DECSI - Departamento de Computação e Sistemas de Informação

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Browsing DECSI - Departamento de Computação e Sistemas de Informação by Author "Araújo, Vanessa Souza"

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    Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function.
    (2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Araújo, Vinicius Jonathan Silva; Rezende, Thiago Silva
    This paper proposes the use of fuzzification functions based on clustering of data based on their density to perform the granularization of the input space. The neurons formed in this layer are built through the density centers obtained with the input data of the model. In the second layer, the nullneurons aggregate the generated neurons in the first layer and allow the creation of if/then fuzzy rules. Even in the second layer, a regularization function is activated to determine the essential nullneurons. The concepts of extreme learning machine generate the weights used in the third layer, but with a regularizing factor. Finally, in the third layer, represented by an artificial neural network, it has a single neuron that the activation function uses robust functions to carry out the model. To verify the new training approach for fuzzy neural networks, we performed real and synthetic database tests for the pattern classification, which led to the conclusion that the data density-based approach the use of regularization factors in the second model layer and neurons with more robust activation functions allowed better results compared to other classifiers that use the concepts of extreme learning machine.
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    Pulsar detection for wavelets soda and regularized fuzzy neural networks based on andneuron and robust activation function.
    (2019) Souza, Paulo Vitor de Campos; Torres, Luiz Carlos Bambirra; Guimarães, Augusto Júnio; Araújo, Vanessa Souza
    The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.