A Meyer wavelet neural networks procedure for prediction, pantograph and delayed singular models

Intelligent Systems with Applications Pub Date : 2025-04-18 DOI:10.1016/j.iswa.2024.200457

Zulqurnain Sabir , Hafiz Abdul Wahab , Mohamed R. Ali , Shahid Ahmad Bhat

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Abstract

This work aims the numerical solutions of the nonlinear form of prediction, pantograph, and delayed differential singular models (NPPD-DSMs) by exploiting the Meyer wavelet neural networks (MWNNs). The optimization is accomplished using the local and global search paradigms of active-set approach (ASA) and genetic algorithm (GA), i.e., MWNNs-GA-ASA. An objective function is designed using the NPPD-MSMs and the corresponding boundary conditions, which is optimized through the GA-ASA paradigms. The obtained numerical outcomes of the NPPD-MSMs are compared with the true results to observe the correctness of the designed MWNNs-GA-ASA. The absolute error in good measures, i.e., negligible, for solving the NPPD-DSMs is plotted, which shows the stability and effectiveness of the MWNNs-GA-ASA. For the reliability of the procedure, the performances through different statistical operators have been presented for multiple trials to solve the NPPD-NSMs.

Mathematics Subject Classification. Primary 68T07; Secondary 03D15, 90C60.

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Meyer小波神经网络程序预测，受电弓和延迟奇异模型

本工作旨在利用Meyer小波神经网络（MWNNs）对非线性形式的预测、受电弓和延迟微分奇异模型（NPPD-DSMs）进行数值求解。优化采用活动集方法（ASA）和遗传算法（GA）的局部和全局搜索范式，即MWNNs-GA-ASA。利用nppd - msm和相应的边界条件设计了目标函数，并通过GA-ASA范式进行了优化。将nppd - mmsms的数值结果与实际结果进行了比较，以观察所设计的MWNNs-GA-ASA的正确性。绘制了求解NPPD-DSMs的好度量的绝对误差，即可以忽略不计，这表明了MWNNs-GA-ASA的稳定性和有效性。为了提高该方法的可靠性，在求解nppd - nsm的多次试验中，给出了不同统计算子的性能。数学学科分类。主68 t07;二级03D15, 90C60。

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来源期刊

Intelligent Systems with Applications

CiteScore

5.60

自引率

0.00%

发文量