Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems

Inteligencia Artificial

Vanessa Souza Araújo¹  Thiago Silva Rezende¹  Augusto Junio Guimaraes¹  Vinicius Jonathan Silva Araújo¹  Paulo Vitor de Campos Souza² 

[1] ;Centro Federal de Educação Tecnológica de Minas Gerais;

关键词: Bootstrap lasso;    Extreme Learning Machines;    Regression Problems;    Fuzzy Neural Network;    Fuzzy Logic Neurons;   

DOI  :  10.4114/intartif.vol22iss63pp114-133

来源: DOAJ

【 摘 要 】

This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and fuzzy systems able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine [36], to achieve a low time complexity, and regularization theory, resulting in sparse and accurate models. A compact set of incomplete fuzzy rules can be extracted from the resulting network topology. Experiments considering regression problems are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.

【 授权许可】

Unknown