A fuzzy neural tree based on likelihood

A novel type of fuzzy neural system is presented. It involves the neural tree concept and is termed as fuzzy neural tree (FNT). Each tree node uses a Gaussian as a fuzzy membership function so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership, thereby presenting a novel type of network. The tree is structured by the domain knowledge and parameterized by likelihood. The FNT is described in detail pointing out its various potential utilizations, in which complex modeling and multi-objective optimization are demanded. One of such utilizations concerns design. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design. © 2015 IEEE.