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 example analysis proves that GRNN model can be used in the data processing of the disease forecasting.
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 results show that the GRNN model constructed in this way can precisely forecast urban short-term traffic flow.
研究结果表明，构建的神经网络模型能够很精确地实时预测城市道路短期交通流。
A model based on general regression neural networks (GRNN) has been established to predict the end point of batch pulping cooking.
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。
The generalized regression neural network(GRNN) and the genetic algorithm(GA) are regarded as the artificial intelligence techniques.
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。
When applied to cracker modeling, MEP-GRNN shows advantage for non-linear molding over RBF-PLS approach. It has good prediction accuracy and stability.
该模型用于渣油裂解建模时，其预报精度和稳定性比r BF -PLS等方法均有所提高，表现了MEP - GRNN为非线性过程建模的优势。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
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。
Example with survey data and compare with BP both showed that the GRNN model makes an effective way to forecast multi-point deformation with rapid calculation and high accuracy.
实例计算与比较结果表明，GRNN模型计算快、精度高，是进行多测点非线性变形监测预报的有效工具。
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.
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。
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。
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.
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。
The respective resistance properties for branch pipe were investigated through experiments and GRNN neural network when both conveying velocity and the flow valve opening of branch pipe were changed.
通过试验和GRNN神经网络对输送表观气速和两分支管路流量控制阀开度发生变化时，各分支管路中的阻力特性进行了研究。
The respective resistance properties for branch pipe were investigated through experiments and GRNN neural network when both conveying velocity and the flow valve opening of branch pipe were changed.
通过试验和GRNN神经网络对输送表观气速和两分支管路流量控制阀开度发生变化时，各分支管路中的阻力特性进行了研究。
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