scholarly journals CLASSIFICATION OF SHORT TECHNICAL TEXTS USING SUGENO FUZZY INFERENCE SYSTEM

2021 ◽  
Vol 2021 (1) ◽  
pp. 16-27
Author(s):  
Andrei Viktorovich Borovsky ◽  
Elena Evgenievna Rakovskaya ◽  
Artem Leonidovich Bisikalo
Keyword(s):  
Fuzzy Inference ◽  
Functional Dependencies ◽  
Inference System ◽  
Operational Characteristics ◽  
Linguistic Rules ◽  
Design Documentation ◽  
Sugeno Model ◽  
Sets Theory ◽  
Technical Terms

The paper presents the results of classification of the short technical texts on the purpose of instruments using fuzzy sets theory and fuzzy logic. An important stage in designing special-purpose technical systems is the choice of equipment with specific operational characteristics. The need to categorize short technical texts, which present a brief description of equipment, annotations, fragments of databases, appears due to the fact that information about the equipment found in thematic abstract collections, technical and design documentation or in contextual advertising is often not structured and scattered. The other problems are a large number of typos, incorrect word usage and definitions in the texts. Much attention is paid to the characteristics of the objects of research and to recording their specific features – a large number of technical terms, abbreviations, symbols. The classifying technique is described, the expediency of application of fuzzy inference of Sugeno system associated with fuzziness of the natural language, the simplicity of mathematical calculations in the course of the experiment. A Sugeno model combines the description of the objects of research in the form of linguistic rules and functional dependencies. This approach greatly facilitates the interpretation of classification results

Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

2008 ◽  
Vol 36 (9) ◽  
pp. 1449-1457 ◽  
Author(s):  
Zoya Heydari ◽  
Farzam Farahmand ◽  
Hossein Arabalibeik ◽  
Mohamad Parnianpour
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 72 (3) ◽  
pp. 685-701 ◽  
Author(s):  
Rui Sun ◽  
Li-Ta Hsu ◽  
Dabin Xue ◽  
Guohao Zhang ◽  
Washington Yotto Ochieng
Keyword(s):  
Fuzzy Inference System ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Receiver Design ◽  
Single Variable ◽  
Gps Measurements ◽  
Inference System ◽  
Static Test ◽  
Neuro Fuzzy

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

2018 ◽  
pp. 326-363
Author(s):  
Jenicka S
Keyword(s):  
Land Cover ◽  
Fuzzy Inference ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Inference System ◽  
Texture Model ◽  
Remotely Sensed Image ◽  
Land Covers ◽  
Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

2019 ◽  
pp. 1247-1283
Author(s):  
Jenicka S.
Keyword(s):  
Land Cover ◽  
Fuzzy Inference ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Inference System ◽  
Texture Model ◽  
Remotely Sensed Image ◽  
Land Covers ◽  
Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

scholarly journals Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system

Heliyon ◽  
2019 ◽  
Vol 5 (8) ◽  
pp. e02046 ◽  
Author(s):  
Choug Abdelkrim ◽  
Mohamed Salah Meridjet ◽  
Nadir Boutasseta ◽  
Lakhdar Boulanouar
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Bearing Faults ◽  
Temporal Features ◽  
Neuro Fuzzy
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