One of the problems in computer science is the gap between natural languages for human beings and programming languages for digital computers, since the former are much more complex than the latter. This is due to the fact that natural languages are fuzzy in nature but programming languages are precise. For example, when we say that a certain university is a good university, fuzziness arises since there is no sharp boundary between a good university and an average one. A university may be good from one person's point of view, but may be considered as just an average one in another person's opinion. To solve a problem by a computer, a great deal of time and energy would be saved if the computer could understand either natural languages themselves or something close to them. In other words, computers would become more powerful if they could accept fuzzy concepts similar to the capability of a human brain.

Factoring techniques are incorporated in computer-oriented algorithms for the synthesis of fan-in limited NAND switching networks. Tree networks with reduced gate count or levels of logic are sought. While example FORTRAN programs emphasize computer execution of the algorithms, they are also efficient for hand execution.

An algorithm which reduces the number of gates and connections (diodes) in two-level, multiple-output combinational logic networks is presented and compared with conventional minimization procedures.