Also application of the algorithm to on-line system identification was discussed.
并讨论了该算法在在线系统辨识中的应用。
The results can be used to control the motion of magnetic suspended system directly on some occasion, and provide the precondition for more accurate identification on line also.
辨识的结果可直接用于某些场合下磁悬浮系统的运动控制，也可以在此基础上进行更为精确的在线辨识。
The means of auto tuning PID with the system parameters identification on line is obtained, and a kind of controlling method of auto tuning PID is designed.
在此基础上，利用系统辩识获得的系统传递函数的系统，设计了一种自整定pid控制方法。
In order to improve the track accuracy of the system, the on-line identification parameter method is used and the parameters of controller can be corrected instantly.
为了提高系统的跟踪准确度，笔者提出在线辨识参数的改进无差拍控制策略。
Traditional PI controllers do not have PI on-line parameter identification function, which makes the system can not reach the best control.
传统的PI控制器不具备参数在线辨识功能，这使得系统的性能达不到最优的控制。
From the linearization procedure, a new approach of system identification that is on-line real time modeling and real time feedback control correction can be found.
但研究发现，在这种线性化过程中，包含了一种新的系统辨识思想，那就是在线实时建模一实时反馈控制校正的思想。
Use is made of a quasi-randon coherence function computer to carry out on-line identification for a typical chydroelectric servo position control system.
利用伪随机相关仪对电液系统进行在线辨识已有成功先例。
The system realizes the on-line digital filteration, automatic sequence arrangement, identification, data processing, etc.
系统成功的实现了在线数字滤波、自动排序、自动识别及数据处理等功能。
The system adopts the techniques of initialization of state mapping plane, calibration of unknown pattern and on-line identification and this makes plant condition clustering efficiently.
该系统采用状态映照平面初始化方法、未知模式标定技术和在线识别技术，并结合知识库和规则推理的运用，有效地实现设备状态的分类。
In the paper the principle of on-line automatic identification system of plant condition is studied appling the self-organization network and topography search method in knowledge processing.
本文应用自组织特征映照模型，以知识处理中的地势搜索原理为出发点，研究并提出了在线式设备状态自动识别系统的工作原理。
This model, as a part of the overall design of control system, shall be the base of on-line identification and process optimization in the future.
该模型为控制系统总体设计的一个部分，是下一步进行模型在线识别、寻优的基础。
This paper presents a recursive parameter identification algorithm for the system. Compared with the iterative algorithm, it can avoid the matrix inversion and can be operated on-line.
本文给出这类系统的参数递推辨识算法，克服了迭代算法不能在线运行、需反复矩阵求逆的不足。
The method is a kind of on line adaptive fuzzy reasoning which is deduced based on fuzzy clustering method. The method can be used in parameters identification of time-varying system.
该方法是在原有模糊聚类法的基础上，推导出的在线自适应模糊推理算法，可应用在时变非线性系统参数在线辨识中。
The system identification is based on immune strategy RBFNN, and the residuals are generated by on-line comparing the system model outputs with the actual system outputs.
系统辨识是基于免疫RBF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出的在线比较得到的。
The system identification was based on immune strategy RBFNN, and the residuals were generated by on-line comparing the system model outputs with the actual system ones.
系统辨识基于免疫r BF神经网络，用于故障检测的残差是通过对系统的模型输出与系统的实际输出进行在线比较得到的。
NNI can perform on-line identification of the bending joint and NNC can accomplish real-time adaptation of the coefficient. Therefore, the system has property of self-adaptability.
在NNI对弯曲关节进行在线辨识的基础上，通过对NNC的权系数进行实时调整，使系统具有自适应性。
NNI can perform on-line identification of the bending joint and NNC can accomplish real-time adaptation of the coefficient. Therefore, the system has property of self-adaptability.
在NNI对弯曲关节进行在线辨识的基础上，通过对NNC的权系数进行实时调整，使系统具有自适应性。
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