Comparison between Mamdani and Sugeno fuzzy inference systems for the mitigation of environmental temperature variations in OCDMA-PONs

Reis, Jose V. dos; Raddo, Thiago R.; Sanches, Anderson L.; Borges, Ben‐Hur V.

doi:10.1109/icton.2015.7193509

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Similar comparative studies were conducted in various fields, such as optical [19] and wireless [20] networks, magnetic bearing system control [21], chaotic time series prediction [22], evaluation of user experience for specific applications [23], and streamflow prediction in the hydrological field [24]. Although Takagi-Sugeno usually showed better results, the research in the hydrological domain showed that the Mamdani FIS is more suitable.…”

Section: Literature Reviewmentioning

confidence: 71%

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

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The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

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Section: Literature Reviewmentioning

confidence: 71%

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

View full text Add to dashboard Cite

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“…Fuzzy Inference Systems are based on two methods: the Mamdani Fuzzy Inference technique [21,[35][36][37][38][39][40] and the Takagi-Sugeno-Kang Inference method [35,36,38,39,[41][42][43][44][45][46][47][48]. The major difference between them lies in the consequent fuzzy rules and defuzzification procedures; the Mamdani Inference method uses fuzzy sets as rule consequent, while Sugeno Inference considers linear functions of input variables.…”

Section: Mamdani Fuzzy Inference Mppt Controller Designmentioning

confidence: 99%

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

Luta

Raji

2019

Energies

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The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

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Review and Investigation of Simplified Rules Fuzzy Logic Speed Controller of High Performance Induction Motor Drives

et al. 2020

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The use of Fuzzy Logic Controller (FLC) as a speed controller for Induction Motor (IM) drives is garnering strong researchers' interest since it has proven to achieve superior performance compared to conventional controllers. The aim of this study is to review and investigate the design, operations, and effects of rules reduction for FLC in IM drives. Based on the literature, the most commonly used technique to design FLC Membership Functions (MFs) rule-base and control model is based on engineering skills and experienced behavioral aspects of the controlled system. Simplified fuzzy rules approaches have been introduced to reduce the number of fuzzy rules in order to realize hardware implementation. This study discusses different simplified rules methods applied to IM drives. Most of the proposed methods shared a common drawback in that they lacked systematic procedures for designing FLC rule base. Therefore, this research proposed a methodological approach to designing and simplifying the FLC rule-base for IM drives based on dynamic step response and phase plane trajectory of the second order representation of IM drives systems. The proposed method presents guidance for designing FLC rule-base based on the general dynamic step response of the controlled system. Following the proposed method procedures, a (9, 25, 49) rules size has been designed and simplified to a (5, 7, 9) rules size. The effectiveness and accuracy of the designed rules as well as the simplified rules were verified by conducting simulation analysis of IM drives using MATLAB/Simulink environment. Step speed command performance comparisons were achieved with both standard designed and simplified rules at various speed demands. The simulation results showed that the simplified rules maintain the drive performance and produced similar behavior as the standard designed rules.

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Comparison between Mamdani and Sugeno fuzzy inference systems for the mitigation of environmental temperature variations in OCDMA-PONs

Cited by 4 publications

References 16 publications

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

Review and Investigation of Simplified Rules Fuzzy Logic Speed Controller of High Performance Induction Motor Drives

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