2,979 publications from this institution
This article focuses on the leader-following consensus of fuzzy fractional order singular perturbation multi-agent systems (FOSPMASs) with order in 0,2. By employing the T-S fuzzy modeling approach, a fuzzy FOSPMAS is constructed. Subsequently, a fuzzy observer-based controller is designed and the error system corresponding to each agent is derived. Through a series of equivalent transformations, the error system is decomposed into fuzzy singular fractional order systems (SFOSs). According to the admissibility of SFOSs, the consensus conditions of the fuzzy FOSPMAS are obtained based on linear matrix inequalities (LMIs) without equality constraint. Finally, the effectiveness of the criteria is verified through an RLC circuit model.
Fire rescue challenges and solutions have evolved from straightforward plane rescue to encompass 3D space due to the rise of high-rise city buildings. Hence, this study facilitates a system with quick and simplified on-site launching and generates real-time location data, enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue. Auto-positioning with step-by-step instructions is proposed when launching the locating system, while no extra measuring instrument like Total Station (TS) is needed. Real-time location tracking is provided via a 3D space real-time locating system (RTLS) constructed using Ultra-wide Bandwidth technology (UWB), which requires electromagnetic waves to pass through concrete walls. A hybrid weighted least squares with a time difference of arrival (WLS/TDOA) positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications. The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound (CRLB). The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter (TDC). The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications. The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.
In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets).We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes.The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity.The emergence and construction of granular models of type-2 and type-n (in general) is discussed.It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.
Although spiking neural network (SNN) has the advantages of strong brain-likeness and low energy consumption due to the use of discrete spikes for information representation and transmission, its performance still needs to be improved. This article improves SNN in terms of the coding process and the spike response function by invoking fuzzy sets. In terms of coding, a new fuzzy C-means coding (FCMC) method is proposed, which breaks the limitation of uniformly distributed receptive fields of existing coding methods and automatically determines suitable receptive fields that reflect the density distribution of the input data for encoding through the fuzzy C-means clustering. In terms of spike response function, triangular fuzzy numbers instead of the commonly used alpha-type function are used as the spike response function. Different from other functions of fixed shape, width parameters of the proposed function are learnt in the iterative way like weights of synapses do. Experimental results obtained on seven benchmark datasets and two real-world datasets with eleven approaches demonstrate that SNN with triangular spike response functions (abbreviated as T-SNN) combining FCMC can achieve improved performance in terms of accuracy, F-measure, AUC, required epochs, running time, and stability.
While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling.
Coping with imbalanced data is a challenging task in practical classification problems. One of effective methods to solve imbalanced problems is to oversample the minority class. Gls SMOTE is a classical oversampling method. However, it exhibits two disadvantages, namely, a linear generation and overgeneralization. In this article, an improved synthetic minority oversampling technique (SMOTE) method, FE- SMOTE, is proposed based on the idea of the method of finite elements. FE- SMOTE not only overcomes the above two disadvantages of SMOTE but also can generate samples that are more in line with the density distribution of the original minority class than those generated by the existing SMOTE variants. The originality of the proposed method stems from constructing a simplex for every minority sample and then triangulating it to expand the region of synthetic samples from lines to space. A new definition of the relative size for triangular elements not only helps determine the number of synthetic samples but also weakens the adverse impact of outliers. Generated samples by FE- SMOTE can effectively reflect the local potential distribution structure arising around every minority sample. Compared with 16 commonly studied oversampling methods, FE- SMOTE produces promising results quantified in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> -mean, AUC, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -measure, and accuracy on 22 benchmark imbalanced datasets and the big dataset MNIST.
Fuzzy reasoning is intensively used in intelligent systems including fuzzy control, classification, expert systems, and networks to name a few dominant categories of such architectures. As being a fundamental construct permeating so many diverse areas, fuzzy reasoning was studied with respect to its fundamental properties such as robustness. The notion of robustness or sensitivity becomes of paramount importance by leading to a more comprehensive understanding of the way in which reasoning processes are developed. In this study, we introduce and study properties of some measures of robustness (or sensitivity) of fuzzy connectives and implication operators and discuss their relationships with perturbation properties of fuzzy sets. The results produced here are compared and contrasted with the previous findings available in the literature. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 393–413, 2005.
