Zulqurnain Sabir , Hafiz Abdul Wahab , Mohamed R. Ali , Shahid Ahmad Bhat
{"title":"Meyer小波神经网络程序预测，受电弓和延迟奇异模型","authors":"Zulqurnain Sabir , Hafiz Abdul Wahab , Mohamed R. Ali , Shahid Ahmad Bhat","doi":"10.1016/j.iswa.2024.200457","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div><div>Mathematics Subject Classification. Primary 68T07; Secondary 03D15, 90C60.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"26 ","pages":"Article 200457"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Meyer wavelet neural networks procedure for prediction, pantograph and delayed singular models\",\"authors\":\"Zulqurnain Sabir , Hafiz Abdul Wahab , Mohamed R. Ali , Shahid Ahmad Bhat\",\"doi\":\"10.1016/j.iswa.2024.200457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div><div>Mathematics Subject Classification. Primary 68T07; Secondary 03D15, 90C60.</div></div>\",\"PeriodicalId\":100684,\"journal\":{\"name\":\"Intelligent Systems with Applications\",\"volume\":\"26 \",\"pages\":\"Article 200457\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligent Systems with Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667305324001315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Systems with Applications","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667305324001315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Meyer wavelet neural networks procedure for prediction, pantograph and delayed singular models
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.
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