Forecasting time series with a logarithmic model for the Polynomial Artificial Neural Networks

J. C. Luna-Sanchez, E. Gómez-Ramírez, K. Najim, E. Ikonen
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引用次数: 2

Abstract

The adaptation made for the Polynomial Artificial Neural Networks (PANN) using not only integer exponentials but also fractional exponentials, have shown evidence of its better performance, especially, when it works with non-linear and chaotic time series. In this paper we show the comparison of the PANN improved model of fractional exponentials with a new logarithmic model. We show that this new model have even better performance than the last PANN improved model.
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多项式人工神经网络的对数模型预测时间序列
利用整数指数和分数指数对多项式人工神经网络(PANN)进行自适应,证明了它具有更好的性能,特别是在处理非线性和混沌时间序列时。本文给出了改进的分数阶指数泛神经网络模型与一种新的对数模型的比较。结果表明,该新模型的性能优于先前的改进模型。
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