Robust analysis and control of parameter-dependent uncertain descriptor systems

2011 ◽  
Vol 60 (5) ◽  
pp. 356-364 ◽  
Author(s):  
Gabriela Iuliana Bara
Keyword(s):  
Descriptor Systems ◽  
Robust Analysis ◽  
And Control ◽  
Parameter Dependent

scholarly journals Improved H2 and H¥ conditions for robust analysis and control synthesis of linear systems

2005 ◽  
Vol 16 (4) ◽  
pp. 427-434 ◽  
Author(s):  
Alexandre Trofino ◽  
Daniel F. Coutinho ◽  
Karina A. Barbosa
Keyword(s):  
Linear Systems ◽  
Control Synthesis ◽  
H Infinity ◽  
Stability Performance ◽  
Augmented Space ◽  
Polytopic Uncertainties ◽  
Discrete Time Case ◽  
Performance Conditions ◽  
And Control ◽  
Parameter Dependent

This paper proposes improved H-2 and H-infinity conditions for continuous-time linear systems with polytopic uncertainties based on a recent result for the discrete-time case. Basically, the performance conditions are built on an augmented-space with additional multipliers resulting in a decoupling between the Lyapunov and system matrices. This nice property is used to develop new conditions for the robust stability, performance analysis, and control synthesis of linear systems using parameter dependent Lyapunov functions in a numerical tractable way.

Texture Analysis and Genetic Algorithms for Osteoporosis Diagnosis

2019 ◽  
Vol 34 (05) ◽  
pp. 2057002
Author(s):  
Laatra Yousfi ◽  
Lotfi Houam ◽  
Abdelhani Boukrouche ◽  
Eric Lespessailles ◽  
Frédéric Ros ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Texture Analysis ◽  
Image Features ◽  
Diagnosis Of Osteoporosis ◽  
Occurrence Matrix ◽  
Two Parameters ◽  
And Control ◽  
Parameter Dependent ◽  
Two Populations ◽  
Distance Parameter

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.

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