Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we\npropose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite\nmodularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically\ndetermine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on\nsix real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs\nthe best in terms of robustness while accurately detecting the number of clusters....
Various optimization methods and network architectures are used by convolutional\nneural networks (CNNs). Each optimization method and network architecture style have their own\nadvantages and representation abilities. To make the most of these advantages, evolutionary-fuzzyintegral-\nbased convolutional neural networks (EFI-CNNs) are proposed in this paper. The proposed\nEFI-CNNs were verified by way of face classification of age and gender. The trained CNNsâ?? outputs\nwere set as inputs of a fuzzy integral. The classification results were operated using either Sugeno\nor Choquet output rules. The conventional fuzzy density values of the fuzzy integral were decided\nby heuristic experiments. In this paper, particle swarm optimization (PSO) was used to adaptively\nfind optimal fuzzy density values. To combine the advantages of each CNN type, the evaluation of\neach CNN type in EFI-CNNs is necessary. Three CNN structures, AlexNet, very deep convolutional\nneural network (VGG16), and GoogLeNet, and three databases, computational intelligence\napplication laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in\nexperiments to classify age and gender. The experimental results show that the proposed method\nachieved 5.95% and 3.1% higher accuracy, respectively, in classifying age and gender....
This paper studies the fault-tolerant control problem of uncertain doubly-fed wind turbine\ngeneration systems with sensor faults. Considering the uncertainty of the system, a fault-tolerant\ncontrol strategy based on a T-S fuzzy observer is proposed. The fuzzy observer is established based\non the T-S fuzzy model of the uncertain nonlinear system. According to the comparison and analysis\nof residual between the state estimation of the fuzzy observer output and the measured value of\nthe real sensor, a fault detection and isolation (FDI) based on T-S fuzzy observer is designed. Then\nby using a Parallel Distributed Compensation (PDC) method we design the robust fuzzy controller.\nFinally, the necessary and sufficient conditions for the stability of the closed-loop system are proved\nby quoting Lyapunov stability theory. The simulation results verify the effectiveness of the proposed\ncontrol method....
This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding\nthe data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the\nsense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the\ndata outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression\nmodel. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is\ndesigned for detecting data outlier. An example is finally provided to validate the presented methods....
We introduce the definition of non-Archimedean 2-fuzzy 2-normed spaces\nand the concept of isometry which is appropriate to represent the notion of\narea preserving mapping in the spaces above. And then we can get isometry\nwhen a mapping satisfies AOPP and (*) (in article) by applying the Benzâ??s\ntheorem about the Aleksandrov problem in non-Archimedean 2-fuzzy\n2-normed spaces....
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