Research on residual GM optimization based on PEMEA-BP correction

Abstract With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Ba...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Junhang Duan, Ling Zhu, Wei Xing, Xi Zhang, Zhong Peng, Huating Gou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/36601fd6d750418ab257ba75c8fde968
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:36601fd6d750418ab257ba75c8fde968
record_format	dspace
spelling	oai:doaj.org-article:36601fd6d750418ab257ba75c8fde9682021-12-02T16:18:04ZResearch on residual GM optimization based on PEMEA-BP correction10.1038/s41598-020-77630-w2045-2322https://doaj.org/article/36601fd6d750418ab257ba75c8fde9682020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77630-whttps://doaj.org/toc/2045-2322Abstract With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.Junhang DuanLing ZhuWei XingXi ZhangZhong PengHuating GouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Junhang Duan Ling Zhu Wei Xing Xi Zhang Zhong Peng Huating Gou Research on residual GM optimization based on PEMEA-BP correction
description	Abstract With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.
format	article
author	Junhang Duan Ling Zhu Wei Xing Xi Zhang Zhong Peng Huating Gou
author_facet	Junhang Duan Ling Zhu Wei Xing Xi Zhang Zhong Peng Huating Gou
author_sort	Junhang Duan
title	Research on residual GM optimization based on PEMEA-BP correction
title_short	Research on residual GM optimization based on PEMEA-BP correction
title_full	Research on residual GM optimization based on PEMEA-BP correction
title_fullStr	Research on residual GM optimization based on PEMEA-BP correction
title_full_unstemmed	Research on residual GM optimization based on PEMEA-BP correction
title_sort	research on residual gm optimization based on pemea-bp correction
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/36601fd6d750418ab257ba75c8fde968
work_keys_str_mv	AT junhangduan researchonresidualgmoptimizationbasedonpemeabpcorrection AT lingzhu researchonresidualgmoptimizationbasedonpemeabpcorrection AT weixing researchonresidualgmoptimizationbasedonpemeabpcorrection AT xizhang researchonresidualgmoptimizationbasedonpemeabpcorrection AT zhongpeng researchonresidualgmoptimizationbasedonpemeabpcorrection AT huatinggou researchonresidualgmoptimizationbasedonpemeabpcorrection
_version_	1718384162873802752

Research on residual GM optimization based on PEMEA-BP correction

Ejemplares similares