其次，实验中测得了大量的混沌数据，在神经网络模型的启发下提出了一种新的符号序列去噪算法，应用该算法提高了测量精度。

Secondly, we have obtained plenty of chaotic data, and presented a new method derived from Neural Network theory to process the symbolic series, which improves the accuracy of measurement.

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最后阐述应用rbf神经网络进行基于混沌的语音信号非线性处理。

Then RBF neural network used in nonlinear processing of speech signals based on chaos aspects is presented.

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对于一个经诊断为混沌的统计量序列，应用神经网络建立模型，短期预测混沌序列。

Short term predictions of chaotic series are realised with neural network model, after diagnosing the time series as chaotic statistic series.

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