This paper is concerned with further studies on control synthesis of discrete-time Takagi-Sugeno (T-S) fuzzy systems. To do this, a novel slack variable technique, which is homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions with arbitrary degrees, is presented by developing an efficient augmented multi-indexed matrix approach. Under the framework of homogenous matrix polynomials, the algebraic properties of both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions are collected into sets of augmented multi-indexed matrices. Thus, more information about the underlying normalized fuzzy weighting functions is involved into control synthesis. Consequently, the relaxation quality of control synthesis of discrete-time T-S fuzzy systems is improved significantly. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.
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http://dx.doi.org/10.1109/TCYB.2014.2316491 | DOI Listing |
PLoS One
January 2025
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, RP China.
This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Creative Product Design, Asia University, Taichung, Taiwan.
Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (MCI) is frequently viewed as a crucial stage before the onset of AD, making it the ideal period for therapeutic intervention.
View Article and Find Full Text PDFJ Clin Endocrinol Metab
January 2025
Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA.
Context: The timing of a woman's final menstrual period (FMP) in relation to her age is considered a valuable indicator of overall health, being associated with cardiovascular, bone health, reproductive, and general mortality outcomes.
Objective: This work aimed to evaluate the relationship between hormones and the "time to FMP" when daily hormone trajectories are characterized by their 1) entropy, and 2) deviation from premenopausal/stable cycle patterns (representing a textbook "gold standard"; GS).
Methods: As part of the Study of Women's Health Across the Nation, urinary luteinizing hormone (LH), follicle-stimulating hormone (FSH), estrogen conjugates (E1C), and pregnanediol glucuronide (PDG) were measured daily from a multiracial sample of 549 mid-life women for the duration of one menstrual cycle.
Neural Netw
January 2025
College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China. Electronic address:
In partial multi-label learning (PML), each instance is associated with multiple candidate labels, but only a subset is the ground-truth label. Due to the ambiguous label information, PML is more challenging than traditional multi-label learning. Conventional PML mainly focuses on learning a desired feature space or label space for disambiguation, ignoring the tight correlation between two spaces.
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