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。
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。
Theoretical analysis indicates that iterative learning control algorithm is robust if initial shift and System parameter disturbance within limited bound.
理论分析表明，当系统状态初值漂移和系统参数扰动在一定范围内，迭代学习控制算法关于是鲁棒的。
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.
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。
The PNN structure was optimized based on statistical results from the PCA for the training samples. A learning algorithm was introduced into the PNN to reduce uncertainties parameter.
以概率乘法公式为理论依据，根据训练样本的PCA结果对PNN进行结构优化，并引入学习算法减小PNN的参数不确定性。
Aiming at this question, this paper proposes a parameter model under evidence loss and deduce an EM updating algorithm which contains learning rate.
针对这样的问题，本文提出一种证据丢失参数模型，并推导出包含学习率的EM更新算法。
Simultaneously, if the system temperature is in a temporary state of stability, perfects the parameter U0 based on the self-learning algorithm.
同时不断判断系统温度是否处于暂时稳定状态，如果是，则启动自学习算法，对U0进行修正。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
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