实例分析证明，广义回归网络模型可以应用于疾病预测数据处理工作，并可以取得更优的分析结果。
The example analysis proves that GRNN model can be used in the data processing of the disease forecasting.
针对广义回归网络在时变环境下难以确定平滑因子，自适应能力弱的缺点，提出了一种基于贡献率的选择优化方案。
For the defect that it is hard to determine the smoothing parameter in time-varying conditions, the adaptive optimizing strategy based on contributing ratio is proposed.
考虑到粘弹性材料阻尼性能随环境的非线性变化，运用GRNN(广义回归网络)对粘弹阻尼材料动态力学性能函数进行逼近，并构建预测模型。
Considering the non-linear behavior of the viscoelastic material according to the change of environment, the GRNN is used to make a model to predict the dynamic property of the material.
广义回归神经网络在逼近能力、分类能力和学习速度方面具有较强优势。
General regression neural network is proved with certain superiority in the ability of approaching, classification and learning speed.
为了实现制浆蒸煮终点的精确预测，建立了基于广义回归神经网络(GRNN)的预测模型。
A model based on general regression neural networks (GRNN) has been established to predict the end point of batch pulping cooking.
针对诊断传感器偏置故障与漂移故障的难点问题，提出了一种基于广义回归神经网络(GRNN)的传感器故障诊断方法。
Aimed at solving the challenging problem of diagnosis for sensor bias and drift faults, a novel approach of sensor fault diagnosis based on generalized regression neural network (GRNN) is proposed.
广义回归神经网络（GRNN）和遗传算法（GA）都是在模拟人的生理活动进而提出的人工智能技术。
The generalized regression neural network(GRNN) and the genetic algorithm(GA) are regarded as the artificial intelligence techniques.
使用了广义回归神经网络预测视线的位置，从而提取视线运动参数；
And In order to extract gaze features, the Generalized Regression Neural Networks is used for gaze position prediction;
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
再以广义回归神经网络建立预测模型，与灰预测模型、多元回归模型进行预测能力及报酬率的比较分析。
It is found that it is better to predict the return rate with general regression neural network than with grey prediction and multiple regression model.
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。
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).
本案例采用结合模糊聚类和广义神经网络回归的聚类算法对入侵数据进行分类。
This case USES combined with fuzzy clustering and generalized regression neural network clustering algorithm for intrusion data classification.
结果表明，在训练集样本数据较少时，广义回归神经网络的预测准确度仍然很高。
The results showed that the prediction accuracy is satisfied, even though there are a few data in training sets.
结果证明广义回归神经网络用于交通运输量预测的有效性。
The result demonstrates the effectiveness of using GRNN to forecast transport volume.
结果证明广义回归神经网络用于交通运输量预测的有效性。
The result demonstrates the effectiveness of using GRNN to forecast transport volume.
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