分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed. D.
分析了动态递归神经网络系统辨识的参数学习算法。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed.
分离系统的线性部分和非线性部分参数学习都采用自然梯度算法。
The natural gradient method is applied for parameter learning of the linear and nonlinear parts of the separating system.
在网络参数学习的同时网络结构也在进行调整，使得误差不断减小。
Network structure is adjusted with networks parameter learning, which could reduce error.
最后讨论了离散尺度与小波核函数的构造，核函数选择与核参数学习。
Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
研究了贝叶斯网络的学习问题，包括贝叶斯网络结构学习和贝叶斯网络参数学习。
The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks.
本文介绍和比较了概率参数学习的各种常用方法，并探求了它们在不同应用背景下的优缺点。
This paper introduces various common methods in probability data learning and make comparison among them under various application background.
提出了部分层学习算法，并推导出隶属度函数的参数学习算法，改善了诊断规则和学习性能。
Meanwhile, parameter learning algorithm of the membership function is developed. Both of them improve diagnostic rules as well as learning properties.
详细分析了贝叶斯网络的建模过程，即贝叶斯网络的结构学习过程和贝叶斯网络的参数学习过程。
In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly.
该模型无需事先确定模糊控制规则，并能通过神经网络的结构及参数学习调整模糊神经网络的结构。
By using this model, people need not select any fuzzy logic in advance, and can adjust the network structure by the structure and parameter learning of the neural network.
对贝叶斯网络的参数学习进行了探讨，结合实例统计和相关性分析建立了车身偏差诊断的贝叶斯网络模型。
Parameter study of Bayesian network is investigated. According to the methods of example statistics and correlation analysis, Bayesian diagnosis model of body deviation is established.
理论分析说明这种模糊规则后件参数学习算法是收敛的、所建模糊模型能够以要求的精度逼近已知的实验数据。
The learning algorithm and the characteristics of the fuzzy rules model which can approximate the experiment data are shown to converge to any arbitrary accuracy by the theoretical analysis.
对基于不完备样本集的参数学习，则首先计算参数值的上限值和下限值，再利用某种策略，如平均值法，得到参数的最终估计值。
The upper limit and the lower limit is computed first when inducing the CPTs from uncompleted dataset, and the point value is estimated by some method such as averaging.
提出了一个基于混合智能的电火花加工电参数学习模型，它模仿熟练操作者的决策过程，由工艺数据库、加工规则库、学习模块和推理模块组成。
A learning model with hybrid intelligence for the electrical parameter in EDM which imitates a decision making process of a skilled operator was described.
31岁那年，我开始了在法国图卢兹大学学习数学的新生活，在这之后我完成了我的博士论文，是关于 参数化
Then at the age of 31, I started a new life with basic studies in mathematics at the University of Toulouse, France.
提出了对随机事件概率分布参数进行自学习的方法，把知识化制造单元中的不确定因素纳入任务控制的数学模型。
The uncertain factors of the knowledgeable manufacturing cell were included in the task control model by utilizing a self-study method of probability distribution parameters of stochastic events.
提出了对随机事件概率分布参数进行自学习的方法，把知识化制造单元中的不确定因素纳入任务控制的数学模型。
The uncertain factors of the knowledgeable manufacturing cell were included in the task control model by utilizing a self-study method of probability distribution parameters of stochastic events.
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