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Dynamic Behavioral Modeling of RF Power Amplifier Based on Time-Delay Support Vector Regression
摘要： A new, dynamic behavioral modeling technique, based on a time-delay support vector regression (SVR) method, is presented in this paper. As an advanced machine learning algorithm, the SVR method provides an effective option for behavioral modeling of radio frequency (RF) power amplifiers (PAs), taking into account the effects of both device nonlinearity and memory. The basic theory of the proposed modeling technique is given, along with a detailed model extraction procedure. Unlike traditional artificial neural network (ANN) techniques, which take time to determine the best configuration of the model, the SVR method can obtain the optimal model in short time, using the grid-search technique. An example of an optimal SVR model selection applied to an RF PA is also given; the performance of the selected model presents a big improvement when compared with the default SVR model. Experimental validation is performed using an LDMOS PA, a single device gallium nitride (GaN) PA, and a Doherty GaN PA, revealing that the new modeling methodology provides very efficient and extremely accurate prediction. Compared with traditional Volterra models, canonical piecewise linear models, and ANN-based models, the proposed SVR model gives improved performance with reasonable complexity. In addition, it is shown that the model can predict accurately the behavior of the PA under input power levels that are different from those under which it is extracted.
关键词： time delay,radio frequency (RF) power amplifiers (PAs),machine learning,Dynamic behavioral model,support vector regression (SVR)
更新于2025-09-23 15:23:52
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A Short Term Day-Ahead Solar Radiation Prediction Using Machine Learning Techniques
摘要： The task of solar power forecasting becomes vital to ensure grid constancy and to enable an optimal unit commitment and cost-effective dispatch. Each year latest techniques and approaches appear to increase the exactitude of models with the important goal of reducing uncertainty in the predictions. The aim of the paper is to compile a big part of the knowledge about solar power forcing, to focus on the most recent advancements and future trends. Firstly, the inspiration to achieve an accurate forecast is presented with the analysis of the economic implications it may have. To address the problem superlative prediction models are rummaged by us using machine learning techniques. We make a comparison between multiple regression techniques for creating prediction models, along with linear least squares and support vector machines using multiple kernel functions. Predictions are analyzed by us in our experiments for the day ahead solar radiation data and it is shown that a machine learning approach yields feasible results for short-term solar prediction. The proposed model achieves a root mean square error improvement of around 29% compared to others proposed model except one.
关键词： Forecasting,SVR,Renewable energy,Short-term,Machine learning
更新于2025-09-23 15:22:29
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SSC prediction of cherry tomatoes based on IRIV-CS-SVR model and near infrared reflectance spectroscopy
摘要： As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand-held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO-SVR and CS-SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV-CS-SVR model for SSC prediction achieved the accuracy with R2 C of 0.9845. Thus, it is feasible to use NIRS with IRIV-CS-SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes.
关键词： IRIV-CS-SVR model,SSC prediction,cherry tomatoes,near infrared reflectance spectroscopy
更新于2025-09-23 15:21:01
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Improved measurement on quantitative analysis of coal properties using laser induced breakdown spectroscopy
摘要： It is of great significance to realize the rapid or online analysis of coal properties for combustion optimization of thermal power plants. In this work, a set of calibration schemes based on laser-induced breakdown spectroscopy (LIBS) was determined to improve the measurement on quantitative analysis of coal properties, including proximate analysis (calorific value, ash, volatile content) and ultimate analysis (carbon and hydrogen). Firstly, different normalization methods (channel normalization and normalization with the whole spectral area) combined with two regression algorithms (partial least-squares regression [PLSR] and support vector regression [SVR]) were compared to initially select the appropriate calibration method for each indicator. Then, the influence of de-noising by the wavelet threshold de-noising (WTD) on quantitative analysis was further studied, thereby the final analysis schemes for each indicator were determined. The results showed that WTD coupled SVR can be well estimated calorific value and ash, the root mean square error of prediction (RMSEP) were 0.80 MJ kg?1 and 0.60%. Coupling WTD and PLSR performed best for the measurement of volatile content, the RMSEP was 0.76%. For the quantitative analysis of carbon and hydrogen, normalization with the whole spectral area combined with SVR can get better measurement results, the RMSEP of the measurements were 1.08% and 0.21%, respectively. The corresponding average standard deviation (RSD) for calorific value, ash, volatile content, carbon and hydrogen of validation sets were 0.26 MJ kg?1, 0.57%, 0.79%, 0.47% and 0.08%, respectively. The results demonstrated that the selection of appropriate spectral pre-processing coupled with calibration strategies for each indicator can effectively improve the accuracy and precision of the measurement on coal properties.
关键词： partial least-squares regression (PLSR),quantitative analysis,normalization,Laser-induced breakdown spectroscopy (LIBS),coal properties,support vector regression (SVR),wavelet threshold de-noising (WTD)
更新于2025-09-19 17:13:59
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Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes
摘要： In order to ensure the edibility of potatoes, fluorescence hyperspectral images of potato samples were obtained to predict the solanine content in potatoes. For the best ROI (region of interest), the S‐component of saturation was extracted by the HSI colorimetric technology to characterize the bud eye of potatoes in three‐dimensional geometric space. The effective bud eye was located as the geometric center of ROI and the average spectral information was obtained. After pretreatment and selection of feature wavelengths, the predicting mode of SVR was established and was optimized by adjusting the penalty coefficient c and the core coefficient g of radial basis function (RBF). Finally, the determinant coefficient of the model was 0.9143 and the root mean square error was 0.0296, which could basically meet the application requirements. It was concluded that the method based on hyperspectral fluorescence image and HSI colorimetry could predict the solanine content in potatoes accurately through the optimized SVR model.
关键词： SVR model,fluorescence hyperspectral image,potatoes,HSI colorimetry,solanine content
更新于2025-09-11 14:15:04
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Quantitative detection of moisture content in rice seeds based on hyperspectral technique
摘要： To explore the best method for quantitative detection of moisture content in rice seeds, the total of 120 samples of rice seeds with different moisture content were studied by hyperspectral technique in the experiment. Sensitive wavelengths of moisture were firstly selected by calculating the migration rate, after that successive projections algorithm (SPA) was used to select characteristic wavelengths. The clustering method was proposed to increase the ability of prediction model by increasing the discrimination of hyperspectral eigenvalues of each sample group. Firstly, fuzzy C-mean clustering (FCM) algorithm was applied to cluster the characteristic wavelengths selected by SPA. Then the prediction model was established by support vector regression (SVR). Due to the unsatisfied clustering effect, simulated annealing genetic algorithm (SAGA) was introduced for clustering. By comparing the results based on original eigenvalues, FCM and SAGA clustering, respectively, it was found that the best method was SAGA. The SAGA-SVR mode achieved the value with R2 p of .8892 and RMSEP of 0.0296. The relaxation variable was introduced to reduce interval threshold because the R2 p was not ideal, and the final value achieved with R2 p of .9318 and RMSEP of 0.0264. It was proved that the SAGA-SVR model can be used for moisture detection of rice seeds.
关键词： moisture content,quantitative detection,rice seeds,SAGA-SVR model,hyperspectral technique
更新于2025-09-11 14:15:04
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Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine
摘要： A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.
关键词： pipeline leakage localization,method of characteristics (MOC),FBG hoop strain sensor,support vector regression (SVR),particle swarm optimization (PSO) algorithm,support vector machine (SVM)
更新于2025-09-04 15:30:14