Comparison of Mamdani and Sugeno Inference Systems for Dynamic Spectrum Allocation in Cognitive Radio Networks

Subhedar, Mansi; Birajdar, Gajanan K.

doi:10.1007/s11277-012-0845-6

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…The canonical structure of rules in Sugeno inference system is ‘If x is A and y is B then z = f ( x , y )’, where A and B are the fuzzy sets in the antecedent and f ( x , y ) is the crisp function in the consequent. Defuzzification is the process of converting the fuzzy set to crisp value . For defuzzification in Mamdani type inference system, the centre of gravity also known as the centroid of the output membership function is considered.…”

Section: Methodsmentioning

confidence: 99%

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan

Sundaraj

et al. 2015

Clinical Respiratory J

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Section: Methodsmentioning

confidence: 99%

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan

Sundaraj

et al. 2015

Clinical Respiratory J

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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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“…The proposed scheme solely depends on using the FLC, the HO rules, and the three input parameters; as previously highlighted. In spectrum mobility, FIS is applied to make the decision of spectrum access based on the condition of Us parameters such as received power, distance between and , arrival rate of , and the degree of mobility [22]. In this work, we considered three input parameters that have a significant effect on system performance.…”

Section: Proposed Ho Schemementioning

confidence: 99%

An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

Alhammadi¹,

Roslee²,

Alias³

et al. 2019

IJECE

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Cognitive radio networks (CRNs) play an important role in wireless communications which have the ability to significantly utilize the spectrum that not in used and reduce the current spectrum scarcity. CR allows unlicensed users (secondary users) to occupy the licensed spectrums without causing interference with licensed users (primary users). This can be achieved smoothly through four main CR procedures: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. In this paper, we propose an intelligent spectrum handoff (SH) scheme based on multiple attributes decision making. The handoff decision depends on three considered parameters: received power, traffic load and arrival rate of the primary users. The simulation results show the proposed scheme outperformed the conventional scheme by reducing the probability of SH which leads to improve system performance.

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Comparison of Mamdani and Sugeno Inference Systems for Dynamic Spectrum Allocation in Cognitive Radio Networks

Cited by 16 publications

References 21 publications

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

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

An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

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