Estimation of industrial production costs, using regression analysis, neural networks or hybrid neural-regression method

IF 1.1 Q1 MATHEMATICS
E. Abounoori, M. Bagherpour
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引用次数: 6

Abstract

Abstract Estimation (Forecasting) of industrial production costs is the most important factors affecting decisions in the highly competitive markets. Thus, improved accuracy of estimation is highly desirable. Comparing regression analysis and neural networks with hybrid neuralregression method, has clearly indicated the superiority of the latter method.
估计工业生产成本,采用回归分析、神经网络或混合神经回归方法
摘要在高度竞争的市场中,工业生产成本的估算(预测)是影响决策的最重要因素。因此,提高估计的准确性是非常可取的。将回归分析和神经网络与混合神经回归方法进行比较,明显表明了混合神经回归方法的优越性。
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来源期刊
CiteScore
2.70
自引率
23.50%
发文量
141
期刊介绍: The Journal of Interdisciplinary Mathematics (JIM) is a world leading journal publishing high quality, rigorously peer-reviewed original research in mathematical applications to different disciplines, and to the methodological and theoretical role of mathematics in underpinning all scientific disciplines. The scope is intentionally broad, but papers must make a novel contribution to the fields covered in order to be considered for publication. Topics include, but are not limited, to the following: • Interface of Mathematics with other Disciplines • Theoretical Role of Mathematics • Methodological Role of Mathematics • Interface of Statistics with other Disciplines • Cognitive Sciences • Applications of Mathematics • Industrial Mathematics • Dynamical Systems • Mathematical Biology • Fuzzy Mathematics The journal considers original research articles, survey articles, and book reviews for publication. Responses to articles and correspondence will also be considered at the Editor-in-Chief’s discretion. Special issue proposals in cutting-edge and timely areas of research in interdisciplinary mathematical research are encouraged – please contact the Editor-in-Chief in the first instance.
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