Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing

Nasiri, Mahdi; Fober, Thomas; Senge, Robin; Hüllermeier, Eyke

doi:10.1109/ifsa-nafips.2013.6608488

Cited by 5 publications

(4 citation statements)

References 13 publications

(14 reference statements)

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“…Modeling of color yield in polyester dyeing 2013: this new model class was evaluated in the context of a concrete case study, namely the modeling of color yield in polyester high temperature dyeing as a function of disperse dyes concentration, temperature and time. (14) It showed that a hybrid modeling approach can outperform a purely data-driven and a purely knowledge-driven approach if expert knowledge and model calibration are combined in a suitable way.…”

Section: Discussionmentioning

confidence: 99%

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Literature review on artificial intelligence in dyeing and finishing processes

El Khaoudi,

El Bakkali,

Messnaoui

et al. 2024

Data and Metadata

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The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research.

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Section: Discussionmentioning

confidence: 99%

“…In their study, Nasiri et al (14) proposed a novel approach called fuzzy pattern trees for modeling fuzzy systems. This approach was developed as an alternative to rule-based system architectures that are commonly used but have perceived limitations.…”

Section: Introductionmentioning

confidence: 99%

Literature review on artificial intelligence in dyeing and finishing processes

El Khaoudi,

El Bakkali,

Messnaoui

et al. 2024

Data and Metadata

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“…In other words, the data is used to "calibrate" the structure of the model as specified by the expert. In the context of the colour yield application mentioned above, this approach has been used successfully in [13].…”

Section: Combined Knowledge-based and Data-driven Modelingmentioning

confidence: 99%

From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection

Hüllermeier

2017

Preprint

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This paper briefly elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for which it has originally been conceived, while being less apt to data-driven model design. An important reason that prevents fuzzy (rule-based) systems from being leveraged in large-scale applications is the flat structure of rule bases, along with the local nature of fuzzy rules and their limited ability to express complex dependencies between variables. This motivates alternative approaches to fuzzy systems modeling, in which functional dependencies can be represented more flexibly and more compactly in terms of hierarchical structures.

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“…Even if using of the data-driven approach is possible, transparency and interpretability of fuzzy systems built in this way is problematic (Chen et al 2018). It should be also remembered that transparency and interpretability of fuzzy systems is often highlighted as one of their key advantages, especially when compared to the "black box" approximation methods, such as Artificial Neural Networks (Nasiri et al 2013). Furthermore, an autonomous or a semi-autonomous (with a limited interaction with a human expert) defining rules from data can be also a demanding process itself (Castro et al 2001, Ishibuchi andYamamoto 2004).…”

Section: Introductionmentioning

confidence: 99%

Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

Macioł

2021

Nauka – Technika – Technologia. Tom 2

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The paper describes a solution of a problem of developing of fuzzy rules compliant with the Takagi-Sugeno approach where a number of available examples (observations) is not sufficient. Modeling of fuzzy premises and generating functions describing a dependence of a result variable on antecedents are described. In our original approach a problem of identification of membership functions of variables com-posing premises and a problem of consequent parameters identification are solved. For the first one, we used a simple technique based on individual judgements of experts. The second one is solved with a linear programming method. In particular, our approach to formulate the consequent parameter identification problem allows using of an extremely effective T-S method when a data-driven approach cannot be applied. In the paper we present a description of our methods and results of simulations of accuracy of the proposed approach, based on commonly known benchmarks. The achieved accuracy of classification is sufficient for the most of decision-making systems of an expert nature.

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Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing

Cited by 5 publications

References 13 publications

Literature review on artificial intelligence in dyeing and finishing processes

Literature review on artificial intelligence in dyeing and finishing processes

From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection

Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

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