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Abstract: This paper discusses interpretability in two main categories of fuzzy systems – fuzzy rule-based classifiers and interpolative fuzzy systems. Our goal is to show that the aspect of high level interpretability is more relevant to fuzzy classifiers, whereas fuzzy systems employed in modeling and control benefit more from low-level interpretability. We also discuss the interpretability–accuracy tradeoff and observe why various rule weighting schemes that have been brought into play to increase adaptability of fuzzy systems rather just increase computational overhead and seriously compromise interpretability of fuzzy systems. [Copyright &y& Elsevier]

Abstract: Transparency, accuracy, compactness and reliability all appear to be vital (even though somewhat contradictory) requirements when it comes down to linguistic fuzzy modeling. This paper presents a methodology for simultaneous optimization of these criteria by chaining previously published various algorithms – a heuristic fully automated identification algorithm that is able to extract sufficiently accurate, yet reliable and transparent models from data and two algorithms for subsequent simplification of the model that are able to reduce the number of output parameters as well as the number of fuzzy rules with only a marginal negative effect to the accuracy of the model. [Copyright &y& Elsevier]