The networks produced by the algorithms presented in these papers differ mainly in properties like generalization performance, size, depth, weight range, etc. Unfortunately, these algorithms take advantage of the data being Boolean and are therefore not directly applicable to many applications. Consequently, the neural network construction algorithm should keep the number of in- and outputs unchanged.
B is small then the output is big .
This means that the developer instinctively first applied a sort of fuzzy threshold function and tried then to find a kind of Boolean rules based on this approximation of the data. If a set of rules, which explains most of the data elements, is found, these rules are quantified and finally refined. This Boolean data set is used to construct a Boolean expression, which 3 is translated into a high order perceptron.