Research on linear predictive control has become mature and linear MPC has gained wide application in industrial processes.
基于线性模型的模型预测控制研究已经相当成熟并得到了广泛的工业应用。
Finally, a linear predictive analysis is made.
最后，进行线性预测分析。
Al present, the linear predictive coding (LPC) is the best method for voice analysis.
线性预测编码(LPC)是当今语音分析中最好的方法。
And then a set of SISO linear predictive control algorithms was constructed to be used on the closed-loop system.
然后对此闭环系统设计一组单变量的线性预测控制算法以获得良好的动态特性。
LSP parameters can be obtained by solving an N-order non-linear equation relating to the linear predictive coding (LPC) coefficients.
LSP参数可以通过求解一个与线性预测系数(LPC)相关的非线性N阶方程得到。
The model of the nonlinear system is obtained by LS-SVM, the offline model is linearize at each sampling instant and uses linear predictive function control methods to obtain the control law.
该算法采用LS-SVM回归建立非线性系统的预测模型，然后，将离线模型在每个采样周期关于当前采样点进行线性化，同时利用线性预测函数控制方法求解解析的控制律。
The Low-Delay Code-Exited Linear Predictive (LD-CELP) Speech Coder can combine high quality with low delay at quite low bit rates.
低时延码激励线性预测(LD - CELP)语音编码能够在较低速率把高质量与低时延结合起来。
Code-Excited Linear Predictive (CELP) coding can provide natural speech quality at medium and low bit rates in contrast with conventional LPC vocoder.
码激励线性预测（CELP）编码在中低速率上与传统的LPC声码器相比可以提供更加自然的语音质量。
Code-Excited Linear Predictive (CELP) coding can provide natural speech quality at medium and low bit rates in contrast with conventional LPC vocoder.
码激励线性预测（CELP）编码在中低速率上与传统的LPC声码器相比可以提供更加自然的语音质量。
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