scholarly journals Fault Detection of Linear Discrete Dynamic Systems by a Generalized-Likelihood-Ratio Method Using Pattern Recognition Technique

1989 ◽  
Vol 25 (6) ◽  
pp. 651-658 ◽  
Author(s):  
Shogo TANAKA
Keyword(s):  
Pattern Recognition ◽  
Fault Detection ◽  
Likelihood Ratio ◽  
Dynamic Systems ◽  
Ratio Method ◽  
Pattern Recognition Technique ◽  
Discrete Dynamic ◽  
Discrete Dynamic Systems ◽  
Recognition Technique ◽  
Likelihood Ratio Method

Fault Detection in Linear Discrete Dynamic Systems by a Pattern Recognition of a Generalized-Likelihood-Ratio

1990 ◽  
Vol 112 (2) ◽  
pp. 276-282 ◽  
Author(s):  
S. Tanaka ◽  
P. C. Mu¨ller
Keyword(s):  
Pattern Recognition ◽  
Likelihood Ratio ◽  
Dynamic Systems ◽  
Abrupt Change ◽  
Detection Method ◽  
Discrete Dynamical System ◽  
Generalized Likelihood Ratio ◽  
Robust Detection ◽  
Chi Squared ◽  
Discrete Dynamic Systems

The detection of an abrupt change in the parameters of a linear discrete dynamical system is considered in the framework of the easily implemented generalized-likelihood-ratio (GLR) method. This paper proposes a robust detection method based on a pattern recognition of the maximum GLR provided by the conventional step-hypothesized GLR method. A numerical example demonstrates that the proposed method is highly superior to the conventional step-hypothesized GLR method and to the Chi-squared test in both detection rate and detection speed.

scholarly journals FAULT DETECTION IN DISCRETE DYNAMIC SYSTEMS WITH UNCERTAINTY BASED ON INTERVAL OPTIMIZATION

2011 ◽  
Vol 16 (4) ◽  
pp. 549-557 ◽  
Author(s):  
Wei Li ◽  
Xiaoli Tian
Keyword(s):  
Fault Detection ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Optimization Model ◽  
Uncertain Parameters ◽  
Interval Optimization ◽  
Numerical Examples ◽  
Discrete Dynamic ◽  
Discrete Dynamic System ◽  
Discrete Dynamic Systems

The imprecision and the uncertainty of many systems can be expressed with interval models. This paper presents a method for fault detection in uncertain discrete dynamic systems. First, the discrete dynamic system with uncertain parameters is formulated as an interval optimization model. In this model, we also assume that there are some errors of observation values of the inputs/outputs. Then, M. Hladík's newly proposed algorithm is exploited for this model. Some numerical examples are also included to illustrate the method efficiency.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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