A Choquet fuzzy integral-based approach to hierarchical network implementation is investigated. In this approach, we generalized the fuzzy integral as an excellent component for decision analysis. The generalization involves replacing the max (or min) operator in information aggregation with a fuzzy integral-based neuron, resulting in increased flexibility. The characteristics of the Choquet fuzzy integral are studied and a network-based decision-analysis framework is proposed. The trainable hierarchical network can be implemented utilizing the fuzzy integral-based neurons and connectives. The training algorithms are derived and several examples given to illustrate the behaviors of the networks. Also, we present a decision making experiment using the proposed network to learn appropriate functional relationships in the defective numeric fields detection domain.
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