{"title":"新型广义非线性分数灰色伯努利模型及其应用","authors":"","doi":"10.1016/j.aej.2024.08.096","DOIUrl":null,"url":null,"abstract":"<div><p>Considering that the existing fractional order grey prediction models are difficult to directly handle the shortcomings of seasonal time series, a novel generalized nonlinear fractional grey Bernoulli model capable of handling both seasonal and conventional time series is constructed. The structure of the new model adopts a more flexible nonlinear Bernoulli equation and a novel adaptive fractional accumulation operation, which endows it with stronger nonlinear fitting capabilities. Furthermore, the introduction of a dynamic parameter endows it with the capability to handle both seasonal and conventional time series simultaneously. Specifically, the structural parameters of the model are no longer obtained through traditional least squares method but instead through a moving average trend removal method and intelligent optimization algorithms, which greatly improves the computational efficiency of the model. Therefore, the practicality of the novel model surpasses that of all existing fractional grey prediction models. Experimental results on two types of datasets demonstrate that the proposed method outperforms existing machine learning models, fractional grey prediction models and statistical prediction model in terms of generalization ability, validating its effectiveness.</p></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110016824009931/pdfft?md5=a4a4145151029818122497c79081e9c7&pid=1-s2.0-S1110016824009931-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A novel generalized nonlinear fractional grey Bernoulli model and its application\",\"authors\":\"\",\"doi\":\"10.1016/j.aej.2024.08.096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Considering that the existing fractional order grey prediction models are difficult to directly handle the shortcomings of seasonal time series, a novel generalized nonlinear fractional grey Bernoulli model capable of handling both seasonal and conventional time series is constructed. The structure of the new model adopts a more flexible nonlinear Bernoulli equation and a novel adaptive fractional accumulation operation, which endows it with stronger nonlinear fitting capabilities. Furthermore, the introduction of a dynamic parameter endows it with the capability to handle both seasonal and conventional time series simultaneously. Specifically, the structural parameters of the model are no longer obtained through traditional least squares method but instead through a moving average trend removal method and intelligent optimization algorithms, which greatly improves the computational efficiency of the model. Therefore, the practicality of the novel model surpasses that of all existing fractional grey prediction models. Experimental results on two types of datasets demonstrate that the proposed method outperforms existing machine learning models, fractional grey prediction models and statistical prediction model in terms of generalization ability, validating its effectiveness.</p></div>\",\"PeriodicalId\":7484,\"journal\":{\"name\":\"alexandria engineering journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1110016824009931/pdfft?md5=a4a4145151029818122497c79081e9c7&pid=1-s2.0-S1110016824009931-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"alexandria engineering journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1110016824009931\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"alexandria engineering journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110016824009931","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
A novel generalized nonlinear fractional grey Bernoulli model and its application
Considering that the existing fractional order grey prediction models are difficult to directly handle the shortcomings of seasonal time series, a novel generalized nonlinear fractional grey Bernoulli model capable of handling both seasonal and conventional time series is constructed. The structure of the new model adopts a more flexible nonlinear Bernoulli equation and a novel adaptive fractional accumulation operation, which endows it with stronger nonlinear fitting capabilities. Furthermore, the introduction of a dynamic parameter endows it with the capability to handle both seasonal and conventional time series simultaneously. Specifically, the structural parameters of the model are no longer obtained through traditional least squares method but instead through a moving average trend removal method and intelligent optimization algorithms, which greatly improves the computational efficiency of the model. Therefore, the practicality of the novel model surpasses that of all existing fractional grey prediction models. Experimental results on two types of datasets demonstrate that the proposed method outperforms existing machine learning models, fractional grey prediction models and statistical prediction model in terms of generalization ability, validating its effectiveness.
期刊介绍:
Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification:
• Mechanical, Production, Marine and Textile Engineering
• Electrical Engineering, Computer Science and Nuclear Engineering
• Civil and Architecture Engineering
• Chemical Engineering and Applied Sciences
• Environmental Engineering