Temat: Type 2 fuzzy systems - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Methodological Proposal for Implementing Interval Type-2 Fuzzy Processors Over Digital Signal Controllers
Autorzy:: Forero, L. L.
Melgarejo, M.
Powiązania:: https://bibliotekanauki.pl/articles/108748.pdf
Data publikacji:: 2010
Wydawca:: Społeczna Akademia Nauk w Łodzi
Tematy:: fuzzy logic
Type-2 fuzzy systems
Fuzzy hardware
embedded systems
Opis:: This article presents a methodological proposal for implementing interval type-2 fuzzy processors over digital signal controller technology. We describe the main considerations that a practitioner or an engineer should follow when implementing an interval type-2 fuzzy system over an embedded processor. These considerations guide the implementation study of eight interval type-2 fuzzy processors, which are fully characterized and tested. Results show that by combining fast computing strategies and technologies like digital signal controllers, the inference time of an embedded type-2 fuzzy processor can be set to hundreds of microseconds.
Źródło:: Journal of Applied Computer Science Methods; 2010, 2 No. 1; 61-81
1689-9636
Pojawia się w:: Journal of Applied Computer Science Methods
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Hybrid Learning of Interval Type-2 Fuzzy Systems Based on Orthogonal Least Squares and Back Propagation for Manufacturing Applications
Autorzy:: Mendez, G.
Hernandez, A.
Powiązania:: https://bibliotekanauki.pl/articles/384517.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: type-2 fuzzy inference systems
type-2 neuro-fuzzy systems
hybrid learning
uncertain rule-based fuzzy logic systems
Opis:: This paper presents a novel learning methodology based on the hybrid algorithm for interval type-2 (IT2) fuzzy logic systems (FLS). Since in the literature only back-propagation method has been proposed for tuning of both antecedent and consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses recursive orthogonal least-squares method for tuning of consequent parameters as well as the back-propagation method for tuning of antecedent parameters. The systems were tested for three types of inputs: a) interval singleton b) interval type-1 (T1) non-singleton, c) interval type-2 non-singleton. The experimental results of the application of the hybrid interval type-2 fuzzy logic systems for scale breaker entry temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that the hybrid learning interval type-2 fuzzy logic systems improve performance in scale breaker entry temperature prediction under the tested condition.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 23-32
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A novel approach to type-reduction and design of interval type-2 fuzzy logic systems
Autorzy:: Starczewski, Janusz T.
Przybyszewski, Krzysztof
Byrski, Aleksander
Szmidt, Eulalia
Napoli, Christian
Powiązania:: https://bibliotekanauki.pl/articles/2147137.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: smooth type-reduction
interval type-2 fuzzy logic systems
Opis:: Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 3; 197--206
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: An analytical insight to investigate the research patterns in the realm of Type-2 fuzzy logic
Autorzy:: Vij, S.
Jain, A.
Tayal, D.
Castillo, O.
Powiązania:: https://bibliotekanauki.pl/articles/951726.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: scientometric analysis
Type 2 fuzzy logic
Type 2 fuzzy systems
Type 2 fuzzy control
Type 2 fuzzy set
analiza scjentometryczna
logika rozmyta typu 2
systemy rozmyte typu 2
Opis:: Fuzzy logic has always been one of the key research areas in the field of computer science as it helps in dealing with the real world vagueness and uncertainty. In recent years, a variant of it, Type-2 Fuzzy Logic has gained enormous popularity for research purposes. In this paper, an analytical insight is provided into the research patterns of Type-2 Fuzzy logic. Web of Science has been used as the data source which consists of Science Citation Index- Expanded (SCI-E), SSCI, A&HCI and ESCI indexed research papers. 600 research papers were extracted from it in the field of Type-2 fuzzy logic from the year 2000 to 2016, which are analyzed both manually and in an automated manner. The performed study is Scientometric in nature and helps in answering research questions like control terms and top authors in this field, the growth pattern in research publications, top funding agencies and countries etc. The major goal of this study is to analyze the research work in type-2 fuzzy logic so as to track the growth of this discipline through the years and envision future trends in this area.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 2; 3-32
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Users-centric adaptive learning system based on interval type-2 fuzzy logic for massively crowded e-learning platforms
Autorzy:: Almohammadi, K.
Hagras, H.
Alghazzawi, D.
Aldabbagh, G.
Powiązania:: https://bibliotekanauki.pl/articles/91622.pdf
Data publikacji:: 2016
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: Type-2 Fuzzy Logic systems
e-learning
intelligent learning environments
Opis:: Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 2; 81-101
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Triangular fuzzy-rough set based fuzzification of fuzzy rule-based systems
Autorzy:: Starczewski, Janusz T.
