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Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • Paulo Vitor de Campos Souza
  • Eduardo A. Soares
  • Augusto Junio Guimarães
  • Vanessa Souza Araujo
  • Vinicius Jonathan S. Araujo
  • Thiago Silva Rezende
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<mark>Journal publication date</mark>20/03/2020
<mark>Journal</mark>Evolving Systems
Number of pages13
Publication StatusE-pub ahead of print
Early online date20/03/20
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s12530-020-09336-3