by lylelinLogic.Minimizer.1.2.1.with.Serial 📥
Logic.Minimizer.1.2.1.with.Serial
In logic minimization the reduction process is iterative, as there may be more than one possible pattern. The resulting N-rule sets can now be used as input for a decision rule extractor in order to transform the N rules into one rule that can be interpreted as a decision rule, using any known technique.
If we set each value equal to 1 if it is less than.5 we would get the data in Table 7. To get the actual data table, we need to convert the data in Table 6 to Table 7. This is called \”soft encoded data\”.
The process that this technique uses to analyze a binary function is similar to a back propagation algorithm, but we do not require a derivative. The constant 10 was chosen in the initial function to prevent the ANN from stopping too early. Mano[ 1 ] provides a good tutorial on how to find minimum errors and a list of error. Our artificial neural network program can be found in the Appendix.
Minor improvements to the logic minimizer can be achieved by applying various generalizations. First, we can delete redundant rules from the set. Any rule that fits more positive sites than the one it fits depends on the one it fits and will be redundant. This is readily identified because each positive site selected by the rule must be selected by at least one other rule. If it does not fit any positive site, it is redundant and it can be deleted. Second, we can identify positive sites that are part of the same O-glycosylation pattern by applying our generalization from Rule 12, Rule 13, and Rule 14 of O-glycosite patterns to O-glycosite rules. In this way, if a rule is defined by the first site of an O-glycosite pattern, the next two sites in the rule could be more generic input for the general rules for any position by simply deleting the second and third O-glycosylation sites. Note that the use of more generic rules at various positions would mean a less well defined S-site model.
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