In this monograph, the authors introduce a novel fuzzy rule-base,
referred to as the Fuzzy All-permutations Rule-Base (FARB). They
show that inferring the FARB, using standard tools from fuzzy logic
theory, yields an input-output map that is mathematically
equivalent to that of an artificial neural network. Conversely,
every standard artificial neural network has an equivalent FARB.
The FARB-ANN equivalence integrates the merits of symbolic fuzzy
rule-bases and sub-symbolic artificial neural networks, and yields
a new approach for knowledge-based neurocomputing in artificial
neural networks.
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