在贝叶斯神经网络中，贝叶斯正则化技术被用来学习神经网络结构。
In the Bayesian neural network, Bayesian regularization technique has been used to study the structure of neural network.
本文主要研究贝叶斯神经网络在电力负荷预测中的利用，论述了电力负荷预测的根本概念和措施。
This paper studies Bayesian neural network in power load forecasting, load forecasting power of the basic concepts and methods.
在我们的实验中，根据某地区近二年来的电力需求的历史数据，采用了贝叶斯神经网络方法对本地区未来某个时刻的电力负荷进行预测。
In our experiments, the past two years in an area under the demand of the historical data, using a Bayesian neural network in the region a future time to predict the power load.
预测结果表明，贝叶斯神经网络的MAPE和RMSE均小于人工神经网络，贝叶斯神经网络具有更好的性能，它可利用于实际预测工作中。
Forecast results show that Bayesian neural network MAPE and RMSE are less than artificial neural network, Bayesian neural network with better performance, it can be applied to predict the actual work.
贝叶斯正则化方法提高BP神经网络的泛化能力。
Bayes' regularization raises the ability to extend of BP neural network.
通过高精度的数控移动工件台获取密集的样本数据，并在神经网络训练过程中采用贝叶斯正则化方法。
Dense sample data are acquired by using numerical control platform of high precision, and the Bayesian generalization is adopted during training the neural network.
采用贝叶斯正则化神经网络（BRNN）对61种金属晶体结合能进行了预测。
The cohesive energy, of 61 metallic crystalloid is predicted by using Bayesian-Regularization neural networks(BRNN).
提出一种新的贝叶斯组合神经网络模型并将其应用于短期交通流量的预测。
Method named BAYESIAN combined neural network model is proposed for short term traffic flow prediction in this paper.
结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。
A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.
基于相空间重构的非线性预报思想，建立一个时滞的BP神经网络模型，采用贝叶斯正则化方法提高BP网络的泛化能力。
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayes' regularization.
并且与图像分类中统计方法的经典算法贝叶斯分类方法做了比较，结果发现，神经网络分类方法的分类效果要优于贝叶斯方法。
Comparing with Bayes method-the classical algorithm, we conclude that the neural network is better than Bayes method. This paper gives all the procedures of SAR image classification.
本文主要涉及的不确定推理模型包括主观贝叶斯的概率推理模型，可信度理论推理模型，证据理论及其改进推理模型以及神经网络推理模型。
In the paper, the models of uncertain reasoning are focused, such as the reasoning model of Bayes probability, Reliability theory, D-S evidence theory and Neural Network.
目前遥感影像分类的常用模型和算法有统计学方法、神经网络、贝叶斯等。
Current remote sensing image classification models and algorithms commonly used statistical methods, neural networks, Bayesian and so on.
相关反馈方法有许多种，如移动查询向量、修改特征权重、贝叶斯、支持向量、神经网络等。
Such as move query vector, modify the weight of characteristics, Bayesian, SVM, neural and networks.
实验数据表明贝叶斯网络比神经网络更适合解决汉语词义消歧问题。
The experimental data shows that Bayesian network is fitter for solving the Chinese WSD than ANN.
有数学的相似性连接的回归，神经网络和贝叶斯网络。
There are mathematical similarities connecting regression, neural networks and bayesian networks.
在控制系统中，将贝叶斯概率引入到模糊rbf神经网络中，增强了系统的推理能力，提高了飞机各个航道位置的模拟伺服精度。
In the control system, Bayes probability is introduced in the fuzzy RBF neural network and it intensity the inference ability and increase the servo precision.
但贝叶斯网络的抗噪声能力却明显逊色于神经网络。
But Bayesian network's noise-resistant ability is inferior to ANN more or less.
利用灰度共生纹理特征向量和灰度共生-差分维数联合特征向量结合BP神经网络和朴素贝叶斯网络都能对地物进行有效识别，识别率在70%以上。
Ground objects can be effectively recognized by gray co-occurrence vector and gray co - dimension feature vector with BP neural network and Bayesian network, recognition rate of 70%.
讨论了单纯使用BP神经网络作人脸的检测判定的不足，并在此基础上提出利用贝叶斯决策对神经网络的仿真结果进行第二次判定的方法。
Discussed the deficiency in face detection of BP neural network which was single used, and put forward a re-decision method by Bayesian decision.
讨论了单纯使用BP神经网络作人脸的检测判定的不足，并在此基础上提出利用贝叶斯决策对神经网络的仿真结果进行第二次判定的方法。
Discussed the deficiency in face detection of BP neural network which was single used, and put forward a re-decision method by Bayesian decision.
应用推荐