This paper offers a new approach that applies the signatures to expert systems modelling. Signatures and their operators, viewed as a generalization of fuzzy signatures, represent a convenient framework for the symbolic representation of data. The models are derived by a three-step algorithm that maps the signatures onto expert systems. An expert systems modelling algorithm is given. Our algorithm has two inputs, the knowledge base, i.e., the rules, and the data base, i.e., the facts, and it constructs the signatures which represent models of expert systems. The algorithm is advantageous because of its systematic and general formulation allowing for the modelling of uncertain expert systems. The theoretical results are exemplified by a case study which produces models of a Bayesian expert system with mechatronics applications.

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No abstract