The neurons represent fuzzy sets used in the antecedents of fuzzy rules and determine the membership degree of the input.
However, for a large value of L and a certain stationarity degree of the input signal, we can treat this term as a deterministic quantity [1].
These findings are unlikely to depend on the truncation degree of the input field models, as long as both MF and SV models have the same truncation degree (at least up to 18) and moderate trends in their power spectra.
Θ denotes the effective utilization degree of the input, (which is relative to the output in the DMU0), which is the efficiency value, indicating whether the financial support is effective in the sample enterprises and whether the output is maximized.
so that, for a large value of L and a certain stationarity degree of the input signal, we can treat this term as a deterministic quantity at this point [1].
The efficient generalization of the dynamic programming approach to trees with multifurcations is non-trivial, since a straightforward approach yields run-times that are exponential in the maximal degree of the input tree.
CLUSTER EDITING is NP-hard [ 24], even if the maximum degree of the input graph is five [ 25] and it cannot be solved in 2 o (k )· n O (1) time assuming ETH [ 25, 26].
We use the value for our experiments, as this is close to the maximum degree Δ of the input networks (which is 184, 190, 323, 243 and 11 for C. elegans, D. melanogaster, S.cerevisiae, H. sapiens and M. musculus, respectively).
The first layer nodes are calculated from the degrees of input in the fuzzy sets, and the nodes products in the second layer represent the operation of the antecedent conjunction.
