Uncertainty and distributed nature inherently exist in big data environment. distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in ...
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Uncertainty and distributed nature inherently exist in big data environment. distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.
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