Goetzen, Piotr
Napoli, Christian
Powiązania:: https://bibliotekanauki.pl/articles/1837416.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: general type-2 fuzzy logic systems
fuzzy-rough fuzzification
regular type-2 t-norms
cropped triangular secondary membership functions
Opis:: In real-world approximation problems, precise input data are economically expensive. Therefore, fuzzy methods devoted to uncertain data are in the focus of current research. Consequently, a method based on fuzzy-rough sets for fuzzification of inputs in a rulebased fuzzy system is discussed in this paper. A triangular membership function is applied to describe the nature of imprecision in data. Firstly, triangular fuzzy partitions are introduced to approximate common antecedent fuzzy rule sets. As a consequence of the proposed method, we obtain a structure of a general (non-interval) type-2 fuzzy logic system in which secondary membership functions are cropped triangular. Then, the possibility of applying so-called regular triangular norms is discussed. Finally, an experimental system constructed on precise data, which is then transformed and verified for uncertain data, is provided to demonstrate its basic properties.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 271-285
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: A constructive approach to managing fuzzy subsets of type 2 in a decision making
Autorzy:: Dymova, L.
Powiązania:: https://bibliotekanauki.pl/articles/1931580.pdf
Data publikacji:: 2003
Wydawca:: Politechnika Gdańska
Tematy:: fuzzy systems of type 2
fuzzy sets of type 2
hyperfuzzy sets
decision making
Opis:: The aim of this paper is to present a constructive methodology and algorithms for operations with fuzzy sets of type 2. The need to elaborate this methodology came from practical problems of Decision Making. To realize the methodology, some simplifications of the problem have been introduced. Particularly, only the trapezium form of membership functions was used. To highlight the difference between the proposed approach and the classical theory of fuzzy sets of type 2, the terms "hyperfuzzy set" and "hyperfuzzy function" have been introduced. Some base situations of hyperfuzzy functions with real arguments and real functions of hyperfuzzy arguments are performed.
Źródło:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 157-164
1428-6394
Pojawia się w:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Fuzzy inference neural networks with fuzzy parameters
Autorzy:: Rutkowska, D.
Hayashi, Y.
Powiązania:: https://bibliotekanauki.pl/articles/1931581.pdf
Data publikacji:: 2003
Wydawca:: Politechnika Gdańska
Tematy:: neuro-fuzzy systems
fuzzy neural networks
fuzzy inference neural networks
fuzzy systems of type 2
fuzzy granulation
Opis:: This paper concerns fuzzy neural networks and fuzzy inference neural networks, which are two different approaches to neuro-fuzzy combinations. The former is a direct fuzzification of artificial neural networks by introducing fuzzy signals and fuzzy weights. The latter is a representation of fuzzy systems in the form of multi-layer connectionist networks, similar to neural networks. Parameters of membership functions (centers and widths) play the role of neural network weights. In this paper, fuzzy inference neural networks with fuzzy parameters are considered. Neuro-fuzzy systems of this kind utilize both approaches: fuzzy neural networks and fuzzy inference neural networks. They also pertain to fuzzy systems of type 2 since membership functions with fuzzy parameters characterize type 2 fuzzy sets. Various architectures of these networks have been obtained for fuzzy systems based on different fuzzy implications. By analogy with fuzzy inference neural networks with crisp parameters, methods of learning fuzzy parameters and rule generation can be derived for neuro-fuzzy systems with fuzzy parameters. Fuzzy inference neural networks are studied in the framework of fuzzy granulation. In particular, fuzzy clustering as fuzzy information granulation is proposed to be applied in order to generate fuzzy IF-THEN rules. Applications of fuzzy inference neural networks are also outlined.
Źródło:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 7-22
1428-6394
Pojawia się w:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Three-dimensional fuzzy control of ultrasonic cleaning
Autorzy:: Morkun, Volodymyr
Kravchenko, Olha
Powiązania:: https://bibliotekanauki.pl/articles/2115809.pdf
Data publikacji:: 2021
Wydawca:: Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:: ultrasonic cleaning
3D fuzzy interval type 2 logic
modeling
spatially distributed systems
Opis:: Consideration of ultrasonic cleaning as a process with distributed parameters enables reduction of power consumption. This approach is based on establishment of control over the process depending on fixed values of ultrasonic responses in set points. The initial intensity of radiators is determined using a three-dimensional (3D) interval type-2 fuzzy logic controller essentially created for processes with distributed parameters, as well as complex expert evaluation of the input data. The interval membership functions for the input and output data consider the space heterogeneity of ultrasonic cleaning. A rule base is formed, which is 2D and not dependent upon the number of input and output parameters. A model illustrating ultrasonic cleaning with a 3D interval type-2 fuzzy logic controller is designed. Comparative analysis of the output parameters of the proposed model and the traditional method indicates an increase in the energy efficiency by 41.17% due to application of only those ultrasonic radiators that are located next to the contamination.
Źródło:: Acta Mechanica et Automatica; 2021, 15, 3; 169--176
1898-4088
2300-5319
Pojawia się w:: Acta Mechanica et Automatica
Dostawca treści:: Biblioteka Nauki

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