Adaptive Weighted Performance Criterion for Artificial Neural Networks

机译：人工神经网络的自适应加权性能标准

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摘要

Extended Weighted Performance Criterion (EWPC) which is motivated from Weighted Information Criterion (WIC) has been shown promising results in previous study and results of the application showed that EWPC is capable of giving noticeable performance among other measures. EWPC includes MSE, RMSE, R4MS4E, MAPE, MAE, GMAE, MdAE, MdAPE, NS, MRAE, MdRAE, GMRAE, RMSPE, RMdSPE, SMAPE, SMdAPE, MSMAPE, MASE, and RMSSE with fixed coefficients. In this study, we attempt to improve the performance of EWPC by using adaptive coefficients. The application study which consists of several simulated and real-world time series data is utilized for showing the performance of the criterion. Comparisons of the results show that Adaptive Weighted Performance Criterion (AWPC) is quite preferable as a consistent measure for model selection in Artificial Neural Networks.

机译：在先前的研究中，以加权信息标准（WIC）为动力的扩展加权性能标准（EWPC）已显示出令人鼓舞的结果，而应用程序的结果表明EWPC能够在其他措施中提供显着的性能。 EWPC包括MSE，RMSE，R4MS4E，MAPE，MAE，GMAE，MdAE，MdAPE，NS，MRAE，MdRAE，GMRAE，RMSPE，RMdSPE，SMAPE，SMdAPE，MSMAPE，MASE和RMSSE（具有固定系数）。在这项研究中，我们尝试通过使用自适应系数来改善EWPC的性能。由几个模拟的和真实的时间序列数据组成的应用研究用于显示该标准的性能。结果比较表明，自适应加权性能标准（AWPC）作为在人工神经网络中进行模型选择的一种一致措施非常可取。

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《International Conference on Artificial Intelligence and Data Processing》|2018年|1-4|共4页
会议地点 Malatya(TR)
作者
Cagatay BAL; Serdar DEMIR;
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Statistics Department Mugla Sitki Kocman University Mugla Turkey;

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Artificial neural networks; Time series analysis; Forecasting; Correlation; Adaptation models; Neurons;

机译：人工神经网络;时间序列分析；预测;相关性适应模型；神经元;

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Adaptive Weighted Performance Criterion for Artificial Neural Networks

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