本文提出一种基于神经网络的多维自回归模型(AR,NLAR)参数估计方法。
A method of parameter estimation for multi-dimension autoregressive models (ar, NLAR) via neural network is given in this paper.
本文提出一种估计自回归ar参数的新算法。
A new algorithm for autoregressive ar parameters estimation is presented in this paper.
结果表明，动态自回归模型时变参数（时变系数）的变化是有规律的，其增量大体上是一些简单周期函数的叠加。
The results showed that the change of time-varying parameters (coefficient) in dynamic AR model has a regularity. Its increments are piled up by some simple period functions.
自回归(AR)参数模型是传统的肌电信号时域分析方法。
Parametric Autoregressive (ar) model is the traditional time-domain EMG signal analyzing method.
文中给出RTVAR模型和GRTVAR模型参数的估计方法，并建立广义回归—时变自回归预测公式。
The parameter estimation of RTVAR and GRTVAR models and the GRTVAR prediction formulas are also established.
用于描述石墨形态的特征由分形维、粗细参数和二维自回归系数共同组成。
Parameters describing character of gray cast graphite morphology such as fractal dimension, roughness and regression coefficients were used to the classification.
文中介绍了一种基于时变自回归模型的归一化参数自适应匹配滤波算法。
In this paper, an algorithm of normalized parametric adaptive matched filter based on time-varying autoregressive model is introduced.
非参数自回归模型因其能够描述许多数据自身所体现的非线性特征而受到人们的广泛关注。
Nonparametric autoregressive model have gained much attention recently, due primarily to the fact that they can describe some nonlinear features exhibited by many data itself in applications.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出一种 自适应 自回归（ADAR）预测模型，可根据谐波过程特性变化 自适应 调整 自回归预测模型的参数乃至结构。
ADAR(ADaptive AR) predicting model is presented, whose parameters and exponent number can be adaptively tuned according to the characteristic variation of harmonic.
数值模拟结果和实际地震数据处理结果表明：自回归滑动平均（ARMA）模型比滑动平均（MA）模型具有参数节省、模型更为高效的特点；
The results of numeric simulation and real seismic data processing showed that the ARMA model was characterized by parameter-economic and high efficiency in comparison with MA model;
主要研究结果如下：1。基于几种非参数估计理论，构造了非参数自回归模型条件方差函数的非参数估计表达式。
The main contributions are as follows:1. Based on several nonparametric estimation theories, several estimation expressions of conditional heteroscedastic function are constructed.
主要研究结果如下：1。基于几种非参数估计理论，构造了非参数自回归模型条件方差函数的非参数估计表达式。
The main contributions are as follows:1. Based on several nonparametric estimation theories, several estimation expressions of conditional heteroscedastic function are constructed.
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