时间序列预测前馈网络协同进化的改进神经元-突触级问题分解方法

Ravneil Nand, B. Sharma
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引用次数: 1

摘要

通过使用问题分解对特定问题进行分解,可以有效地解决复杂问题。即使将不同的问题分解方法组合在一起,在时间序列预测中也显示出良好的效果。在网络的不同阶段应用不同的问题分解方法,可以发挥各自的优势,更好地解决手头的问题。两种不同的问题分解方法的混合版本在过去已经显示出有希望的结果。本文提出了一种改进的神经元-突触级问题分解方法,利用前馈神经网络进行时间序列预测。结果表明,改进后的模型在更多的数据集上取得了较好的结果。在某些情况下,与文献中的其他几种方法相比,所提出的方法的结果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modified Neuron-Synapse Level Problem Decomposition Method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction
Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature.
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