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The AND node is a Boolean AND based on the classic fuzzy min function modified to compensate for missing data. In its purest use, it evaluates to true when all its child-nodes are true and false when any child-node is false.

The classic fuzzy min in practice tends to ignore the possible contribution of unknown data. The missing data compensation function used in NetWeaver was developed after many years of empirical study of way subject matter experts tended to hedge their decisions when faced with unknown inputs.


  • Minimum number of child-nodes: 0
  • Maximum number of child-nodes: no limit
  • Valid input range: -1. to +1. (false to true)


The AND node value is the summation of two parts: the classic fuzzy min function and a missing data compensation function.

The fuzzy min is simply the minimum value of its child-nodes.

The missing data compensation part is described below.

The weighted1) average is calculated by: weighted average={sum{i=1}{n}{{value_i}*{weight_i}}}/{sum{i=1}{n}{weight_i}}

Weight is a node property that has meaning in only some situations. By default its value is 1, but can be changed to any numeric value.