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Artificial neural networks for improving the cost model development process

机译:人工神经网络,用于改进成本模型开发过程

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In order to cope with the changes occurring in U.K. industry within the next decade, it is expected that the quantity, type, accuracy and complexity of cost models will need to be greatly increased. The objective of this study is therefore to improve the speed and effectiveness with which cost models can be developed and applied. This current work is concerned with identifying and examining current mathematical modelling techniques used to establish CER's and determining their relative advantages and limitations in terms of their effects on the overall cost model development process. Experimental results are presented that indicate the advantages of using artificial neural networks (ANN) models when compared with traditional regression based techniques.
机译:为了应对未来十年内英国工业发生的变化,预计将需要大大增加成本模型的数量,类型,准确性和复杂性。因此,本研究的目的是提高开发和应用成本模型的速度和有效性。这项当前工作涉及确定和检查用于建立CER的当前数学建模技术,并确定其相对优势和局限性,从而影响其对整个成本模型开发过程的影响。实验结果表明,与传统的基于回归的技术相比,使用人工神经网络(ANN)模型具有优势。

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