Finally, a linear predictive analysis is made.
最后，进行线性预测分析。
VSELPC? Vector Sum Excited Linear Predictive Coding?
向量和激励线性预编码?。
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。
This paper systematically analysis the problem of errors of linear predictive coding.
本文对图象线性预测编码的误码问题进行了系统的分析。
It encodes speech or other audio signals in frames using linear predictive analysis-by-synthesis coding.
它使用线性综合分析预测编码在侦中编译语音或其他音频信号。
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。
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。
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。
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。
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。
The CELP algorithm includes many important module such as LP (Linear Predictive) analysis, adaptive codebook, random codebook and gain quantization of code etc.
CELP算法中重要的模块有线性预测(LP)分析，自适应码本，随机码本，码本增益量化的等。
Nonlinear predictive control is realized by the global linear model based roll optimizing, and ontime adjusting using neural network based nonlinear model of the nonlinear system.
全局线性模型用于滚动优化 ，非线性模型用于预测系统输出和校正线性模型 ，实现非线性预测控制。
Nonlinear predictive control is realized by the global linear model based roll optimizing, and on-time adjusting using neural network based nonlinear model of the nonlinear system.
利用全局线性模型进行滚动优化，利用非线性预测模型校正线性模型，实现非线性预测控制。
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。
You can conclude that a simple linear model offers more predictive power than simply using the mean of the Exhaustion scores.
可以得出这样的结论：与仅仅使用消耗值的均值相比，简单线性模型的预测能力更好。
Regression expression reflects the linear relation between responding variable and predictive variable.
回归式反映了响应变量和预测变量间的线性关系。
Generalized predictive control is implemented for a class of nonlinear system by substituting it with a time-varying linear system.
将一类非线性系统等价表示为时变线性系统，研究了非线性系统的广义预测控制。
From the method of predictive linear program based on the method GM (1, 1), we build the center approach grey GM (1, 1) model. Thus we improve the grey predictive linear program.
根据基于GM(1,1)模型的预测型线性规划思想方法，建立了中心逼近式GM(1,1)模型，从而给出对灰色预测型线性规划的改进。
Simulation experiments compared with single predictive control approve this method's feasibility and validity. The ability of dealing with the non-linear system is raised.
最后通过对控制系统的设计与仿真试验，与单一预测控制进行比较，证实了该方法的可行性与有效性。
Model predictive control (MPC), also known as receding horizon control (RHC), is a class of model-based control theories that use linear or nonlinear process models to forecast system behavior.
模型预测控制(MPC)，也称为滚动时域控制(RHC)，是一种基于模型的控制理论，采用线性或非线性模型预测系统的活动。
Firstly, it considers the simple linear model predictive control algorithms.
首先考虑简单的线性预测控制。
Two-step model predictive control is applied, which decomposes the MPC problem into a dynamic optimization problem upon linear model and a static rooting problem of nonlinear algebraic equation.
采用两步法预测控制，即将预测控制问题分解为一基于线性模型的的动态优化问题及一非线性模型的静态求根问题。
The results show that in the simulation of non-linear system model, this framework for Bayesian predictive filter can implement the tracking of simple motion and the orientation prediction.
实验结果表明，在非线性系统模型的仿真中，贝叶斯预测滤波框架能够较好的实现对简单物体运动的跟踪和方位的预测。
Using the step response behavior of inertia process and the superposition theorem of linear system, this paper puts forward a fuzzy predictive model of dead-time processes.
利用惯性过程的阶跃响应特性和线性系统中的叠加原理，提出了纯滞后过程的模糊预估模型。
It is difficult to keep favorable controlling performance of model predictive control (MPC) by linear model due to controlled plant nonlinearity in propylene rectifier.
在丙烯精馏塔的控制中，由于被控对象的非线性特性，采用线性模型的模型预测控制器难以保持良好的控制性能。
Practice has proved that the predictive results of theses methods are not ideal for the complicate non-linear systems such as call centers.
实践证明，对于呼叫中心这类复杂的非线性系统，使用统计学方法预测的效果并不理想。
To overcome the mismatch, with the backward, non-linear characteristics of temperature control of dope production, the paper introduces the predictive controls based on the neural network PID.
为了克服这种不协调现象，针对涂料生产线中温度控制大滞后、非线性等特点，本文在神经网络PID控制的基础上，引入预测控制思想。
Predictive capability between the new model (principal components ANN model) and linear regression model for the same predictors is discussed based on the independent samples and historical samples.
将这种新的预测模型（主成分神经网络预报模型）与同样根据这些预报因子建立的逐步回归预报模型进行了对比分析。
The paper presents an optimal control scheme based on moving optimization principle of model predictive control for linear system with constraints.
以预测控制的滚动优化原理为基础提出了一种约束线性系统的最优控制方法。
A new model predictive controller ABRMPC is presented for constrained linear system with bounded additive perturbation.
针对存在有界外界扰动的有约束线性系统，本文提出了一种新的MPC控制器abrm PC。
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