Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system

Renewable Energy ◽

10.1016/j.renene.2015.08.028 ◽

2016 ◽

Vol 86 ◽

pp. 302-315 ◽

Author(s):

Wahiba Yaïci ◽

Evgueniy Entchev

Keyword(s):

Thermal Energy ◽

Performance Prediction ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Energy System ◽

Solar Thermal ◽

System Modelling ◽

Solar Thermal Energy ◽

Inference System ◽

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Prediction of the performance of a solar thermal energy system using adaptive neuro-fuzzy inference system

2014 International Conference on Renewable Energy Research and Application (ICRERA) ◽

10.1109/icrera.2014.7016455 ◽

2014 ◽

Author(s):

Wahiba Yaici ◽

Evgueniy Entchev

Keyword(s):

Thermal Energy ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Energy System ◽

Solar Thermal ◽

Solar Thermal Energy ◽

Inference System ◽

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Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources

Optimization Using Evolutionary Algorithms and Metaheuristics ◽

10.1201/9780429293030-7 ◽

2019 ◽

pp. 117-133 ◽

Author(s):

P. A. Adedeji ◽

S. O. Masebinu ◽

S. A. Akinlabi ◽

N. Madushele

Keyword(s):

Water Resources ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Energy System ◽

Inference System ◽

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Adaptive neuro fuzzy inference system modelling of multi-objective optimisation of electrical discharge machining process using single-wall carbon nanotubes

Australian Journal of Mechanical Engineering ◽

10.7158/m13-074.2015.13.2 ◽

2015 ◽

Vol 13 (2) ◽

pp. 97-117 ◽

Author(s):

S Prabhu ◽

B K Vinayagam

Keyword(s):

Carbon Nanotubes ◽

Fuzzy Inference System ◽

Electrical Discharge ◽

Fuzzy Inference ◽

Electrical Discharge Machining ◽

Machining Process ◽

Single Wall Carbon Nanotubes ◽

System Modelling ◽

Inference System ◽

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Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique

Applied Energy ◽

10.1016/j.apenergy.2014.08.022 ◽

2014 ◽

Vol 134 ◽

pp. 197-203 ◽

Author(s):

L. Yang ◽

E. Entchev

Keyword(s):

Performance Prediction ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

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Thermal Performance Prediction of Two-Phase Closed Thermosyphon Using Adaptive Neuro-Fuzzy Inference System

Heat Transfer Engineering ◽

10.1080/01457632.2014.916161 ◽

2014 ◽

Vol 36 (3) ◽

pp. 315-324 ◽

Author(s):

Mehdi Shanbedi ◽

Ahmad Amiri ◽

Sajjad Rashidi ◽

Saeed Zeinali Heris ◽

Majid Baniadam

Keyword(s):

Thermal Performance ◽

Performance Prediction ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Two Phase ◽

Inference System ◽

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Performance prediction of a solar thermal energy system using artificial neural networks

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2014.07.040 ◽

2014 ◽

Vol 73 (1) ◽

pp. 1348-1359 ◽

Author(s):

Wahiba Yaïci ◽

Evgueniy Entchev

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Thermal Energy ◽

Performance Prediction ◽

Energy System ◽

Solar Thermal ◽

Solar Thermal Energy ◽

Artificial Neural

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Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.314-316.341 ◽

2011 ◽

Vol 314-316 ◽

pp. 341-345

Author(s):

Bo Di Cui

Keyword(s):

Experimental Data ◽

Surface Roughness ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

System Modelling ◽

Inference System ◽

High Speed Turning ◽

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

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Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system

Numerical Heat Transfer Part A Applications ◽

10.1080/10407782.2016.1139974 ◽

2016 ◽

Vol 70 (1) ◽

pp. 103-116 ◽

Author(s):

Maryam Savari ◽

Sajjad Rashidi ◽

Ahmad Amiri ◽

Mehdi Shanbedi ◽

Saeed Zeinali Heris ◽

...

Keyword(s):

Laminar Flow ◽

Flow Regime ◽

Thermal Performance ◽

Performance Prediction ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Laminar Flow Regime ◽

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Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2010.06.011 ◽

2011 ◽

Vol 26 (1) ◽

pp. 38-45 ◽

Author(s):

K. Kucuk ◽

C.O. Aksoy ◽

H. Basarir ◽

T. Onargan ◽

M. Genis ◽

...

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

System Modelling ◽

Inference System ◽

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Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite, short carbon fibers (CF) and nano-TiO2 using adaptive neuro-fuzzy inference system (ANFIS)

Composites Part B Engineering ◽

10.1016/j.compositesb.2011.11.026 ◽

2012 ◽

Vol 43 (2) ◽

pp. 549-558 ◽

Author(s):

Ali Haghighat Mesbahi ◽

Dariush Semnani ◽

Saeid Nouri Khorasani

Keyword(s):

Wear Rate ◽

Carbon Fibers ◽

Performance Prediction ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Specific Wear Rate ◽

Nano Tio2 ◽

Inference System ◽

Neuro Fuzzy ◽

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