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- 摘要
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- 实验方案
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Evaluation of electrical efficiency of photovoltaic thermal solar collector
摘要： In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
关键词： hybrid machine learning model,Renewable energy,photovoltaic-thermal (PV/T),least square support vector machine (LSSVM),adaptive neuro-fuzzy inference system (ANFIS),neural networks (NNs)
更新于2025-09-23 15:21:01
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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems
摘要： The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
关键词： water pumping system management,photovoltaic power,empirical models,forecast,artificial neural network,neuro fuzzy inference system
更新于2025-09-23 15:19:57
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Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models
摘要： Laser-based surface texturing provides highly controlled interference fit between two parts. In this work, artificial intelligence-based models were used to predict the surface properties of laser processed stainless steel 316 samples. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were developed to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. The accuracy of the models was measured by calculating the root mean square error and mean absolute error. The reliability of the ANFIS and FFNN models for the output prediction of the laser surface texturing (LST) system were investigated by using the data measured from experiments based on a 3^3 factorial design, with main processing parameters set as laser power, pulse repetition frequency, and percentage of laser spot overlap. The relative assessment of the models was performed by comparing percentage error prediction. Finally, the impact of input data was examined using predicted response surface plots. Results showed that ANFIS prediction was 48% more accurate compared with that provided by the FFNN model.
关键词： Artificial neural network,Adaptive neuro-fuzzy inference system,Laser texturing
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - A Wideband Fabry-Perot Antenna with Low-RCS High-GBP Using Embed-chessboard Polarization Conversion Metasurface
摘要： An improved adaptive neuro-fuzzy inference system (IANFIS) is proposed to build a model to predict the resonant frequency shift performance of surface acoustic wave (SAW) gas sensors. In the proposed IANFIS, by directly minimizing the root-mean-squared-error performance criterion, Taguchi-genetic learning algorithm is used in the ANFIS to find both the optimal premise and consequent parameters and to simultaneously determine the most suitable membership functions. The five design parameters of SAW gas sensors are considered to be the input variables of the IANFIS model. The input variables include the number of electrode finger pairs, the electrode overlap, the separation distance of two interdigital transducers on the substrate, the dimensions of the stable temperature-cut (ST-cut) quartz substrate, and the electrode thickness. The output variable of the IANFIS model is composed of the resonant frequency shift performance. The results predicted by the proposed IANFIS are compared with those obtained by the back-propagation neural network. The comparison has shown that the performance prediction of resonant frequency shift using the proposed IANFIS is effective. In addition, the sensitivity analyses of the five design parameters have also shown that both the electrode overlap and the dimensions of the ST-cut quartz substrate have the most influence on the resonant frequency shift performance.
关键词： Adaptive neuro-fuzzy inference system,surface acoustic wave (SAW) gas sensors,Taguchi-genetic learning algorithm
更新于2025-09-19 17:13:59
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A Hybrid Modified FA-ANFIS-P&O Approach for MPPT in Photovoltaic Systems under PSCs
摘要： A modified firefly algorithm (MFO)-based adaptive neuro-fuzzy inference system (ANFIS) combined with the perturbation and observation (P&O) is used in this paper to track the maximum power point (MPP) in photovoltaic systems (PVs). The proposed method identifies and tracks the MPP in two stages. First, according to the irradiance on the solar panels, the ANFIS approximately identifies the MPP. In the second stage, the P&O method starts to act in the tracking cycle and initiates an accurate searching process from that point. The suggested hybrid method covers the problems of commonly-used methods, such as inability in detecting the global MPP under partial shading conditions (PSCs) and trapping in the local optima. Furthermore, the method provides significantly higher speed for the MPP tracking under various irradiance patterns. To prove the above-mentioned claims, the given approach is compared with the P&O method as a common method in the MPPT and particle swarm optimization (PSO) which operates based on swarm intelligence. Simulation results obtained from MATLAB/Simulink environment show that the proposed method identifies and tracks the MPP under uniform irradiance and PSCs in a very short time of roughly 0.2 s.
关键词： PV system,Solar energy,Modified firefly algorithm,Perturbation and observation,Adaptive neuro fuzzy inference system
更新于2025-09-12 10:27:22
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Adaptive neuro-fuzzy inference system based modeling of recast layer thickness during laser trepanning of Inconel-718 sheet
摘要： Re-solidification of molten material during the laser trepanning of Inconel-718 is a major hindrance in achieving good quality drill with high precision and accuracy. Re-solidification affects the performance of the drilled hole. Many researchers have tried for the optimization of laser trepan drilling in order to improve the drilled hole quality characteristics. But till now, limited work has been reported in concern with recast layer formation in laser trepan drilling of Inconel-718. This paper experimentally investigated the recast layer formation during laser trepan drilling followed by the prediction of the recast layer formation using the adaptive neuro-fuzzy inference system (ANFIS). Experiments are performed on 1.4-mm-thick Inconel-718 sheet using pulsed Nd: YAG laser. Recast layer thickness has been measured for each experiment followed by the ANFIS-based prediction of recast layer. Moreover, the effect of different input parameters on the recast layer has also been discussed.
关键词： Recast layer,Laser trepan drilling,Adaptive neuro-fuzzy inference system,Inconel-718
更新于2025-09-11 14:15:04
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Forecasting Solar Activity with Computational Intelligence Models
摘要： It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).
关键词： Adaptive Neuro-Fuzzy Inference System,Solar Activity Forecasting,Computational Intelligence Models,Brain Emotional Learning-based Fuzzy Inference System,Solar cycles
更新于2025-09-04 15:30:14