As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
Based on the switching manifold approach to chaos synchronization, a controlling strategy of adaptive chaotic synchronization based on system identification is presented.
在切换流形控制混沌系统同步的基础上，提出一种基于系统辨识的自适应混沌同步控制策略。
Synchronization and parameters identification of uncertain different-structural chaotic system via active control is researched.
研究了基于主动控制的不确定异构系统之间的同步和参数辨识问题。
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