A novel recurrent neurofuzzy network is proposed in this paper. More specifically, in this work we generalize the recurrent neurofuzzy network structure proposed in [1], which is in turn is an improvement of the feedforward structure introduced in [2]. The network structure is composed by two structures: a fuzzy inference system and a neural network. The fuzzy inference system contains fuzzy neurons modeled with the aid of logic operations processed via t-norms and s-norms. The neural network is composed by nonlinear elements placed in series with the previous logical element. The network model implicitly encodes a set of if-then rules and its recurrent multi layer structure performs fuzzy inference. The topology induces a clear relationship between the network structure and an associated fuzzy rule-based system. This means that linguistic knowledge can efficiently be inserted or extracted from the network.
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