In this paper, an adaptive anti-disturbance switching (AADS) control
strategy is proposed for switched Takagi-Sugeno fuzzy systems (ST-SFSs)
subject to multi-source disturbances. The disturbances consist of two
parts: the available unmodeled disturbance and the disturbance modeled
by dynamic neural network. Firstly, a novel adaptive disturbance
observer is designed to approximate the dynamic neural network modeled
disturbance. Secondly, the attenuation performance from the output to
the available disturbance is analyzed by the L 2 gain index. Thirdly, a
controller based on the adaptive disturbance observer is constructed for
the ST-SFS under the average dwell time switching signal limitation.
Further, under the designed adaptive disturbance observer, and the
controller, a sufficient condition is established for the ST-SFS to
realize multi-source disturbance suppression (DS). Finally, a
mass-spring-damping simulation example is given to verify the
rationality of the established AADS control scheme.
Objective: We aimed to identify potential functional network brain-activity abnormalities in patients with orbital fractures (OFs) by using the voxel-wise degree centrality (DC) method.Methods:We selected 20 patients with OFs (12 men and 8 women) and 20 healthy controls (HCs; 12 men and 8 women) matched by gender, age, and education level for this study. Resting-state functional magnetic resonance imaging (fMRI) has been widely used in various disciplines. We calculated receiver operating characteristic (ROC) curves to differentiate characteristics between patients with orbital fractures and HCs; in addition, we applied correlation analyses between behavioral performance and average DC values in different areas. The DC method served to evaluate spontaneous brain activity.Results:The DC values of patients with OFs were higher in the right cerebellum 9 area (Cerebelum_9_R) and left cerebellar peduncle 2 area (Cerebelum_Crus2_L) than those in HCs. The area under the curve (AUC) values for Cerebelum_9_R and Cerebelum_Crus2_L were 0.983 and 1, respectively. The accuracy of our ROC curve analysis result was reliable. Conclusion:Many brain regions seem to show abnormal brain network characteristics in patients with orbital fractures, suggesting potential neuropathic mechanisms.
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