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Enhancing Fingrams to deal with precise fuzzy systems

机译：增强Fingram以处理精确的模糊系统

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摘要

Interpretability is a highly valued capability of fuzzy systems that turns essential when dealing with human interaction. Precise fuzzy modeling prioritizes performance at the cost of harming interpretability. Fuzzy Inference-grams (Fingrams) permit the graphical representation of fuzzy systems facilitating their comprehension, analysis and interpretation at inference level. We enhance Fingrams to better represent and analyze precise fuzzy systems. A specific metric and new representations handle the particularities of such systems. A new visual artifact allows to discover the set of data instances not covered by a given fuzzy system. A novel visual representation allows to study in detail the elements that are involved in the inference of a single data instance. The potentials of the enhanced methodology are sketched by taking the Fuzzy Unordered Rule Induction Algorithm (FURIA) as an illustrative example of precise fuzzy system. For instance, a highly valuable representation is obtained for the stretching mechanism of FURIA, thus facilitating its comprehensibility. (C) 2015 Elsevier B.V. All rights reserved.
机译：可解释性是模糊系统的一项极有价值的功能，在处理人机交互时变得至关重要。精确的模糊建模以牺牲可解释性为代价，优先考虑性能。模糊推理图（Fingrams）允许模糊系统的图形表示，从而有助于在推理级别对其进行理解，分析和解释。我们增强Fingrams以便更好地表示和分析精确的模糊系统。特定的度量标准和新的表示方式可以处理此类系统的特殊性。一个新的视觉工件可以发现给定模糊系统未涵盖的数据实例集。新颖的视觉表示可以详细研究单个数据实例的推断所涉及的元素。通过将模糊无序规则归纳算法（FURIA）作为精确模糊系统的说明性示例，可以勾勒出增强方法的潜力。例如，对于FURIA的拉伸机制获得了非常有价值的表示形式，从而促进了其可理解性。 （C）2015 Elsevier B.V.保留所有权利。

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