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A neural-fuzzy modelling framework based on granular computing: Concepts and applications

机译：基于粒度计算的神经模糊建模框架：概念与应用

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摘要

Fuzzy and neural-fuzzy systems have successfully and extensively applied to solve problems in many research areas such as those associated with industrial, medical and academic applications. However, recent trends reveal a demand for a workflow with a particular emphasis on transparency, simplicity, system interpretability as well as on a strong link with human cognition. Such requirement is mainly driven by research areas where expert knowledge is of very high importance and any new proposed modelling system falls under the interpretability scrutiny of experts in order to confirm the system's validity. The relatively recent paradigm of granular computing (GrC) offers an ideal opportunity for a transparent knowledge discovery methodology to be combined with fuzzy logic thereby towards a systematic modelling framework with a focus on the overall transparency of the system. Such transparency in the workflow allows for better interaction between the expert process knowledge and the system design which translates into a better performing system. In this paper a systematic modelling approach using granular computing (GrC) and neural-fuzzy modelling is presented. In this research study a GrC algorithm is used to extract relational information and data characteristics out of an initial database. The extracted knowledge and granular features are then translated into a linguistic rule-base of a fuzzy system. This rule-base is finally elicited and optimised via a neural-fuzzy modelling structure. During the various steps of this methodology the transparency features are highlighted and it is shown here how the system designer can take advantage of such features to enhance the system. The proposed modelling framework is applied to a multi-dimensional and complex data set consisting of measurements of mechanical properties of heat treated steel. The data set is collected from a real industrial process and the measurements are dictated by customer production demands and the data set is very sparse with many discontinuities. The proposed framework successfully models the mechanical properties of heat treated steel and it further improves upon the performance of previously established modelling structures.
机译：模糊和神经模糊系统已成功且广泛地用于解决许多研究领域中的问题，例如与工业，医学和学术应用有关的问题。但是，最近的趋势表明，对工作流的需求特别强调透明性，简单性，系统可解释性以及与人类认知的紧密联系。这种要求主要是由研究领域驱动的，在这些领域中，专家知识非常重要，任何新提出的建模系统都必须经过专家的可解释性审查，才能确认系统的有效性。相对较新的粒度计算范式（GrC）为透明知识发现方法与模糊逻辑结合提供了理想的机会，从而形成了以系统总体透明为重点的系统建模框架。工作流中的这种透明性允许专家过程知识与系统设计之间更好的交互，从而转化为性能更好的系统。本文提出了一种使用粒度计算（GrC）和神经模糊建模的系统建模方法。在这项研究中，使用GrC算法从初始数据库中提取关系信息和数据特征。然后将提取的知识和粒度特征转换为模糊系统的语言规则库。最终通过神经模糊建模结构来引出并优化该规则库。在此方法的各个步骤中，将突出显示透明功能，并在此处显示系统设计人员如何利用这些功能来增强系统。所提出的建模框架被应用于多维和复杂的数据集，该数据集包括对热处理钢的机械性能的测量。数据集是从真实的工业过程中收集的，并且测量结果是由客户的生产需求决定的，并且该数据集非常稀疏，具有许多不连续性。提出的框架成功地模拟了热处理钢的机械性能，并进一步改善了先前建立的建模结构的性能。

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