Network structure is adjusted with networks parameter learning, which could reduce error.
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。
In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly.
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。
The parameter learning algorithm of dynamic recurrent neural network based on system identification is analyzed.
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 natural gradient method is applied for parameter learning of the linear and nonlinear parts of the separating system.
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。
Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
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。
The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks.
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。
Meanwhile, parameter learning algorithm of the membership function is developed. Both of them improve diagnostic rules as well as learning properties.
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。
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.
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.
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。
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