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.
提出用动态回归神经网络建立高速公路宏观交通流模型。
A dynamic recurrent neural network to freeway macroscopic traffic flow modeling is presented.
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.
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;
分析了具有遗忘特性及信息锁存能力的状态回归神经网络的计算方法。
An analysis is made in the paper about the computing method of the recurrent neural networks that has the characteristic of oblivion and the ability of information latching.
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.
最后对回归神经网络和径向基神经网络在故障诊断中的应用进行了初步的研究。
And, the application of Recurrent Network and Radius Basis Network to fault diagnosis is also primarily studied.
本文研究了三层对角回归神经网络(DRNN)用于直流电动机实时控制的方法。
In this paper, three layers of DRNN is used in the real - time identification and control of DC motor.
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.
该文介绍了内回归神经网络逼近非线性ARMA模型、用于时间序列预测的可行性。
This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.
针对复杂多变量系统难以建模的问题，采用多层局部回归神经网络离线建立其预测模型。
Aiming at the difficulties in modeling the complex MIMO system, the multilayer local recurrent neural network is used to build the predictive model of the process off-line.
采用动态对角回归神经网络作为辨识器和控制器，实现了机器人轨迹跟踪的最小误差控制。
Using dynamic recurrent neural networks as identification and controller, the minimum error control of robot tracking the idea locus is implemented.
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.
为了提高卡尔曼滤波估计精度，提出了一种基于回归神经网络补偿卡尔曼滤波器估计误差的方法。
A method based on recurrent neural network compensation Kalman's evaluation error is proposed in order to enhance the evaluation precision.
针对信息科学和控制理论中经常涉及的一类泛函极值问题，提出基于连续回归神经网络的求解方法。
In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control.
采用新型对角回归神经网络来辩识系统模型，可对PID控制器参数进行整定，实现多变量解耦控制。
Adopting new type diagonal regression neural network to identify the system model, the parameters of PID controller have been set, and the multi -variable decoupling control being realized.
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.
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.
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.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
一种新的自适应支持向量回归神经网络(SVR - NN)提出，它结合了分别支持向量机和神经网络的优点。
A novel adaptive support vector regression neural network (SVR-NN) is proposed, which combines respectively merits of support vector machines and a neural network.
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.
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).
针对如何解算n人非合作的动态博弈对策中的纳什均衡解问题，提出一种利用退火回归神经网络极值搜索算法解算纳什均衡解的方法。
An algorithm is proposed to solve the Nash equilibrium solution for ann-person noncooperative dynamic game by an annealing recurrent neural network for extremum seeking algorithm (ESA).
研究了简化型内回归神经网络基于自适应梯度下降法的训练算法，并提出了一种基于简化型内回归神经网络的非线性动态数据校核新方法。
An adaptive gradient descent algorithm for training simplified internally recurrent networks (SIRN) is developed and a new method of reconciling nonlinear dynamic data based on SIRN is proposed.
该预测模型将为这是最好的？利用回归或神经网络的不同形式的利弊是什么？
Which predictive modelling will be best for this? What are the pros and cons of using the different forms of regression or neural networks?
这样设置便可以创建不包含隐藏层的神经网络模型，从而使该神经网络模型与逻辑回归等效。
This setting will create a neural network model that does not contain a hidden layer, and that therefore is equivalent to logistic regression.
具体如回归预测法、指数平滑法、灰色模型预测法、BP神经网络法、RBF神经网络法。
Such as the return of specific prediction method, smoothing index, grey model prediction, BP neural network, RBF neural network .
在此基础上，分别运用多元线性回归和BP神经网络方法研究边坡稳定性预测模型，并将其结果与极限平衡分析方法进行对比。
Based on this, forecast model for slope stability was studied by multivariate linear regression and BP neural network methods, and the results were compared with those by limit equilibrium method.
在此基础上，分别运用多元线性回归和BP神经网络方法研究边坡稳定性预测模型，并将其结果与极限平衡分析方法进行对比。
Based on this, forecast model for slope stability was studied by multivariate linear regression and BP neural network methods, and the results were compared with those by limit equilibrium method.
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