forgetting factor

Method of dynamically selecting forgetting factor in reputation model

声望模型中一种动态选择遗忘因子的方法

This paper adopts adaptive Kalman filter with forgetting factor .

本文研究了基于遗忘因子的自适应滤波算法。

A new strategy based on forgetting factor method is proposed for packet dropout compensation .

针对控制信号丢包，提出了一种新的丢包补偿策略，即基于遗忘因子法的丢包补偿策略。

Convergence Rate of Least Squares Algorithm with Forgetting Factor

变遗忘因子最小二乘算法的收敛速度

A New Method of Identification of Multivariable Systems : Stochastic Approximation Algorithm with Forgetting Factor

一种多变量系统辨识的新方法：带遗忘因子随机逼近法

The self tuning of the forgetting factor and its application to the fault diagnosis

遗忘因子模糊自调整算法及在故障诊断中的应用

On H ∞ Filtering with Forgetting Factor for Airborne SAR Motion Compensation System

带遗忘因子的H∞滤波在机载SAR运补系统中的应用

An identification method with auto-regulation forgetting factor in time-varying and color noise system

自调整遗忘因子的有色噪声时变系统辨识

Dynamical recurrent least square algorithm with forgetting factor is presented to train network parameters .

针对对角回归结构，推导出带遗忘因子的动态递推最小二乘法对网络的参数进行训练。

An identification algorithm with auto-regulation forgetting factor for fast time varing system

一种自动调整遗忘因子的快速时变参数辨识方法

Identification algorithm with adaptive vector forgetting factor

自适应向量遗忘因子辨识算法

The model parameters are identified by using the recursive least squares with forgetting factor method .

控制结构采用广义预测控制器的结构，参数辨识采用带遗忘因子的递推最小二乘法，控制律采用广义预测控制。

Forgetting Factor Algorithm for Aircraft Flutter Modal Parameter Identification

基于遗忘因子算法的飞行器颤振模态参数辨识

Especially forgetting factor ECAB has excellent performance under large cyclic frequency error .

特别是遗忘因子ECAB算法，在循环频率误差较大的情况下，仍能保持很好的性能。

In this paper , an adaptive forgetting factor algorithm is obtained based on on-line equalization error measurements .

该文在获取均衡误差最小的即时过程中推导出一种应用在DFE中的自适应遗忘因子RLS算法。

Kalman gain is improved through variable forgetting factor which will avoid the accumulation of errors .

卡尔曼增益通过变遗忘因子进行改进，避免误差的累积。

Application of the Extented Forgetting Factor Recursive Least Squares Estimator to GPS

推广的遗忘因子递推最小二乘算法在GPS中的应用

The open-closed loop iterative learning method with forgetting factor and its convergence condition are given .

采取带有遗忘因子的开闭环迭代方法，给出其收敛条件。

A Variable Forgetting Factor RLS Algorithm for Cyclostationary Signals

一种针对循环平稳信号的变遗忘因子RLS算法

A new wideband DOA tracking method is proposed . The new method has good tracking ability when the appropriate forgetting factor is selected .

提出了一种宽带信源DOA跟踪的新方法，通过选择合适的遗忘因子，该算法对DOA快速变化的宽带信源有很好的跟踪能力。

Firstly , to limit the particle degeneracy , a new method of Kalman particle filter is proposed by introducing forgetting factor .

通过引入遗忘因子概念，提出了基于遗忘因子的卡尔曼粒子滤波方法，在一定程度上缓解了传统方法在参数估计过程中的粒子衰竭问题。

Forgetting factor stochastic gradient algorithm ( FG algorithm for short ) is presented and its convergence is studied by using stochastic process theory .

提出了时变随机系统的遗忘梯度辨识算法，并运用随机过程理论研究了算法的收敛性。

The way of choosing the forgetting factor is stated so that the minimum upper bound of the parameter estimation error is obtained .

阐述了最佳遗忘因子的选择方法，以获得最小参数估计上界。

An improved algorithm was presented , called forgetting factor CAB ( F CAB ), in which cyclic frequency need not be estimated .

提出一种改进的CAB算法&遗忘因子CAB算法（F-CAB），该算法无需对循环频率进行估计。

The result show that if forgetting factor is equal to 1 , RLS adaptive filter can realize any part of the adaptive inverse control .

研究结果表明，在遗忘因子为1时，RLS自适应滤波器能够很好地实现自适应逆控制中的各个环节。

The algorithm introduced the forgetting factor to get the support vectors at the first training . The number of support vectors is decreased by 28 % .

该算法在第一次小样本训练时引入了遗忘因子，该因子使支持向量数减少了28%。

Due to the real-time feature of dynamic dispatching , the prediction accuracy of transit vehicle running time prediction model can be improved with a forgetting factor .

本文针对动态调度对车辆运行信息的实时性要求高的特点，提出公交车辆运行时间预测模型，并通过衰减因子来提高模型的预测精度。

System parameters were identified by adapting recursion least square method , and the forgetting factor was introduced to improve the model tracing ability of TCS .

利用递推最小二乘法进行了系统系数辨识，并引入遗忘因子以提高TCS的模型跟踪性能。

The algorithm of SOM neural network is improved for better system 's performance . A forgetting factor is used in algorithm in order to accelerate its convergence speed .

为了改善系统的性能，对自组织神经网络的算法进行了改进，利用遗忘因子以加速算法的收敛速度。

A lot of real observation data are used for simulation tests and results show that when forgetting factor is decreased , the one-step forecasting performance can be improved .

针对大量实测数据进行仿真实验，结果表明：减小遗忘因子可以提高一步预测的性能。