文章基本信息

标题：Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems
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作者：Paulo Vitor de Campos Souza ; Augusto Junio Guimaraes ; Vanessa Souza Araújo 等
期刊名称：Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
印刷版ISSN：1137-3601
电子版ISSN：1988-3064
出版年度：2018
卷号：21
期号：62
页码：114-133
DOI：10.4114/intartif.vol22iss63pp114-133
语种：English
出版社：Spanish Association for Intelligence Artificial
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 .
其他摘要：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.