发的严重动态爆发的危机，预测地图和对确认案例（CS）的分析成为一项至关重要的改变任务。 In this study, a new forecasting model is presented to analyze and forecast the CS of COVID-19 for the coming days based on the reported data since 22 Jan 2020. The proposed forecasting model, named ISACL-MFNN, integrates an improved interior search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-forward neural network (MFNN). ISACL结合了CL策略，以增强ISA的性能并避免捕获本地Optima。通过这种方法，它旨在通过将其参数调整为最佳值来训练神经网络，从而在预测结果上达到高智能水平。研究了世界卫生组织（WHO）报道的COVID-19的官方数据，研究了ISACL-MFNN模型，以分析未来几天的确认案件。通过引入一些指标，包括平均绝对误差（MAE），均方根误差（RMSE）和平均绝对百分比误差（MAPE）以及与其他优化算法的比较，可以通过引入一些指标来验证和评估有关预测模型的性能。提出的模型在受影响最大的国家（即美国，意大利和西班牙）进行了研究。实验模拟表明，拟议的ISACL-MFNN提供了有希望的性能，而不是其他算法，同时预测候选国家的任务。
COVID-19 is a novel coronavirus that was emerged in December 2019 within Wuhan, China. As the crisis of its serious increasing dynamic outbreak in all parts of the globe, the forecast maps and analysis of confirmed cases (CS) becomes a vital great changeling task. In this study, a new forecasting model is presented to analyze and forecast the CS of COVID-19 for the coming days based on the reported data since 22 Jan 2020. The proposed forecasting model, named ISACL-MFNN, integrates an improved interior search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-forward neural network (MFNN). The ISACL incorporates the CL strategy to enhance the performance of ISA and avoid the trapping in the local optima. By this methodology, it is intended to train the neural network by tuning its parameters to optimal values and thus achieving high-accuracy level regarding forecasted results. The ISACL-MFNN model is investigated on the official data of the COVID-19 reported by the World Health Organization (WHO) to analyze the confirmed cases for the upcoming days. The performance regarding the proposed forecasting model is validated and assessed by introducing some indices including the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and the comparisons with other optimization algorithms are presented. The proposed model is investigated in the most affected countries (i.e., USA, Italy, and Spain). The experimental simulations illustrate that the proposed ISACL-MFNN provides promising performance rather than the other algorithms while forecasting task for the candidate countries.
