OPTIMAL PSS-PARAMETER SELECTION ALGORITHM WITH NEW PERFORMANCE MEASURE

1990 ◽  
pp. 301-306
Author(s):  
J.W. Jung ◽  
J.B. Choo ◽  
Y.M. Park
Keyword(s):  
Performance Measure ◽  
Parameter Selection ◽  
Selection Algorithm

Optimal PSS-Parameter Selection Algorithm with New Performance Measure

1989 ◽  
Vol 22 (9) ◽  
pp. 301-306
Author(s):  
J.W. Jung ◽  
J.B. Choo ◽  
Y.M. Park
Keyword(s):  
Performance Measure ◽  
Parameter Selection ◽  
Selection Algorithm

scholarly journals Parameter Selection in the Parameter Estimation of Grade Transitions in a Polyethylene Plant

2015 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Niklas Andersson ◽  
Per-Ola Larsson ◽  
Johan Åkesson ◽  
Niclas Carlsson ◽  
Staffan Skålén ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Steady State ◽  
State Parameter ◽  
Parameter Selection ◽  
Selection Algorithm ◽  
Parameter Estimations ◽  
Dynamic Case ◽  
Nominal Parameter ◽  
Best Parameter ◽  
Parameter Values

A polyethylene plant at Borealis AB is modelled in the Modelica language and considered for parameter estimations at grade transitions. Parameters have been estimated for both the steady-state and the dynamic case using the JModelica.org platform, which offers tools for steady-state parameter estimation and supports simulation with parameter sensitivies. The model contains 31 candidate parameters, giving a huge amount of possible parameter combinations. The best parameter sets have been chosen using a parameter-selection algorithm that identified parameter sets with poor numerical properties. The parameter-selection algorithm reduces the number of parameter sets that is necessary to explore. The steady-state differs from the dynamic case with respect to parameter selection. Validations of the parameter estimations in the dynamic case show a significant reduction in an objective value used to evaluate the quality of the solution from that of the nominal reference, where the nominal parameter values are used.

Evaluation of an Actuator Placement Method for Active Noise Control Applications

1998 ◽  
Vol 120 (4) ◽  
pp. 875-879 ◽  
Author(s):  
K. Naghshineh ◽  
L. P. Heck ◽  
J. A. Olkin ◽  
J. W. Kamman
Keyword(s):  
Noise Control ◽  
Broad Band ◽  
Active Noise Control ◽  
Performance Measure ◽  
Numerical Results ◽  
Selection Algorithm ◽  
Qr Algorithm ◽  
Active Noise ◽  
Improved Performance ◽  
Actuator Placement

In our previous work, we developed a new actuator placement algorithm that is capable of selecting the best actuator placement for active noise control problems over a broad band of frequencies. The actuator selection algorithm is based on a novel extension of the Householder QR subset selection algorithm. The QR algorithm uses the l2 matrix norm as a performance measure. In this paper, numerical results generated by that algorithm are compared with numerical results generated using five different performance measures. These measures, which are based on different matrix norms and functions of the actuator frequency responses, yield actuator placements that result in active noise control systems with improved performance and robustness.

Predictive Modeling of Product Returns for Remanufacturing

2015 ◽  
Author(s):  
Jungmok Ma ◽  
Harrison M. Kim
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Mixed Model ◽  
Moving Average ◽  
Predictive Performance ◽  
Performance Measure ◽  
Selection Algorithm ◽  
Product Returns ◽  
Distributed Lag

As awareness of environmental issues increases, the pressures from the public and policy makers have forced OEMs to consider remanufacturing as the key product design option. In order to make the remanufacturing operations more profitable, forecasting product returns is critical with regards to the uncertainty in quantity and timing. This paper proposes a predictive model selection algorithm to deal with the uncertainty by identifying better predictive models. Unlike other major approaches in literature (distributed lag model or DLM), the predictive model selection algorithm focuses on the predictive power over new or future returns. The proposed algorithm extends the set of candidate models that should be considered: autoregressive integrated moving average or ARIMA (previous returns for future returns), DLM (previous sales for future returns), and mixed model (both previous sales and returns for future returns). The prediction performance measure from holdout samples is used to find a better model among them. The case study of reusable bottles shows that one of the candidate models, ARIMA, can predict better than the DLM depending on the relationships between returns and sales. The univariate model is widely unexplored due to the criticism that the model cannot utilize the previous sales. Another candidate model, mixed model, can provide a chance to find a better predictive model by combining the ARIMA and DLM. The case study also shows that the DLM in the predictive model selection algorithm can provide a good predictive performance when there are relatively strong and static relationships between returns and sales.

scholarly journals A non-linear data mining parameter selection algorithm for continuous variables

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187676 ◽  
Author(s):  
Peyman Tavallali ◽  
Marianne Razavi ◽  
Sean Brady
Keyword(s):  
Data Mining ◽  
Parameter Selection ◽  
Continuous Variables ◽  
Selection Algorithm ◽  
Non Linear

scholarly journals Research on the order parameter selection algorithm based on correlation analysis and principal component analysis——Taking the Logistics sector in Gansu Province as an example

2020 ◽  
Vol 214 ◽  
pp. 02031
Author(s):  
Mu Dan ◽  
Shi Chuwei
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Social System ◽  
Order Parameter ◽  
Principal Component ◽  
Component Analysis ◽  
Parameter Selection ◽  
Order Parameters ◽  
Gansu Province ◽  
Selection Algorithm

An order parameter selection algorithm based on correlation analysis and principal component analysis was designed according to the statistical analysis method, the selection principle of order parameters of social system, and the correlation test in correlation analysis and the variable contribution test in principal component analysis in this paper. The redundant variables were eliminated from the system by correlation analysis first, and then the variables with high contribution to the system were selected by principal component analysis, so the order parameters obtained accordingly not only have low information redundancy, but also reflect the actual information of the social system to the greatest extent. At the end of this paper, the logistics sector in Gansu Province was taken as an example to select the panel data from 2006 to 2015. Eight indices were extracted as the order parameters of the logistics sector in Gansu Province from the sixteen indices which are redundant selected by this algorithm. The order parameters selected by rational judgment reflect 99% of the original information. The results show that the order parameters in the social system can be correctly and reasonably selected by this order parameter selection algorithm based on correlation analysis and principal component analysis.

Sign in / Sign up

Export Citation Format

Share Document