Time Series Analysis, Modeling and Applications - A Computational Intelligence Perspective
Time Series Analysis, Modeling and Applications - A Computational Intelligence Perspective
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时间序列分析、建模和应用——计算智能视角
DOI：
10.1007/978-3-642-33439-9_12
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Coyle D
中科院分区：
文献类型：
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作者：
Coyle D
互信息已被发现是一个合适的测量变量之间的依赖输入变量的选择。对于时间序列预测，互信息可以量化包含在时间序列的滞后测量中的平均信息量。信息量可用于选择最佳时滞τ和嵌入维数Δ，以优化预测精度。通过模糊和循环神经网络（FNN和RNN）等传统和计算智能技术进行时间序列建模和预测已被推广用于脑电预处理和特征提取，以最大限度地提高信号可分性，从而提高脑机接口（BCI）系统的性能。这项工作表明，空间上不同的EEG通道有不同的最佳时间嵌入参数的变化和发展，这取决于类的运动想象（运动想象）正在处理。为了确定最佳的时间嵌入每个EEG通道（时间序列）为每个类的方法的基础上估计的部分互信息（PMI）。PMI选择的嵌入参数用于在基于自组织模糊神经网络（SOFNN）的预测器被专业化以在基于预测的信号处理框架中预测通道和类别特定数据之前嵌入每个通道和类别的时间序列，称为神经时间序列预测预处理（NTSPP）。18个主题的结果表明，主题，通道和类特定的最佳时间嵌入参数选择使用PMI改进NTSPP框架，增加时间序列的可分性。本章还展示了如何将一系列传统的信号处理工具与多种基于计算智能的方法相结合，包括SOFNN和实用群优化（PSO），以开发一种更自主的参数优化设置，并最终开发出一种新颖且更准确的BCI。
Mutual information has been found to be a suitable measure of dependence among variables for input variable selection. For time-series prediction mutual information can quantify the average amount of information contained in the lagged measurements of a time series. Information quantities can be used for selecting the optimal time lag,τ, and embedding dimension, Δ, to optimize prediction accuracy. Times series modeling and prediction through traditional and computational intelligence techniques such as fuzzy and recurrent neural networks (FNNs and RNNs) have been promoted for EEG preprocessing and feature extraction to maximize signal separability to improve the performance of brain-computer interface (BCI) systems. This work shows that spatially disparate EEG channels have different optimal time embedding parameters which change and evolve depending on the class of motor imagery (movement imagination) being processed. To determine the optimal time embedding for each EEG channel (time-series) for each class an approach based on the estimation of partial mutual information (PMI) is employed. The PMI selected embedding parameters are used to embed the time series for each channel and class before self-organizing fuzzy neural network (SOFNN) based predictors are specialization to predict channel and class specific data in a prediction based signal processing framework, referred to as neural-time-seriesprediction- preprocessing (NTSPP). The results of eighteen subjects show that subject-, channel- and class-specific optimal time embedding parameter selection using PMI improves the NTSPP framework, increasing time-series separability. The chapter also shows how a range of traditional signal processing tools can be combined with multiple computational intelligence based approaches including the SOFNN and practical swarm optimization (PSO) to develop a more autonomous parameter optimization setup and ultimately a novel and more accurate BCI.