Wszystkie pola: Fuzzy interval - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Group decision making using interval-valued intuitionistic fuzzy soft matrix and confident weight of experts
Autorzy:: Das, S.
Kar, S.
Pal, T.
Powiązania:: https://bibliotekanauki.pl/articles/91665.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: multiple attribute group decision making
MAGDM
interval-valued intuitionistic fuzzy soft matrix
IVIFSM
interval-valued intuitionistic fuzzy soft sets
IVIFSSs
Opis:: This article proposes an algorithmic approach for multiple attribute group decision making (MAGDM) problems using interval-valued intuitionistic fuzzy soft matrix (IVIFSM) and confident weight of experts. We propose a novel concept for assigning confident weights to the experts based on cardinals of interval-valued intuitionistic fuzzy soft sets (IVIFSSs). The confident weight is assigned to each of the experts based on their preferred attributes and opinions, which reduces the chances of biasness. Instead of using medical knowledgebase, the proposed algorithm mainly relies on the set of attributes preferred by the group of experts. To make the set of preferred attributes more important, we use combined choice matrix, which is combined with the individual IVIFSM to produce the corresponding product IVIFSM. This article uses IVIFSMs for representing the experts’ opinions. IVIFSM is the matrix representation of IVIFSS and IVIFSS is a natural combination of interval-valued intuitionistic fuzzy set (IVIFS) and soft set. Finally, the performance of the proposed algorithm is validated using a case study from real life.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 57-77
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: 2.

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: 3.

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: 4.

Tytuł:: Type-reduction of the discretised interval type-2 fuzzy set : approaching the continuous case through progressively finer discretisation
Autorzy:: Greenfield, S.
Chiclana, F.
Powiązania:: https://bibliotekanauki.pl/articles/91818.pdf
Data publikacji:: 2011
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: defuzzification
type reduction
exhaustive method
Type-Reduced Set
Greenfield-Chiclana Collapsing Defuzzifier
Representative Embedded Set Approximation
Nie-Tan Method
Nie-Tan Set
discretisation
Opis:: The defuzzification of a type-2 fuzzy set is a two stage process consisting of firstly type-reduction, and secondly defuzzification of the resultant type-1 set. This paper considers three approaches to discrete interval type-reduction: 1. The exhaustive method which produces the Type-Reduced Set, 2. the Greenfield-Chiclana Collapsing Defuzzifier which gives rise to the Representative Embedded Set Approximation, and 3. the Nie-Tan Method from which the Nie-Tan Set is derived. In the discrete case these three type-1 sets are distinct. The behavior of the three sets under fine discretisation is investigated experimentally, in order to shed light on the relationships between the continuous versions of these type-1 sets.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 3; 183-193
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: 5.

Tytuł:: Handwrittenword recognition using fuzzy matching degrees
Autorzy:: Wróbel, Michał
Starczewski, Janusz T.
Fijałkowska, Justyna
Siwocha, Agnieszka
Napoli, Christian
Powiązania:: https://bibliotekanauki.pl/articles/2031113.pdf
Data publikacji:: 2021
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: offline handwriting recognition
handwritten strokes
fuzzy matching degrees
interval type-2 fuzzy sets
decision trees
bigram frequency
Opis:: Handwritten text recognition systems interpret the scanned script images as text composed of letters. In this paper, efficient offline methods using fuzzy degrees, as well as interval fuzzy degrees of type-2, are proposed to recognize letters beforehand decomposed into strokes. For such strokes, the first stage methods are used to create a set of hypotheses as to whether a group of strokes matches letter or digit patterns. Subsequently, the second-stage methods are employed to select the most promising set of hypotheses with the use of fuzzy degrees. In a primary version of the second-stage system, standard fuzzy memberships are used to measure compatibility between strokes and character patterns. As an extension of the system thus created, interval type-2 fuzzy degrees are employed to perform a selection of hypotheses that fit multiple handwriting typefaces.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 229-242
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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