- 标题
- 摘要
- 关键词
- 实验方案
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[Lecture Notes in Networks and Systems] Renewable Energy for Smart and Sustainable Cities Volume 62 (Artificial Intelligence in Renewable Energetic Systems) || Prediction PV Power Based on Artificial Neural Networks
摘要： The goal of this contribution is to estimate the power delivered by a multicrystals solar photovoltaic module based on artificial neural networks. Two structures of ANNs were tested: multiple-layer perceptron and radial basic function. The results obtained gave good coefficients of correlation, the statistical R2-value obtained is about 0.96 to predict this important parameter.
关键词： Artificial neural network (ANNs),Multiple-layer perceptron (MLP),Radial basic function (RBF),Photovoltaic (PV) power
更新于2025-09-23 15:23:52
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Dynamic spectrum nonlinear modeling of VIS & NIR band based on RBF neural network for noninvasive blood component analysis to consider the effects of scattering
摘要： Dynamic spectrum (DS) is expected to achieve non-invasive analysis of blood components by extracting the absorbance of arterial blood at multiple wavelengths. However, the nonlinearity caused by scattering of blood components is still a factor that limits the detection accuracy. According to the idea of “overlay modeling” in the “M + N” theory, theoretically, the consideration of nonlinear factor in modeling analysis can further improve the prediction accuracy of calibration model. But there is currently no recognized formula to describe this nonlinear relationship. In this paper, the ability of RBF neural network to approximate arbitrary nonlinear functions with arbitrary precision is used to approximate the nonlinear relationship between the spectrum and the component concentration from the perspective of ?tting. The calibration sets and prediction sets were randomly selected from the VIS & NIR band DS data of 231 volunteers, and 10 groups of modeling experiments were carried out. The results showed that compared with the conventional partial least squares (PLS) modeling method, the modeling indicators (correlation coe?cient (R) and root mean square error(RMSE)) of prediction set using radial basis function (RBF) neural network modeling have been signi?cantly improved. The modeling experiments suggest that the nonlinearity caused by scattering should not be ignored in DS non-invasive blood component analysis. By ?tting the nonlinear relationship, RBF neural network can better re?ect the actual mapping relationship between the spectrum and the component concentration, because it can not only consider the general part (Linear factor) in DS, but also the details (Nonlinear factor), which can e?ectively improve the accuracy of the non-invasive blood component analysis based on DS.
关键词： Scattering,Nonlinearity,RBF neural network,Dynamic spectrum (DS),Non-invasive detection
更新于2025-09-23 15:23:52
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Post Deposition Treatments of (Ag,Cu)(In,Ga)Se <sub/>2</sub> Thin Films for Solar Cells
摘要： Different alkali alternatives for post-deposition of ACIGS were tested, both conventional fluoride salts and in the form of metals. XPS analysis of surfaces treated with K or KF as well as Rb or RbF have been performed, before (only for K and Rb) and after an ammonia etch. In addition to a strong suppression of Cu and Ag near the surface, we observe a difference in the re-distribution of Ga in the surface region after the etch depending on pdt element. Our results are consistent with the formation of K-In-Se and Rb-In-Se compounds for both metal alkalis and alkali fluorides. We find a similar beneficial effect on cell performance for the best cells with the metals as with the fluoride salts.
关键词： KF,Rb,RbF,ACIGS,K,alkali post deposition treatment,(Ag,Cu)(In,Ga)Se2
更新于2025-09-23 15:21:01
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[IEEE 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Karachi, Pakistan (2020.3.26-2020.3.27)] 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Terminal Sliding Mode Nonlinear Control Strategy for MPPT Application of Photovoltaic System
摘要： The electricity generation from the photovoltaic (PV) system has been considered as an alternative energy resource to the fossil fuels since last decade. Solar energy is the most abundantly available renewable resource on earth. However, source to load conversion efficiency of PV system is low but installation cost is appreciable. In order to achieve maximum power, the system must be operated at maximum power point (MPP). Maximum power point tracking (MPPT) is very essential in the process of maximum power extraction of the PV system. This research article presents the terminal sliding mode control (TSMC) nonlinear MPPT control paradigm for stand-alone PV system using buck-boost converter. Radial basis function neural network (RBF NN) is generated the reference for the proposed TSMC in controller. The simulations are performed in MATLAB/Simulink. To evaluate the developed controller performance, TSMC is tested under varying conditions of environment and resistive load with fault and uncertainty. Moreover, proposed nonlinear TSMC MPPT control technique is compared with the conventional techniques such as proportional integral derivative (PID) and perturb and observe (P&O). The finite time stability analysis is explained via Lyapunov function.
关键词： TSMC,Finite time stability,Buck-Boost converter,MPPT,RBF NN
更新于2025-09-23 15:21:01
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[IEEE 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Winterton, South Africa (2019.8.5-2019.8.6)] 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Maximum Rooftop Photovoltaic Hosting Capacity with Harmonics as Limiting Factor a?? Case Study for Mauritius
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-23 15:19:57
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[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Graphene-based THz Antenna with A Graphene-metal CPW Feeding Structure
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-19 17:13:59
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A three dimensional meshfree-simulation of the selective laser sintering process with constant thermal coefficients applied to nylon 12 powders
摘要： 3D printing is an intersting process in the context of creating original objects.Selective laser sintering printers use a laser to fuse polyamide particles together with specific resin and heat. The difference in temperature between the different areas in the process causes the appearance of deformations, the objective of this work is the modeling of the thermal SLS phenomenona, by following the evolution of the temperature as a function of time.This model is based on the resolution of the heat conduction equation coupling with convection and radiation conditions with a distribution heat source and constant thermal coefficients by the meshless method based on radial basis function , the result of this study,will be presented and compared with other works.
