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。
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。
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。
The RBF network function approximation theory and method are introduced, and the method of nonlinear error correction of sensor is presented based on generalized regression neural network(GRNN).
介绍了径向基函数网络的函数逼近原理和方法，提出了一种基于广义回归神经网络（GRNN）的传感器非线性误差校正方法。
Based on neural network of capacitive pressure sensor nonlinear correction.
基于神经网络的电容式压力传感器非线性校正。
Secondly, according to the change of damage structural frequency and modal, the fixity factor is identified by RBF neural network, then FEM correction of truss is finished.
其次，根据损伤后的结构频率与模态的变化，应用径向基神经网络，进行结构节点固结系数的识别，从而实现对网架结构有限元模型的修正。
A predictive correction model for thermal characteristics of heat-pipe evacuated tubular solar collector was established based on BP neural network.
基于BP神经网络建立了热管式真空管集热器热性能的预测校正模型。
In this paper, the principle of nonlinear correction of data by using RBF neural network, the structure of neural network and the determination method of parameters are described.
介绍了利用RBF神经网络进行数据非线性校正的原理以及RBF神经网络结构和参数的确定方法。
Use the image illumination correction, reduce the dimension of face images and different lighting conditions, the use of human face images improved the BP neural network for recognition.
运用了图像进行光照校正，人脸图像进行降维及不同的光照条件下的人脸图像运用改进型的BP神经网络对进行识别。
This paper establishes dynamic forward feedback correction model with the method of combining Bayes regularization and BP neural network.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
This paper establishes dynamic forward feedback correction model with the method of combining Bayes regularization and BP neural network.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
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