Network-Based Decision Making Via Generalized Fuzzy Integral Operators

Recent advances in network-based decision making methods have given rise to computationally efficient solution methodologies for intelligent systems. One type of hierarchical network implementation, the fuzzy integral operator approach, is investigated. In this approach, we generalized the Choquet fuzzy integral as an excellent component for decision analysis and making. This involves extending the standard operators in information aggregation with generalized operators, resulting in increased flexibility. The characteristics of the Choquet fuzzy integrals and their generalizations are addressed and network-based decision making frameworks are then proposed. The trainable hierarchical networks are able to perceive and interpret complex decisions by using those processing elements called neurons. We also present a decision making experiment using the proposed network to learn appropriate functional relationships in the defective numeric fields detection domain.