eg-VSSA: An extragradient variable sample-size stochastic approximation scheme: Error analysis and complexity trade-offs

2016 ◽  
Author(s):  
Afrooz Jalilzadeh ◽  
Uday V. Shanbhag
Keyword(s):  
Error Analysis ◽  
Sample Size ◽  
Stochastic Approximation ◽  
Approximation Scheme ◽  
Variable Sample Size ◽  
Trade Offs

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

A variable sample size run sum coefficient of variation chart

2022 ◽  
Author(s):  
Wai Chung Yeong ◽  
Yen Yoon Tan ◽  
Sok Li Lim ◽  
Khai Wah Khaw ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size

Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size

2020 ◽  
Vol 88 ◽  
pp. 106041 ◽  
Author(s):  
Xiao-han Wang ◽  
Yong Zhang ◽  
Xiao-yan Sun ◽  
Yong-li Wang ◽  
Chang-he Du
Keyword(s):  
Feature Selection ◽  
Sample Size ◽  
Artificial Bee Colony ◽  
Variable Sample Size ◽  
Multi Objective ◽  
Bee Colony

Monitoring the coefficient of variation using a variable sample size control chart

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1561-1576 ◽  
Author(s):  
Philippe Castagliola ◽  
Ali Achouri ◽  
Hassen Taleb ◽  
Giovanni Celano ◽  
Stelios Psarakis
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Coefficient Of Variation ◽  
Size Control ◽  
Variable Sample Size

A two Timescale Stochastic Approximation Scheme for Simulation-Based Parametric Optimization

1998 ◽  
Vol 12 (4) ◽  
pp. 519-531 ◽  
Author(s):  
Shalabh Bhatnagar ◽  
Vivek S. Borkar
Keyword(s):  
Stochastic Approximation ◽  
Perturbation Analysis ◽  
Parametric Optimization ◽  
Approximation Scheme ◽  
Infinitesimal Perturbation Analysis ◽  
Simulation Based ◽  
Infinitesimal Perturbation ◽  
Two Timescale Stochastic Approximation

A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation-based parametric optimization as an alternative to traditional “infinitesimal perturbation analysis” schemes. It avoids the aggregation of data present in many other schemes. Its convergence is analyzed, and a queueing example is presented.

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