A hierarchical knowledge-based environment for linguistic modeling: Models and iterative methodology

Although linguistic models are highly descriptive, they suffer from inaccuracy in some complex problems. This fact is due to problems related to the inflexibility of the linguistic rule structure that has been considered. Moreover, methods often employed to design these models from data are also biased by the former structure and by their nature, which is close to prototype identification algorithms. In order to deal with these problems of linguistic modeling, an extension of the knowledge base of linguistic fuzzy rule-based systems was previously introduced, i.e., the hierarchical knowledge base (HKB) (IEEE Trans. Fuzzy Systems 10 (1) (2002) 2). Hierarchical linguistic fuzzy models, derived from this structure, are viewed as a class of local modeling approaches. They attempt to solve a complex modeling problem by decomposing it into a number of simpler linguistically interpretable subproblems. From this perspective, linguistic modeling using an HKB can be regarded as a search for a decomposition of a non-linear system that gives a desired balance between the interpretability and the accuracy of the model. Using this approach, we are able to effectively explore the fact that the complexity of the systems is usually not uniform. We propose a well-defined hierarchical environment adopting a more general treatment than the typical prototype-oriented learning methods. This iterative hierarchical methodology takes the HKB as a base and performs a wide variety of linguistic modeling. More specifically, from fully interpretable to fully accurate, as well as intermediate trade-offs, hierarchical linguistic models. With the aim of analyzing the behavior of the proposed methodology, two real-world electrical engineering distribution problems from Spain have been selected. Successful results were obtained in comparison with other system modeling techniques.