Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
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DOI：
10.1016/j.neucom.2012.01.014
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发表时间：
2012-06-01
期刊：
影响因子：
6
通讯作者：
Zhang, Mengjie
中科院分区：
文献类型：
--
作者：
Chandra, Rohitash;Zhang, Mengjie
协同进化将问题分解为子组件，并采用进化算法来解决它们。协同进化是神经网络进化的有效方法。协同进化中不同的问题分解方法决定了神经网络的分解和编码方式，从而影响其性能。一个好的问题分解方法应该提供足够的多样性，并将相互作用的变量分组，这些变量是神经网络中的突触。神经网络在混沌时间序列预测中已经显示出了良好的效果。本文采用两种问题分解方法训练Elman递归神经网络求解混沌时间序列问题。Mackey-Glass，Lorenz和太阳黑子时间序列被用来证明合作神经进化方法的性能。结果表明，在性能方面的准确性相比，从文献中的一些方法。(C)2012爱思唯尔有限公司版权所有。
Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. A good problem decomposition method should provide enough diversity and also group interacting variables which are the synapses in the neural network. Neural networks have shown promising results in chaotic time series prediction. This work employs two problem decomposition methods for training Elman recurrent neural networks on chaotic time series problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the cooperative neuro-evolutionary methods. The results show improvement in performance in terms of accuracy when compared to some of the methods from literature. (C) 2012 Elsevier B.V. All rights reserved.