The Influence of Experimental Data Quality and Quantity on Parameter Estimation Accuracy

2006 ◽  
Vol 1 (1) ◽  
pp. 139-145 ◽  
Author(s):  
A. Guisasola ◽  
J.A. Baeza ◽  
J. Carrera ◽  
G. Sin ◽  
P.A. Vanrolleghem ◽  
...  
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Data Quality ◽  
Estimation Accuracy

scholarly journals JOINT DELAY AND AZIMUTH ESTIMATION OF COHERENT WAVES FOR MILLIMETER-WAVE BAND CHANNEL SOUNDING

2020 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Kentaro Saito ◽  
Ahmad Salaam Mirfananda ◽  
Jun-ichi Takada ◽  
Mitsuki Nakamura ◽  
Wataru Yamada ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Millimeter Wave ◽  
Mobile Communication ◽  
Channel Modeling ◽  
Cloud Services ◽  
Mimo Channel ◽  
Estimation Accuracy ◽  
Wave Band ◽  
Channel Sounding ◽  
Millimeter Wave Band

The user traffic in the mobile communication area has rapidly increased owing to the widespread of smartphones and various cloud services. To handle the increasing traffic, in the fifth-generation mobile communication system (5G), the millimeter-wave multiple-input and multiple-output (MIMO) communication technology is under development. Because the MIMO transmission performance heavily depends on the radio propagation characteristics, various MIMO channel measurements are needed for the performance evaluation and system design. The accurate and efficient parameter estimation algorithm which estimates the propagation delays and angle of arrivals (AoA) of radio waves is also indispensable for the purpose. In this paper, we extended the joint delay and azimuth estimation (JADE) method based on multiple signal classification (MUSIC) algorithm. In our proposal, the drawback of the MUSIC that the performance degrades for the estimation of coherent waves was solved by applying the smoothing technique in the frequency domain. It also makes the antenna calibration simpler. We implemented the proposed algorithm for the channel sounding system in the 66 GHz band, which is one of the candidate frequency bands for the International Mobile Telecommunications (IMT) system and evaluated the effectiveness through the experiment in an anechoic chamber. The result showed that our proposed method can de-correlate the signal components of coherent waves, and improved the parameter estimation accuracy significantly. The root means square error (RMSE) of the propagation delay estimation was improved from 2.7 ns to 0.9 ns, and the RMSE of the AoA estimation was improved from 20.3 deg. to 7.2 deg. The results are expected to be utilized for the millimeter wave band MIMO channel modeling.

scholarly journals A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shaolong Chen ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Liu Yang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
System Modeling ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Function Parameter ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

Model Verification of Mixed Dynamic Systems

1978 ◽  
Vol 100 (2) ◽  
pp. 266-273 ◽  
Author(s):  
J. D. Chrostowski ◽  
D. A. Evensen ◽  
T. K. Hasselman
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Dynamic Systems ◽  
Statistical Parameter ◽  
Model Verification ◽  
Passive Networks ◽  
Parameter Values ◽  
Basic Configuration ◽  
General Method

A general method is presented for using experimental data to verify math models of “mixed” dynamic systems. The term “mixed” is used to suggest applicability to combined systems which may include interactive mechanical, hydraulic, electrical, and conceivably other types of components. Automatic matrix generating procedures are employed to facilitate the modeling of passive networks (e.g., hydraulic, electrical). These procedures are augmented by direct matrix input which can be used to complement the network model. The problem of model verification is treated in two parts; verification of the basic configuration of the model and determination of the parameter values associated with that configuration are addressed sequentially. Statistical parameter estimation is employed to identify selected parameter values, recognizing varying degrees of uncertainty with regard to both experimental data and analytical results. An example problem, involving a coupled hydraulic-mechanical system, is included to demonstrate application of the method.

Parameter Estimation of Linear Sensorimotor Synchronization Models: Phase Correction, Period Correction, and Ensemble Synchronization

2015 ◽  
Vol 3 (1-2) ◽  
pp. 52-87 ◽  
Author(s):  
Nori Jacoby ◽  
Naftali Tishby ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Peter E. Keller
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Linear Models ◽  
Estimation Error ◽  
Phase Correction ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Single Subject ◽  
Novel Method ◽  
Parameter Estimation Methods

Linear models have been used in several contexts to study the mechanisms that underpin sensorimotor synchronization. Given that their parameters are often linked to psychological processes such as phase correction and period correction, the fit of the parameters to experimental data is an important practical question. We present a unified method for parameter estimation of linear sensorimotor synchronization models that extends available techniques and enhances their usability. This method enables reliable and efficient analysis of experimental data for single subject and multi-person synchronization. In a previous paper (Jacoby et al., 2015), we showed how to significantly reduce the estimation error and eliminate the bias of parameter estimation methods by adding a simple and empirically justified constraint on the parameter space. By applying this constraint in conjunction with the tools of matrix algebra, we here develop a novel method for estimating the parameters of most linear models described in the literature. Through extensive simulations, we demonstrate that our method reliably and efficiently recovers the parameters of two influential linear models: Vorberg and Wing (1996), and Schulze et al. (2005), together with their multi-person generalization to ensemble synchronization. We discuss how our method can be applied to include the study of individual differences in sensorimotor synchronization ability, for example, in clinical populations and ensemble musicians.

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