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 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。
Recent studies on Generalized Congruence Neural Network (GCNN) show that the convergence rate of GCNN is faster than that of BP network.
有关广义同余神经网络（GCNN）的初步研究表明，相对于普通BP网络，GCNN具有很快的收敛速度。
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。
A generalized optimization method based on distributed knowledge - - - - simulated annealing algorithm based on KOHO-NEN neural network is presented in this paper.
本文提出了一种基于分布式知识的广义优化方法——基于KOHONEN神经网络的模拟退火算法。
Two improved algorithm were proposed: neural network generalized predictive control based on LM optimizer and multi parallel network generalized predictive control based on jump predictive.
本文提出了两种改进算法：基于LM优化的神经网络广义预测控制和基于跳步预测的多网络并行广义预测控制。
This case USES combined with fuzzy clustering and generalized regression neural network clustering algorithm for intrusion data classification.
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。
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。
A new type of generalized polynomials neural network was proposed to reconstruct 3d implicit surface from the scattered points.
针对点云数据的三维重建问题，提出了一种隐曲面重构的广义多项式神经网络新方法。
Comparing with the models based on multiple statistic analysis, generalized regress-ion neural network or adapted fuzzy neural network model, it shows better learning precision and generalization.
与多元线性回归、模糊回归和自适应模糊神经网络相比，该模型学习精度高且具有较好的泛化能力，能取得较好的预测效果。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
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