Fuzzy logic approach for knowledge modeling in an Ontology: A review

Kindo, Abdoul Azize; Kaladzavi, Guidedi; Malo, Sadouanouan; Camara, Gaoussou; Tapsoba, Theodore Marie Yves; Kolyang,

doi:10.1109/sccic51516.2020.9377335

Cited by 3 publications

(4 citation statements)

References 68 publications

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“…And it was made practically useful in the 1990s in Japan by Sujeno, who introduced fuzzy logic measures, integrals, and controllers in creating driver assistance and braking systems to protect drivers from careless collisions [9]. In the study by Zadeh et al and Kindo et al [7,10], Lotfi ZADEH highlighted fuzzy logic as a basis for the theory of possibilities through the definition of a set of mathematical principles for knowledge representation based on degrees of truth and membership rather than on the net membership of traditional binary or boolean logic (values 0 or 1) [7,10].…”

Section: Historical Backgroundmentioning

confidence: 99%

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Berbiche,

Hlyal,

El Alami

2024

ISI

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In light of recent mutations and economic volatility stemming from unforeseen global events and increasing security concerns, supply chains are confronted with the complex challenge of fulfilling uncertain customer demands while ensuring sustained value addition. This study introduces a novel approach utilizing fuzzy logic decision-making automation to address and mitigate the impact of current disruptions. By employing if-then scenarios, this methodology facilitates the generation of more accurate predictions and smarter supply planning, enabling effective decision-making, particularly in critical areas such as semiconductor supply sourcing. The integration of Artificial Intelligence within this framework provides dynamic visibility into real-world supply chain operations, thereby aiding in more informed and effective regulatory decisions and fostering continuous improvement. The core innovation of this research lies in the development of a unique Mamdani-fuzzy logic model designed to enhance supply planning. This model extends beyond the realm of efficient inventory management to encompass safer demand forecasting and market segmentation, showcasing its superiority in versatility and effectiveness compared to conventional methods, and leveraging expert knowledge to navigate uncertainties. Through the process of fuzzification, relevant indicators such as Consumption Severity, Market Sensitivity, and Commodity Importance are identified and modeled, with input variables being assigned membership functions and categorized into varying degrees of significance. The Mamdani-inference rules are then formulated, and the output of the fuzzy logic model is defuzzified using the centroid technique to derive precise supply quantities. Simulations conducted in MATLAB demonstrate the model's capacity to convert uncertainty into optimal supply measures across diverse scenarios, thereby enhancing safety and efficiency within the supply chain by minimizing excess inventory and preventing stockouts. This hybrid approach, combining mathematical reasoning with human expertise, validates the efficacy and robustness of fuzzy logic as a potent cognitive and modeling technique for facilitating precise and responsive decision-making in the face of unexpected incidents, imprecision, and qualitative factors affecting the supply chain.

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Section: Historical Backgroundmentioning

confidence: 99%

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Berbiche,

Hlyal,

El Alami

2024

ISI

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“…A promising way to describe subject areas is a combination of the principles of designing and developing ontological models and the principles of fuzzy logic [17,18]. The authors pointed out that the design of ontologies during the creation of complex software usually affects the adequacy of the model and causes inconsistency in the development process.…”

Section: Related Workmentioning

confidence: 99%

Approach to Implementation of Configuration Process for Adaptive Software Systems based on Ontologies

Fedasyuk,

Lutsyk

2023

IJC

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Analysis of scientific research on the development of adaptive and self-adaptive software systems is conducted. It is established that the use of machine learning methods and feedback diagrams is an effective way to design and develop adaptive software. It is determined that the existing methods do not fully provide the possibility of dynamic changes and expansion of functional and graphic characteristics. The software adaptation process is designed based on the ontological model using the semantic decision-making mechanism. The proposed method allows us to dynamically determine the necessary system characteristics and perform software adaptation. Modification process takes into account the information about currently active device based on data about the needs and requirements of the user. Using the results of designing an abstract approach to software configuration modification, an experimental study of the speed of generating optimal system settings is conducted. According to the results of the experiment, it is established that the new method demonstrates 20% better indicators of the speed of generating software settings compared to classical approaches.

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“…Fuzzy systems are a numerical modeling technique fitted to experimental data, which are widely used [1]. The distinction of fuzzy systems is the use of fuzzy sets, which possess the characteristic that their elements may belong to them partially (in a degree of membership), thus capturing the natural imprecision of human knowledge [2].…”

Section: Introductionmentioning

confidence: 99%

“…x n ], F i is the fuzzy sets, z l is the output of the R l rule and C l,k is the kth coefficient associated with the R l rule. The antecedent part of the rule is known as (1), and the consequent part of the rule is known as (2). In addition to the output z = {z i } of each rule, it is necessary to calculate the degree of truth ω = {ω i } of all the rules, to compute the output of a TSK fuzzy logic system.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Model Parameter and Structure Optimization Using Analytic, Numerical and Heuristic Approaches

Morales-Viscaya,

Alonso-Ramírez,

Castro-Liera

et al. 2023

Symmetry

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Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new methodology for structure and parameter tuning of Takagi–Sugeno–Kang fuzzy models using several optimization techniques. The output parameters are determined analytically, by finding the minimum of the root-mean-square error (RMSE) for a properly defined error function. The membership functions are simplified by considering symmetry and equispacing, to reduce the optimization problem of finding their parameters, and allow it to be carried out using the numerical method of gradient descent. Both algorithms are fast enough to finally implement a strategy based on the hill climbing approach to finding the optimal structure (number and type of membership functions) of the fuzzy system. The effectiveness of the proposed strategy is shown by comparing its performance, using four case studies found in current relevant works, to the popular adaptive network-based fuzzy inference system (ANFIS), and to other recently published strategies based on evolutionary fuzzy models. In terms of the RMSE, performance was at least 28% better in all case studies.

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Fuzzy logic approach for knowledge modeling in an Ontology: A review

Cited by 3 publications

References 68 publications

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Approach to Implementation of Configuration Process for Adaptive Software Systems based on Ontologies

Fuzzy Model Parameter and Structure Optimization Using Analytic, Numerical and Heuristic Approaches

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