Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring

Roshani, Gholam Hossein; Feghhi, S.A.H.; Adineh-Vand, A.; Khorsandi, M.

doi:10.1016/j.measurement.2013.07.005

Cited by 35 publications

(9 citation statements)

References 18 publications

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“…Many researchers have used the different types of ANNs in gamma densitometry [3][4][5][6][7][8][9][10][11][12][13] in order to classification, clustering and prediction. Cong et al [14] reviewed applications of ANNs in flow and heat transfer problems in nuclear engineering.…”

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confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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“…In the next application the authors used response surface methodology and the ANFIS model to quantify the effects of physical characteristics of magnetite on Fenton-like oxidation efficiency of methylene blue [18]. For petroleum products monitoring Roshani et al applied the ANFIS model to predict fluid density for a gamma ray densitometer [19]. the output values of the corresponding nodes.…”

Section: Wali Et Al Compared Intelligent Controllers Such Asmentioning

confidence: 99%

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Viharos

Kis

2015

Measurement

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Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use inputoutput patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a NeuroFuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement.

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“…ANNs have many applications in nuclear engineering (Cong et al, 2013;Roshani et al, 2013;Hayati et al, 2013;Khorsandi et al, 2013) and are known as a powerful prediction tool (Haykin, 1994).…”

Section: 3-neural Networkmentioning

confidence: 99%

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

Roshani

Feghhi

Setayeshi

2015

Radiation Measurements

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Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring

Cited by 35 publications

References 18 publications

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

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