Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Department of Botany, Hazara University, Mansehra, Pakistan.
Comput Math Methods Med. 2021 Oct 4;2021:2536720. doi: 10.1155/2021/2536720. eCollection 2021.
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.
本文旨在介绍一种基于 Levenberg-Marquardt 反向传播神经网络 (LMBNNs) 的随机求解器，用于解决非线性宿主-向量-捕食者模型。该非线性宿主-向量-捕食者模型依赖于五类，即宿主植物的易感/感染种群、易感/感染向量种群以及捕食者种群。通过使用验证、测试、样本数据和训练，对三种不同类型的非线性宿主-向量-捕食者模型的 LMBNN 求解器的数值性能进行了观察。这些数据的比例分别选择为较大的部分，即 80%用于训练，10%用于验证和测试。通过 LMBNNs 对非线性宿主-向量-捕食者模型进行数值处理，并使用参考解进行了比较研究。使用 LMBNNs 减少均方误差 (MSE) 来表示模型的结果。通过 MSE、误差直方图 (EHs) 和回归/相关性等比例措施，对 LMBNNs 的性能、准确性、一致性和有效性进行了数值评估。
