From Type-2 Fuzzy Rate-Based Neural Networks To Social Networks' Behaviors
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)(2016)
摘要
the booming environment of social networks like Facebook, Twitter and Instagram is expanding more and more instantly. These rapid changes increase the complexity of these networks simultaneously. In this paper, a new approach is proposed to model some complex behaviors of social networks, including "attention inversion". The model consists of an embedded type-2 fuzzy inference system in collaboration with rate-based neural networks. Finally, an experiment is performed on some selected Twitter hashtags to represent the performance of the model.
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关键词
Neural networks,Type-2 fuzzy,Social networks
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