Imprecise reasoning using neural networks

Abstract

A logic is defined that weighs all available information and implements it using an emulated neural network. This allows the resulting expert system to be able to learn through examples. It also handles fuzziness in the facts and the rules, as well as the logical operations, in a natural and uniform way. It is more realistic than the certainty factor formalism which leaves out information because it takes the minimum of the certainty factors for the AND operation and maximum of the certainty factors for the OR operation. In the present scheme, all activations are weighted and taken into account. Compared with classical expert systems, the present system has the advantage of operating in two modes. In the normal mode, rules are given by experts and weights are assigned values. In the learning mode, weights are allowed to vary while the system is fed with examples.