1. Control applications with FPGA : case of approaching FPGAs for students in an intelligent control classDušan Fister, Alen Jakopič, Mitja Truntič, 2025, izvirni znanstveni članek Opis: Experience shows that knowledge transfer and understanding of fundamental FPGA principles are greatly improved by exercising laboratory practices and manual hands-on operations. Hence, a case study was performed on two didactic platforms for students of intelligent control techniques that were upgraded with FPGAs to be involved in laboratory practices. Among others, platforms allow implementation of traditional linear control algorithms, such as PID, or modern non-linear control algorithms, such as fuzzy logic or artificial neural networks. Initially, the underlying physics can be carefully studied, and the mathematical model can be derived. Then, such a model can be discretized into its digital form, an appropriate controller can be designed, and its performance can be compared to the known benchmark. Controllers and control parameters can be practiced by students themselves, offering underlying potential for improving students’ understanding of the fundamentals of FPGA.
Ključne besede: fuzzy logic controller, didactic tool, practicing laboratory works, understanding fundamental FPGA principles
Objavljeno v DKUM: 09.12.2025; Ogledov: 0; Prenosov: 1
 Celotno besedilo (11,85 MB) |
2. Multicriteria risk evaluation model : utilizing fuzzy logic for improved transparency and quality of risk evaluation in healthcareRok Drnovšek, Marija Milavec Kapun, Simona Šteblaj, Uroš Rajkovič, 2025, izvirni znanstveni članek Opis: Introduction: Risk management is essential for quality assurance in modern healthcare organizations. Risk matrices are widely used
to evaluate risks in healthcare settings; however, this approach has noteworthy weaknesses and limitations. This paper introduces
a novel risk evaluation model that utilizes multicriteria decision-making and fuzzy logic, to enhance the transparency and quality of the
risk evaluation process in healthcare.
Methods: The Multicriteria Evaluation Model was developed using the Decision Expert method and expert knowledge integration.
Fuzzy logic was integrated within the model, using partial degrees of membership and probabilistic analysis, to address uncertainties
inherent to healthcare risk evaluation. The evaluation model was tested with healthcare professionals active in the field of risk
management in clinical practice and compared with the risk matrix.
Results: The designed evaluation model utilizes multicriteria decision-making while encompassing the risk matrix framework to
boost user understanding and enable meaningful comparison of results. Compared with the risk matrix, the model provided similar or
marginally higher risk-level evaluations. The use of degrees of membership enables evaluators to articulate a wide range of plausible
risk consequences, which are often overlooked or ambiguously addressed in the traditional risk matrix approach.
Discussion and Conclusions: The evaluation model demonstrates increased transparency of the decision-making process and
facilitates in-depth analysis of the evaluation results. The utilization of degrees of membership revealed distinct strategies for handling
uncertainty among participants, highlighting the weaknesses of using single value evaluation approach for the presented and similar
decision problems. The presented approach is not limited to healthcare-related risk evaluation, but has the capacity to improve risk
evaluation practices in diverse settings
Ključne besede: multi-criteria decision-making, risk management, fuzzy logic, decision support, patient care, DEX
Objavljeno v DKUM: 01.12.2025; Ogledov: 0; Prenosov: 0
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4. A fuzzy model of power supply system controlJanez Usenik, 2012, izvirni znanstveni članek Opis: In this article, a mathematical model of control of a dynamic system is described; one such system could be a power supply system. Analytical approaches that have been developed to describe the influence of production and stock, i.e. additional capacities, require a hierarchical spatial pattern and demand. Demand is usually an inherently stochastic process, but in this article we simulate it as an output fuzzy variable in a fuzzy system, in which all the input variables are also fuzzy. Furthermore, an interesting use of neural sets is shown, which is presented as an efficient method for the optimisation of the fuzzy system. At the end, a numerical example is given.
Ključne besede: power supply system, energy capacities, demand, fuzzy logic, neural net
Objavljeno v DKUM: 10.07.2015; Ogledov: 2472; Prenosov: 40
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9. Decomposed fuzzy proportional-integral-derivative controllersMarjan Golob, 2001, izvirni znanstveni članek Opis: In this paper, several types of decomposed proportional-integral-derivative fuzzy logic controllers (PID FLCs) are tested and compared. An important feature of decomposed PID FLCs are their simple structures. In its simplest version, the decomposed PID FLC uses three one-input one-output inferences with three separate rule bases. Behaviours of proportional, integral and derivative PID FLC parts are defined with simple rules in proportional rule base, integral rule base and derivative rule base. The proposed decomposed PID FLC has been compared with several PID FLCs structures. All PID FLCs have been realised by the same hardware and software tools and have been applied as a real-time controller to a simple magnetic suspension system.
Ključne besede: fuzzy logic control, PID control, decomposed fuzzy system, magnetic suspension system
Objavljeno v DKUM: 01.06.2012; Ogledov: 2148; Prenosov: 114
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