NETSYN : a connectionist approach to synthesis knowledge acquisition and use
journal contributionposted on 01.01.1992 by Nenad Ivezic, James Henry. Garrett, Carnegie Mellon University.Engineering Design Research Center.
Any type of content formally published in an academic journal, usually following a peer-review process.
Abstract: "The goal of machine learning for synthesis is the acquisition of the relationships between form, function, and behavior properties that satisfy design requirements. The proposed approach creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance for a posed artificial design problem. The objective of future work is to apply NETSYN to realistic design domains, specifically the domain of computer system design as practiced by M1."