Possibilistic reasoning in a computational neural network
Andreas Kanstein, M. Thomas, K. Goser · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
Possibilistic reasoning is implemented in a computational neural network for the formulation of a new classification system. The possibilistic classification is derived in analogy to the reasoning used in Bayesian classifiers. A principle of relational consistency is introduced to establish a connection of possibility and probability distributions. It is shown that possibilistic classification is suitable if distributions of very small classes like system failure data tend to be covered by distributions of large clusters. The classification system is also a paradigm for the implementation of a fuzzy logic system in a neural network architecture.