FUZZY ADDITIVE HYBRID OPERATORS FOR NETWORK-BASED DECISION-MAKING

Evidence Aggregation Networks based on multiplicative fuzzy hybrid operators were introduced by Krishnapuram and Lee. They have been used for image segmentation, pattern recognition, and general multicriteria decision making. One of the drawbacks to these networks is that the training is complex and quite time consuming. In this article, we modify these aggregation networks to implement additive fuzzy hybrid connectives. We study the theoretical properties of two classes of such aggregation operators, one where the union and intersection components are based on multiplication, and the other where these components are derived from Yager connectives. These new networks have similar excellent properties such as backpropagation training and node interpretability for decision making under uncertainty as do their multiplicative precursors. They also have the advantage that training is easier since the derivatives of the additive hybrid operators are not as complex in form. The appropriate training algorithms are derived, and several examples given to illustrate the properties of the networks. (C) 1994 John Wiley & Sons, Inc.