Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem

Kapetanakis, Theodoros N.; Vardiambasis, Ioannis O.; Lourakis, Emmanuel; Maras, A.M.

doi:10.1109/lawp.2018.2862939

Cited by 13 publications

(15 citation statements)

References 11 publications

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“…Using the artificial intelligent technique, the complexity of steam turbine can be studied and analysed which considers the high, medium and low-pressure stage of steam turbine. Using the model prepared by artificial neural network can be used for designing, synthesis, generating simulation and to monitor the power plant control system [12][13][14][15][16][17]. 160-Megawatt steam power plant has different motives like steam extraction, heater for feeding water and separator for moisture.…”

Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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Section: Data Collection and Proposed Methodsmentioning

confidence: 99%

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Abdullah¹,

Hamoodi²,

Mohammed³

2021

JESA

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“…The block diagrams of the ANN models applied in this paper are displayed in Figure 3. The multilayer perceptron (MLP) architecture [22,23,86,87], which is popular for similar applications according to the universal approximation theorem, was implemented for each ANN model of Figure 3.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

“…The validation dataset was used to validate the ANN model during the hyperparameters' adjustments by the learning algorithm and to early stop the training algorithm in order to avoid overfitting. Finally, the testing dataset, which is an independent set of data kept unseen from the ANN model during training, was used to evaluate the performance and to test the quality of the model [86,87].…”

Section: Data Preprocessingmentioning

confidence: 99%

Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

et al. 2020

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In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

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“…In addition, with a slight change even in a single geometrical parameter, the whole simulation process is to be repeated for getting a new solution. Thus, the obligation of having an instant solution for every minor change in the geometry is effectively resolved by using soft computing based models [12–32]. These models, once trained properly, give the instant answer for every minor change in the geometrical parameters.…”

Section: Introductionmentioning

confidence: 99%

“…It is a sound approach for building non‐linear and complex relationship between a set of input and output data patterns. Soft computing techniques are classified into fuzzy logic systems, artificial neural networks (ANN) and evolutionary algorithms [13, 14]. Fuzzy logic is a simple, flexible and multi‐valued logical system.…”

Section: Introductionmentioning

confidence: 99%

Multi‐adaptive neuro‐fuzzy inference system modelling for prediction of band‐notched behaviour of slotted‐UWB antennas optimised using evolutionary algorithms

Sarkar

Khan

Talukdar

2020

IET Microwaves, Antennas & Propagation

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Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem

Cited by 13 publications

References 11 publications

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Multi‐adaptive neuro‐fuzzy inference system modelling for prediction of band‐notched behaviour of slotted‐UWB antennas optimised using evolutionary algorithms

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