approximation principles造句

• After introducing the unified model structure and applying stochastic approximation principle , the general recursive identification algorithm of several on - line identification methods is developped
  在引进统一的模型结构以后，利用随机逼近原理，提出了各种在线识别算法的一般递推形式。
• This dissertation presented two new methods of robust adaptive track control for a class of mimo strong nonlinear system with external disturbance . one method makes use of taylor approximation principle to linearize the mimo strong nonlinear system at the ideal equilibrium point , meanwhile external disturbance is considered , and then designs two on - line neural network controller respectively , which can dynamically compensate the high order items of taylor series and the control signals at ideal equilibrium point under the drive of state error between linear and nonlinear system . a linear feedback controller obtained by pole assignment and two on - line neural network act on the practical mimo high nonlinear system together , guaranteeing the whole system robust stable and tracking the specified signal ; the other method designs three on - line neural networks for this class of system
  本文对于一类含有外部扰动的多输入多输出( mimo )强非线性系统，提出了两种新的鲁棒自适应跟踪控制方法，第一种利用了taylor近似的原理，在考虑了外部扰动的情况下，将mimo强非线性系统在理想平衡点处线性化，分别设计了两个在线神经网络控制器，在线性和非线性系统之间的状态误差驱动下动态补偿系统的taylor近似高阶项及理想平衡点处的控制信号，满足极点配置方法的线性反馈控制器和两个在线神经网络联合作用于实际的被控mimo强非线性系统，在保证整个系统鲁棒稳定性的情况下，能够跟踪给定的指令信号；另一种方法是针对这类系统设计了3个在线神经网络，分别实时抵消这类非线性系统中的非线性部分、与控制量耦合的非线性项以及外部扰动，使得受控系统的输出可以完全跟踪给定输入参考信号。