Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

被引:4
作者
Souza, Paulo Vitor de Campos [1 ]
Soares, Eduardo A. [2 ]
Guimaraes, Augusto Junio [1 ]
Araujo, Vanessa Souza [1 ]
Araujo, Vinicius Jonathan S. [1 ]
Rezende, Thiago Silva [1 ]
机构
[1] Ctr Univ Una, Betim, MG, Brazil
[2] Fed Univ Lavras UFLA, Dept Engn, Lavras, MG, Brazil
关键词
Fuzzy neural networks; Autonomous data density; Optical interconnection network; EXTREME LEARNING-MACHINE; CLASSIFICATION; REGRESSION; MODEL;
D O I
10.1007/s12530-020-09336-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:899 / 911
页数:13
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