Temat: fuzzy logic type 2 - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Research trends on fuzzy logic controller for mobile robot navigation : a scientometric study
Autorzy:: Rani, Somiya
Jain, Amita
Castillo, Oscar
Powiązania:: https://bibliotekanauki.pl/articles/384308.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: fuzzy logic controller
autonomous mobile robot navigation
type-2 fuzzy logic
optimized fuzzy controller
Opis:: The present study shows the scientometric analysis of the publications on the fuzzy logic controller in autonomous mobile robot navigation during the period 2000 to 2018. The data is collected using Web of Science core collection database and analyzed at various levels such as Web of Science categories, publication years, document types, funding agencies, authors, research areas, countries or region, control terms, and organization to evaluate the research patterns. An extensive study is done to find the research trends in this area.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 87-108
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: 2.

Tytuł:: Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and its Optimization with Genetic Algorithms
Autorzy:: Hidalgo, D.
Castillo, O.
Melin, P.
Powiązania:: https://bibliotekanauki.pl/articles/384559.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: modular neural networks
type-2 fuzzy logic
pattern recognition
genetic algorithms
Opis:: We describe in this paper a comparative study between Fuzzy Inference Systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 59-73
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:: 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: 4.

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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