Stamatis N, Parthimos D, Griffith T M
Department of Diagnostic Radiology, University of Wales, College of Medicine, Cardiff, UK.
Int J Neural Syst. 1996 Sep;7(4):417-28. doi: 10.1142/s0129065796000397.
We have explored the potential of an artificial neural network to capture the dynamics of chaotic temporal fluctuations in arterial pressure and flow. Model generated signals that simulate this ubiquitous physiological phenomenon in both form and complexity were used to train a Multilayer Perceptron (MLP) after first locating the optimum time delay to unfold the attractor governing the dynamics. Prediction horizons were maximized with a new stopping criterion capable of continuously tracking the trajectories of the model system. Single-step predictions were consistently good throughout the study. Long-term predictions obtained by using the MLP as a signal generator were very successful when the number of hidden nodes was carefully chosen. Moreover, short- and long-term predictions could also be obtained even when the dynamics was nonstationary.
我们探讨了人工神经网络捕捉动脉血压和血流中混沌时间波动动态的潜力。在首先确定展开控制动态的吸引子的最佳时间延迟后，使用模拟这种普遍存在的生理现象的形式和复杂性的模型生成信号来训练多层感知器（MLP）。通过一种能够持续跟踪模型系统轨迹的新停止准则，预测范围得以最大化。在整个研究过程中，单步预测一直表现良好。当仔细选择隐藏节点的数量时，将MLP用作信号发生器获得的长期预测非常成功。此外，即使动态是非平稳的，也可以获得短期和长期预测。
