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Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

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Computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

Fuzzy System Design Using Current Amplifier for 20 nm CMOS Technology

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Computers, Materials & Continua 2022年第7期72卷 1815-1829页

作者： Shruti Jain Cherry Bhargava Vijayakumar Varadarajan Ketan Kotecha Jaypee University of Information Technology Waknaghat172234India Symbiosis Institute of Technology Symbiosis International(Deemed University)Pune411021India School of Computer Science and Engineering The University of New South WalesSydneyNSW 2052Australia Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune411021India

In the recent decade,different researchers have performed hardware implementation for different applications covering various areas of *** this research paper,a novel analog design and implementation of different steps of fuzzy systems with current differencing buffered amplifier(CDBA)are proposed with a compact structure that can be used in many signal processing *** proposed circuits are capable of wide input current range,simple structure,and are highly *** electrical parameters were compared for the proposed fuzzy system when using different membership *** novelty of this paper lies in the electronic implementation of different steps for realizing a fuzzy system using current *** the power supply voltage of CDBA is 2V,it results in 155mW,power dissipation;4.615KΩ,input resistance;366KΩ,output resistances;and 189.09 dB,common-mode rejection ratio.A 155.519 dB,voltage gain,and 0.76V/μs,the slew rate is analyzed when the power supply voltage of CDBAis *** fuzzy system is realized in 20nm CMOS technology and investigated with an output signal of high precision and high speed,illustrating that it is suitable for realtime *** this research paper,a consequence of feedback resistance on the adder circuit and the defuzzified circuit is also analyzed and the best results are obtained using 100K *** structure has a low hardware complexity leading to a low delay and a rather high quality.

关键词： Current amplifiers membership functions fuzzy system fuzzy operators defuzzified circuit feedback resistance

Describing fuzzy sets using a new concept:fuzzify functor

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Optoelectronics Letters 2009年第1期5卷 69-72页

作者：魏克新王兆霞王权 Tianjin Key Laboratory of Control Theory & Applications in Complicated Systems Tianjin University of Technology Department of Electrical and Computer Engineering National University of Singapore

This paper proposed a fuzzify functor as an extension of the concept of fuzzy *** fuzzify functor and the first-order operated fuzzy set are *** the theory analysis,it can be observed that when the fuzzify functor acts on a simple crisp set,we get the first order fuzzy set or type-1 fuzzy *** operating the fuzzify functor on fuzzy sets,we get the higher order fuzzy sets or higher type fuzzy sets and their membership *** the fuzzify functor we can exactly describe the type-1 fuzz...

关键词： membership functions

Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

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IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 141-148页

作者： Runmei Li Chaoyang Jiang Fenghua Zhu Xiaolong Chen Beijing Jiaotong University the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application. © 2014 Chinese Association of Automation.

关键词： Data handling Forecasting Fuzzy sets membership functions Uncertainty analysis

MINER RULE CONSIDERING FUZZINESS OF STRESS DAMAGE UNDER DIFFERENT LOAD SEQUENCES

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Transactions of Nanjing University of Aeronautics and Astronautics 2008年第3期25卷 182-186页

作者：王旭亮聂宏南京航空航天大学航空宇航学院

Since the traditional Miner rule ignores the influence of the load sequence on the fatigue life, the fuzzy rules are used to analyze the fuzziness of the fatigue damage caused by the stress nearby the fatigue limit under different load sequences. The improved fuzzy Miner rule can reflect the influence of the load sequence on the fatigue life. Results of the example show that the prediction error can be reduced from 61.6% to 21.7%.

关键词： atigue damage fuzzy rules membership functions load sequence Miner rule

A Hybrid Structure of Fuzzy Neural Networks Generation via Q-Learning

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International Journal of Systems and Control

International Journal of Systems and Control 2007年第1期 90-101页

作者： Meng Joo Er School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798

In this paper, a hybrid approach termed Biased Dynamic Self-Generated Fuzzy Q-Learning (BDSGFQL) for automatically generating Fuzzy Neural Networks (FNNs) is proposed. In the proposed method, an FNN is generated via the Q-learning and the embedded human expert knowledge. The human expert knowledge is embedded as a bias of the system according to the condence level of the knowledge. The novel BDSGFQL methodology can also automatically create, delete and adjust fuzzy rules according to the evaluations of the entire system as well as the individual fuzzy rules. The salient characteristics of the BDSGFQL approach are: 1) Capable of embedding expert knowledge according to the condence level; 2) Capable of structure self-identication and automatic parameter estimation and modication; 3) FNNs can be quickly generated without supervised learning; 4) Fuzzy rules can be created, adjusted and deleted dynamically; 5) membership functions of an FNN can be dynamically adjusted according to the evaluation of reinforcement learning. Simulation studies of a wall-following task by a mobile robot demonstrate the superiority of the proposed method.

关键词： Biased Dynamic Self-Generated Fuzzy Q-Learning Ellipsoidal Basis Function membership functions

New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

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Journal of Computer and Communications 2024年第5期12卷 151-169页

作者： Elias Ayinbila Apasiya Abdul-Mumin Salifu Peter Awon-Natemi Agbedemnab Department of Computer Science School of Computing and Information Sciences C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

关键词： EMFES Breast Cancer Type-2 Fl Soft Computing membership functions Fuzzy Set Fuzzy Rules Risk Factors.