采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
该方法由三部分组成：主元分析pca、时间延迟神经网络、软测量模型的在线校正。
It is composed of three elements: PCA, time-delay neural network and model updating, where the offline model is trained through the algorithm GABP.
摘要针对一类能够由中立型变延迟非线性微分方程描述的神经网络模型，给出了全局渐近稳定的不依赖于时间延迟的充分条件。
A sufficient condition guaranteeing the global asymptotical stability of the equilibrium point is derived for a class of neural network models with variable delay and neutral type delay.
为了易于分析和应用，许多神经网络模型忽略了神经元之间信息传输所带来的时间延迟。
In order to analyze and apply it easily, the transmission delays are ignored in modeling of most systems.
为了易于分析和应用，许多神经网络模型忽略了神经元之间信息传输所带来的时间延迟。
In order to analyze and apply it easily, the transmission delays are ignored in modeling of most systems.
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