设计了基于BP神经网络的PID控制器，实现PID控制器参数自学习、自整定。
The PID controller based on BP neural networks is designed to realize control parameter self-learning and self-adjusting.
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.
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.
新控制器在控制过程中借助模糊神经网络的自学习算法实现控制参数的在线调整。
The parameters of new controller can be adjusted on line based on the ability of fuzzy ne ural network.
该方法利用RBF神经网络的自学习、自适应能力自调整系统的控制参数。
This method uses the liability of self-study and self-adaptability of RBF network to turning parameters of system.
该控制器将神经网络和PID控制规律融为一体，既具有常规PID控制器结构简单、参数物理意义明确的优点，又具有神经网络自学习、自适应的能力。
It has the simple structure and definite physics meaning parameters as a regular PID controller, and has the self study ability as neural nets.
介绍了余热处理计算机控制系统的在线温度预报模型、设定模型和参数跟踪自学习模型的建立方法。
The method of establishing on-line temperature prediction model, set point model and parameter tracing self-learning model of computerized heat recovery processing control system is introduced.
通过神经网络的自学习、实现PID控制参数的自适应调整。
The adapted-self tune-up for PID control was completed through learning-self of neural network.
在电梯正常运行前必须自学习，及当平层磁条的位置或系统参数有有变动后亦须重新进行一次自学习运行。
Before the unit runs in Normal operation a Learn run has to be performed. A repeatition of the Learn run is also necessary if a door zone magnet was removed or if the parameter CON SPE was changed.
给出一种基于FCMAC的自学习控制器的结构及合适的学习算法，这种网络每次学习少量参数，算法简单。
FCMAC based controller structure and a simple learning algorithm were also proposed. In the learning algorithm only small parts of parameters of the FCMAC were adjusted at each learning iteration.
实验证明，利用粒子群算法对评估函数进行参数优化是可行的，通过大量的训练后这种算法不但有效地提高了象棋程序的水平而且使象棋具有了自学习的能力。
The result of experience has proved the method using PSO to optimize the evaluation function is effectively to enhance the strength of the Chinese-chess program by self-learning.
实验证明，利用粒子群算法对评估函数进行参数优化是可行的，通过大量的训练后这种算法不但有效地提高了象棋程序的水平而且使象棋具有了自学习的能力。
The result of experience has proved the method using PSO to optimize the evaluation function is effectively to enhance the strength of the Chinese-chess program by self-learning.
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