A Fuzzy Model for Ni-Cd Batteries

Sarvi, Mohammad; Safari, Masoud

doi:10.11591/ij-ai.v2i2.1784

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…This system is a non-linear learning model that uses least-square performance metrics together with back-propagation methods to train the fuzzy inference system's membership functions (MF) and its included parameters based on input and output data sets (Kim et al, 2013). The advantages of ANFIS over traditional estimation methods are as follows: (1) ANFIS represents a powerful approach for building complex and non-linear relationships between input and output data; (2) ANFIS does not require a large number of accurate measured data; (3) it has fast learning and adaptation capability (Sarvi & Safari, 2013;Turkmen, Yildiz, Guney, & Kaya, 2009) and (4) the main strength of ANFIS models is that they are universal approximations with the ability to solicit interpretable If-Then rules (Jang & Sun, 1995). To illustrate the procedures of an ANFIS, for simplicity, we consider only two inputs x, y and one output f out in this system.…”

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods

Ziari

Sobhani²,

Ayoubinejad

et al. 2015

Road Materials and Pavement Design

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The accuracy of pavement performance prediction is a critical part of pavement management and directly influences maintenance and rehabilitation strategies. Many models with various specifications have been proposed by researchers and used by agencies. This study presents nine variables affecting pavement condition and analyses the accuracy of the Group Method of Data Handling (GMDH) and Adaptive Neuro Fuzzy Inference System (ANFIS) models in predicting pavement performance in short and long terms of a pavement life cycle. The nine effective variables are extracted from groups of pavement age, traffic conditions, environmental changes and pavement structure. The International Roughness Index (IRI) is used as the pavement performance index. Results show that GMDH with 1st, 2nd, 3rd and 4th-order polynomials is not able to predict pavement performance for short and long terms. However, the specified ANFIS models can accurately predict pavement performance in the short term, but not in the long term.

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Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods

Ziari

Sobhani²,

Ayoubinejad

et al. 2015

Road Materials and Pavement Design

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Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for Photovoltaic Applications

Selmani¹,

Ed-Dahhak²,

Outanoute³

et al. 2016

IJPEDS

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Lead-acid batteries have been the most widely used energy storage units in stand-alone photovoltaic (PV) applications. To make a full use of those batteries and to improve their lifecycle, high performance charger is often required. The implementation of an advanced charger needs accurate information on the batteries internal parameters. In this work, we selected CIEMAT model because of its good performance to deal with the widest range of lead acid batteries. The performance evaluation of this model is based on the co-simulation LabVIEW/Multisim. With the intention of determining the impact of the charging process on batteries, the behaviour of different internal parameters of the batteries was simulated. During the charging mode, the value of the current must decrease when the batteries’ state of charge is close to be fully charged.

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A Fuzzy Model for Ni-Cd Batteries

Cited by 2 publications

References 11 publications

Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods

Analysing the accuracy of pavement performance models in the short and long terms: GMDH and ANFIS methods

Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for Photovoltaic Applications

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