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Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network


Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters’ definition of the network architecture, which may be a problem especially for real-time large-scale tasks. Therefore, this paper proposes a new model that uses a non-parametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of temporal distribution (asynchronous or client–server) were performed and compared with state-of-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

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    Based on Abasikeles and Akay (2013).

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    Similarly, the technique used by Gao et al., instead of analyzing samples, analyzes the membership degreee values that make up the neurons of the first layer.

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    The database can be found in

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The thanks of this work are destined to CEFET-MG and Faculdade UNA de Betim.

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Correspondence to Paulo Vitor de Campos Souza.

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de Campos Souza, P.V., Soares, E.A., Guimarães, A.J. et al. Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network. Evolving Systems 12, 899–911 (2021).

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  • Fuzzy neural networks
  • Autonomous data density
  • Optical interconnection network