The chaotic BP algorithm is applied to nonlinear system modeling to obtain the optimal approximation in the global sense.
将混沌BP算法应用于非线性系统建模，以求获得全局意义下的最优逼近。
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。
When the output vector dimensions equal to the input vector dimensions, the necessary and sufficient conditions for decoupling and complete linearization to nonlinear system are given.
同时给出了输入输出维数相等时的解耦并完全线性化的充要条件。
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