Approach the Interval Type-2 Fuzzy System and PSO Technique in Landcover Classification

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-42058-1_34 ◽

2020 ◽

pp. 402-414 ◽

Author(s):

Dinh Sinh Mai ◽

Long Thanh Ngo ◽

Le Hung Trinh

Keyword(s):

Fuzzy System ◽

Landcover Classification ◽

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FPGA Implementation of Interval Type-2 Fuzzy System Based on Nie-Tan Algorithm

2020 32nd International Conference on Microelectronics (ICM) ◽

10.1109/icm50269.2020.9331498 ◽

2020 ◽

Author(s):

R. Maciel ◽

R. L. Moreno ◽

T. C. Pimenta ◽

P. M. S. R. Rizol

Keyword(s):

Fuzzy System ◽

Fpga Implementation ◽

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EEG signals classifications of motor imagery using adaptive neuro-fuzzy inference system and interval type-2 fuzzy system

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2017.084220 ◽

2017 ◽

Vol 16 (2) ◽

pp. 106

Author(s):

Shereen A. El aal ◽

Rabie A. Ramadan ◽

Neveen I. Ghali

Keyword(s):

Motor Imagery ◽

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Eeg Signals ◽

Inference System ◽

Neuro Fuzzy ◽

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Sliding Mode Control for Interval Type-2 Fuzzy System Under Fading Channels

2020 IEEE 16th International Conference on Control & Automation (ICCA) ◽

10.1109/icca51439.2020.9264383 ◽

2020 ◽

Author(s):

Yekai Yang ◽

Yugang Niu ◽

Bei Chen ◽

Yuanyuan Zou

Keyword(s):

Sliding Mode Control ◽

Fading Channels ◽

Fuzzy System ◽

Sliding Mode ◽

Mode Control ◽

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A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2013.2255613 ◽

2013 ◽

Vol 21 (3) ◽

pp. 492-509 ◽

Author(s):

Yang-Yin Lin ◽

Jyh-Yeong Chang ◽

N. R. Pal ◽

Chin-Teng Lin

Keyword(s):

Fuzzy System ◽

Interval Type ◽

Recurrent Interval ◽

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FPGA implementation of a new interval type-2 Beta neuro-fuzzy system with on-chip learning for image denoising application

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2016.06.011 ◽

2016 ◽

Vol 55 ◽

pp. 164-179 ◽

Author(s):

Manel Elloumi ◽

Mohamed Krid ◽

Dorra Sellami Masmoudi

Keyword(s):

Image Denoising ◽

Fuzzy System ◽

Fpga Implementation ◽

Neuro Fuzzy ◽

On Chip ◽

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Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability

IEEE Transactions on Cybernetics ◽

10.1109/tsmcb.2012.2230253 ◽

2013 ◽

Vol 43 (6) ◽

pp. 1781-1795 ◽

Author(s):

Chia-Feng Juang ◽

Chi-You Chen

Keyword(s):

Fuzzy System ◽

Data Driven ◽

High Learning ◽

Improved Model ◽

Interval Type ◽

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Interval Type-2 Fuzzy System for a safe autonomous robot navigation in uncertain environment

2014 XL Latin American Computing Conference (CLEI) ◽

10.1109/clei.2014.6965170 ◽

2014 ◽

Author(s):

Wanderson A. S. Silva ◽

Ricardo A. L. Rabelo ◽

A. M. Santana

Keyword(s):

Fuzzy System ◽

Robot Navigation ◽

Autonomous Robot ◽

Uncertain Environment ◽

Autonomous Robot Navigation ◽

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Vibration Control Design for a Plate Structure with Electrorheological ATVA Using Interval Type-2 Fuzzy System

Applied Sciences ◽

10.3390/app7070707 ◽

2017 ◽

Vol 7 (7) ◽

pp. 707 ◽

Author(s):

Chih-Jer Lin ◽

Chun-Ying Lee ◽

Ying Liu

Keyword(s):

Vibration Control ◽

Fuzzy System ◽

Control Design ◽

Plate Structure ◽

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A Comparative Study of Membership Functions for an Interval Type-2 Fuzzy System Used for Dynamic Parameter Adaptation in Particle Swarm Optimization

Recent Developments and New Direction in Soft-Computing Foundations and Applications - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-319-32229-2_26 ◽

2016 ◽

pp. 373-385

Author(s):

Frumen Olivas ◽

Fevrier Valdez ◽

Oscar Castillo

Keyword(s):

Particle Swarm Optimization ◽

Comparative Study ◽

Fuzzy System ◽

Particle Swarm ◽

Dynamic Parameter ◽

Membership Functions ◽

Swarm Optimization ◽

Parameter Adaptation ◽

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A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.02.020 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-9

Author(s):

Farheen Azad ◽

Keyword(s):

Fuzzy Set ◽

Fuzzy System ◽

Probabilistic Reasoning ◽

Fuzzy Theory ◽

Physical World ◽

Fundamental Aspect ◽

Fuzzy Classifiers ◽

Human Thinking ◽

This review paper of fuzzy classifiers with improved interpretability and accuracy parameter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership function. Also, this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system.

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