Abstract
This paper presents a learning algorithm for fuzzy neural networks based on nullneurons able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and regularization method, resulting in sparse and accurate models. Experiments considering pattern classification are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.
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