关键词： heat transfer,Meshfree method,radical basis function (RBF),thermal modeling,selective laser sintering(SLS)
更新于2025-09-19 17:13:59
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[IEEE 2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) - Hangzhou (2018.8.16-2018.8.19)] 2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) - RBF Neural Network Adaptive PID Control for Energy Storage System in Grid-Connected Photovoltaic Microgrid
摘要： In photovoltaic microgrid, the output power of PV array will change with the change of external light and temperature. In order to maintain the stability of output power of PV system, energy storage system is usually added into the system. In a energy storage system, the most widely used control method is proportional-integral-derivative (PID) based control, however, the control effect of PID control is dependent on parameter tuning. In this paper, a PID controller based on adaptive radial basis function (RBF) neural network is proposed to adjust the PID parameters adaptively, and improve the response speed, anti-interference ability and robustness of the traditional PID controller. In the end, the energy storage system is built in the MATLAB/Simulink software, which is applied to the 100kW photovoltaic array to stabilize the output power when the light and temperature changed.
关键词： adaptive control,energy storage system,photovoltaic microgrid,RBF neural network,PID control
更新于2025-09-11 14:15:04
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Analysis on the Influence of the Distributed Photovoltaic on the Line Loss of Distribution System
摘要： In order to analyze synchronization control problems of two non-identical homodromy eccentric rotors (ERs) in a nonlinearly coupled system of vibrating machinery-part (NCS-VMP), a new electromechanical coupling nonlinear dynamic model considering nonlinear acting force of the part and nonlinear support is established, NCS- VMP’s complex control is converted into rotating speed and phase synchronous control of two homodromy non- identical exciters. By considering the dynamic interactions among the vibration body, the part and the ERs, the nonlinear dynamical equation of the NCS-VMP are established. An accurate synchronization control of speed and phase method are proposed for two homodromy ERs in NCS-VMP. The precise speed and phase synchronization control is mainly re?ected as: The cross-coupling control strategy is used which considering the coupling effect between two co-rotating exciters. The radial basis function network adaptive global sliding mode algorithm (RBFN- AGSMA) is used to adaptively approximate the total uncertainty of system including the nonlinear support and the nonlinear force of the parts, which can effectively reduce the estimation error. The radial basis function network method can suppress the jitter of the system and make the in?uence of the system more stable after replacing the sign function. The stability of RBFN-AGSMA controller is proved by Lyapunov theory. The controller’s property is veri?ed through numerical methods and taking the sliding mode control (SMC) algorithm into comparison. Results indicate that the designed control method can reduce chattering clearly compared with the SMC algorithm, and it is capable to improve the control accuracy of two non-identical homodromy exciters. By studying the effects of parameter change in the NCS-VMP on the system, the strong robustness of RBF network global sliding mode controller to parameter perturbations is proved. It is proposed that RBFN-AGSMA controller can control two nonidentical homodromy ERs in NCS-VMP to achieve accurate vibration trajectory in the working direction.
关键词： RBF network adaptive global sliding mode algorithm,stability,nonidentical homodromy coupling exciters,Nonlinearity support,nonlinear coupled vibration system
更新于2025-09-11 14:15:04
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Robust fusion algorithm based on RBF neural network with TS fuzzy model and its application to infrared flame detection problem
摘要： A robust fusion algorithm based on Radial Basis Function (RBF) neural network with Takagi-Sugeno (TS) fuzzy model is proposed in view of the data loss, data distortion or signal saturation which is usually occurred in the process of infrared flame detecting with multiple sensors. To initialize the model, the traditional K-means clustering algorithm is used to obtain the number of the fuzzy rules and the center of the membership function. Compared with the traditional RBF neural network with TS fuzzy model, the output of the node in the proposed model is constructed taking into account the membership degree of the feature components in each item of the output polynomial of the hidden layer nodes in consequent fuzzy network. A new weighted activation degree (WAD) is defined to calculate the firing strength (i.e., fuzzy rule applicability) of the fuzzy node instead of the commonly used Mahalanobis distance. The feature representation coefficients used in the above WAD fully consider the variant representation degree of different features in different fuzzy clusters, thus the developed method can deal with the abnormal outputs of the fuzzy rules caused by the variation of the feature components of the raw data obtained from the complex industrial environments. The robustness of the proposed approach is validated with experimental data obtained from a developed triple-channel infrared flame detector and the experiment results show that the convergence rate, accuracy and generalization ability of the proposed method are improved compared with the traditional RBF neural network with TS fuzzy model in [1] and the GA-BP (Genetic Algorithm-Back Propagation) model in [2]. In particular, the required number of the hidden layer nodes in the proposed approach is the least among the aforementioned methods.
关键词： robustness,feature representation coefficient,Infrared flame detector,TS fuzzy model,RBF neural network
更新于2025-09-04 15:30:14