| 1. | Microcontroller i o system
微控制器输出入系统 |
| 2. | Ac output module , controller
控制器输出模块 |
| 3. | Controller ac output module
控制器输出模块 |
| 4. | The added neural network can be took on as the offset to the system , so the output of the neural network is constrained as to ensure the whole robustness of the system according the requirement of robustness
增加的神经网络控制器输出可以看作是系统的偏差，根据系统鲁棒性的要求，对神经网络的控制输出进行限制，这样可以确保系统的整体鲁棒性。 |
| 5. | A speed - sensorless control method for bldcm , which applies recurrent fuzzy neural network ( rfnn ) , is presented in this paper based on the dynamic model of bldcm . the rfnn controller is used as a speed controller to mimic the optimized output of the system
本文基于bldcm的动态模型提出了一种性能较好的递归模糊神经网络( rfnn )无速度传感器bldcm控制方法,采用rfnn控制器作为转速控制器来近似最优控制器输出。 |
| 6. | In the proposed method , the controller takes the buffer length as congestion indication , takes sources quality and bandwidth utility as object function so as to learn on line . as the controller outputs , the coding rate for input traffic sources and the corresponding user percentage are used to adjust the cells " arrival rate to the multiplexer buffer . compared with the previous method where cells " arrival rate is tuned only by the encoding rate and the encoding rates for all input traffic sources are regulated in a body , the proposed method guarantee that the quality of cells are optimal while cell loss rate is minimized , which means quality of service is guaranteed
在该方法中，拥塞控制器以缓冲区大小信元作为拥塞指示，以信源质量和带宽利用率作为目标函数进行在线学习，控制器输出包括信源编码率及其对应的用户数在全部用户中所占的百分比，即根据信源编码率及对应的用户百分数调整信源输入流，从而克服了以往拥塞控制方法中仅仅调整编码率带来的对所有信源进行整体调整的缺陷，使控制系统在信元损失率最小情况下确保信源输入流质量最高，从而有效地利用了网络带宽。 |
| 7. | In this dissertation , two kinds of optimization , methods are proposed . firstly , only these linking weights corresponding to the control rules that affect the control performance significantly are updated in order to reduce the compute works and speed up the training progress . secondly , the updating step is adjusted adaptively in accordance with the error and the change of error of the system based on the t - s model to get better performance
针对模糊神经网络控制器一般存在着在线权值调整计算量大、训练时间长、过度修正权值可能导致系统剧烈振荡等缺点，提出了两种模糊神经网络控制器的优化方法：在线自学习过程中仅对控制性能影响大的控制规则相关的权值进行修正，以减小计算量，加快训练速度；基于t - s模糊模型，根据偏差及偏差变化率大小动态自适应调节权值修正步长，抑制控制器输出的剧烈变化，避免系统发生剧烈振荡。 |
| 8. | Based on continuous time system , convergence discussion and testifying were made to iterative learning control algorithm under the condition of constraints . then algorithm a and algorithm b that mentioned before are testified that they can be used under the conditions of that controller output has constraints
本文针对这一情况作了讨论，基于连续时间系统，对控制器输出有限制的情况下的迭代学习算法做了收敛性讨论和证明，并且证明了前面提出的算法a和算法b可用于控制器输出有限制情况下的机械手控制。 |
| 9. | At the same time , this paper summarized the experience on solving the problem of amplitude limitation of the controller output and proposed a method to predict system output by use of the model prediction with feedback correction . the on - line intelligence switch of controller output between the limited amplitude and imc controller output is determined according to the state whether the system output and the predictive output are within the given error range
同时，本文在总结前人对输入受限问题的处理经验的前提下，提出用带反馈校正的模型预测作系统输出预测，根据系统响应和系统预测值是否在给定误差范围内来共同决定控制量在限幅值与内模控制器输出值之间进行在线智能切换。 |
| 10. | The apparatus includes a multimedia processor for performing a multimedia function when a controller is in a sleep mode wherein the controller is converted to an active mode when a specified key signal is input into the controller in the sleep mode and wherein the controller outputs an inactive signal to inactivate the multimedia processor
该装置包括一个多媒体处理器，当控制器处于休眠模式时，该多媒体处理器可以执行多媒体功能，其中当某一特殊关键信号输入到处于休眠模式的控制器时，该控制器可转为激活状态，并且其中所述控制器输出一个去激活信号，去激活多媒体处理器。 |
