With strong noise, the performance of the artificial neural network correction is limited.

但其在既有加性噪声 ，又有乘性噪声时校正效果难以令人满意。

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The traditional neural network correction has a good adaptivity to the noise. But with a stronger low frequency space noise, the correction effect is very poor.

传统的神经网络非均匀性校正算法对噪声具有较好的自适应性，但当空间低频噪声较大时，校正效果明显下降。

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The results validate more validity of nonlinear error correction model on the wavelet neural network than linear vector autoregressive model, and forecast validly the nonlinear economy system.

研究证明，小波神经网络所建立的非线性误差校正模型有较好的预测效果，能够有效地预测非线性经济系统。

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