1) L-M Bayesian regulation
L-M贝叶斯正则化
2) Bayesian regularization
贝叶斯正则化
1.
The predictive models using Bayesian regularization neural network and grey model are established for the H+ concentration in the HPO, two kinds of model are compared and associated to establish model and to predict.
以某己内酰胺厂磷酸羟胺(HPO)的制备的现场数据为基础,利用贝叶斯正则化神经网络和灰色模型建立了磷酸羟胺中的H+浓度的预测模型;比较了神经网络和灰色模型的差异,并把两者结合起来,建立模型进行预测。
2.
This paper compares the results by standard BP algorithm with that of Bayesian regularization together with LM algorithm.
比较了标准的BP算法和用贝叶斯正则化与Levenberg-Marquardt算法相结合的改进BP网络训练的结果。
3.
The BPNN model of Bayesian regularization method was adopted to create the adaptivity and generalization of BPNN.
基于混沌退火算法和BPNN模型的末敏弹系统效能参数优化,引入贝叶斯正则化方法的BPNN模型,使神经网络具有自适应性和推广能力。
3) Bayes regularization
贝叶斯正则化
1.
The Bayes regularization method is employed to get a well generalized neural networks, the redundant and irrelevant attributes can be deleted from the prime attributes set by pruning the input node of the networks.
采用贝叶斯正则化方法训练 ,以得到推广性优良的神经网络 ,并提出启发性的遗传算法。
2.
This paper establishes dynamic forward feedback correction model with the method of combining Bayes regularization and BP neural network.
文中采用贝叶斯正则化与BP网络结合的方法,建立动态前馈校正模型。
3.
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.
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。
4) LM Bayesian regularization algorithm
LM贝叶斯正则化算法
5) Bayesian regularization algorithm
贝叶斯正则化算法
1.
The Levenberg-Marquart(L-M) Bayesian regularization algorithm is combined with the back-propagation(BP) neural network to make the BP network achieve better generalization,faster speed of convergence and higher learning accuracy.
将BP神经网络和L-M贝叶斯正则化算法相结合,可使BP神经网络在推广能力、收敛速度和逼近精度上能够获得很大的提高。
6) Bayesian-Regularization Algorithm
贝叶斯正则化(BayesianRegularization)算法
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