Time series prediction with evolvable block-based neural networks
Time series prediction with evolvable block-based neural networks
复制标题
使用可进化的基于块的神经网络进行时间序列预测
DOI：
10.1109/ijcnn.2004.1380192
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
S. Kong
中科院分区：
文献类型：
--
作者：
S. Kong
本文使用基于块的神经网络（BBNNS）介绍了时间序列预测技术。构建模型动力系统可以是时间序列预测问题的一般方法。但是，通常未知的功能形式和过程生成时间序列数据的动力学顺序。 BBNNS是一种可发展的神经网络模型，通过使用进化算法同时优化网络结构和连接权重，提供了基本非线性非线性动力学系统的无模型估计。基准Mackey-Glass时间序列的经验结果表明，进化的BBNN可以以足够的精度预测复杂的动态系统的未来行为。
This paper presents a time series prediction technique using the block-based neural networks (BbNNs). Building a model dynamical system can be a general approach to the time series prediction problem. However, the functional form and the order of the dynamics of the process generating the time series data are usually unknown. BbNNs, an evolvable neural network model with simultaneous optimization of network structure and connection weights by use of evolutionary algorithms, provide a model-free estimation of underlying nonlinear dynamical systems. Empirical results with a benchmark Mackey-Glass time series show that the evolved BbNNs can predict the future behavior of a complex dynamical system with sufficient accuracy.