Search Results (188)

Accurate prediction of transportation carbon emissions is essential for identifying emerging emission pressures and supporting the transition toward sustainable transportation systems. This study develops a hybrid prediction framework that integrates Elastic Net Regression (ENR), Beetle Antennae Search (BAS), and Generalized Regression Neural Network (GRNN) to estimate transportation-sector monthly carbon emissions from 2008 to 2023 in Anhui Province, China. ENR is employed to identify key influencing factors, while BAS optimizes GRNN parameters to enhance model convergence and generalization. In an ex-post out-of-sample evaluation using observed explanatory variables from 2023, comparative experiments show that the ENR–BAS–GRNN model achieves the highest accuracy (R² = 0.99, ARE < 1%), outperforming all benchmark models. The results indicate that integrating feature selection with intelligent optimization can effectively capture nonlinear emission dynamics. Beyond improving estimation accuracy, the proposed framework provides a quantitative tool for monitoring transportation-related carbon emissions and diagnosing the factors associated with emission changes. When coupled with scenario-based projections of explanatory variables, the framework can support ex-ante forecasting. The study therefore contributes a computationally lightweight and data-driven approach for integrating carbon emission measurement, monitoring, and policy support into regional sustainable transportation governance. Full article

In order to improve the performance of three-phase NPC inverters under unbalanced working conditions, a quasi-proportional complex integral controller (QPCI) three-phase inverter control method based on an adaptive fractional-order algorithm is proposed. Firstly, the reasons for the poor performance of conventional control methods under unbalanced load conditions are analyzed. Subsequently, a fractional-order quasi-proportional complex integral (FO-QPCI) controller is proposed, and the effects of its control parameters, including proportional gain (KP), integral gain (KI), resonant frequency (ω_c), and fractional order (μ), are systematically investigated. Furthermore, the optimal control parameter dataset is utilized to train a Generalized Regression Neural Network (GRNN), through which an adaptive parameter-tuning model is established. As a result, the proposed FO-QPCI controller can dynamically adjust its control parameters according to different voltage references and load unbalanced levels. Finally, to verify the effectiveness of the proposed control strategy, both simulation models and an experimental platform based on a three-level inverter are developed. The results show that the proposed control method has good control ability for the output voltage and harmonics under unbalanced working conditions. Full article

Moisture content (MC) is an important indicator affecting the quality of dried lemon slices. To achieve rapid and non-destructive MC detection, this study developed a lemon slice MC detection model based on the fusion of image texture and spectral features. A total of 240 lemon slices were dried at 80 °C, and hyperspectral imaging (HSI) data and reference MC values were collected at different drying times. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) were used to select characteristic wavelengths. Image texture features were extracted using the gray-level co-occurrence matrix (GLCM), and the spectral features and image texture features were concatenated and fused. Kernel principal component analysis (KPCA) was then applied to reduce the dimensionality of the fused feature set. Finally, support vector machine (SVM), general regression neural network (GRNN), and partial least squares (PLS) models were established for MC detection. The results showed that the spectral-feature-based models achieved good predictive performance. The image texture-feature-based models also demonstrated predictive capability, whereas spectral–texture feature fusion further improved prediction accuracy. Among all models, the PLS model based on the spectral–texture fused features achieved the best performance, with a coefficient of determination of prediction ($R_p^2$) of 0.9890 and a root mean square error of prediction (RMSEP) of 0.1916 g/g in the prediction set. These results indicate that HSI combined with spectral–texture feature fusion provides a promising approach for rapid MC detection in lemon slices. Full article

This study investigated the forecasting performance of the South African gold sales series using the seasonal autoregressive integrated moving average (SARIMA), generalised autoregressive conditionally heteroscedastic (GARCH), general regression neural network (GRNN) and artificial neural network (ANN)-based extreme learning machine (ELM). This study employed traditional methods and a recently developed ML method for single hidden-layer feed-forward neural networks (SLFNs). The findings revealed that $\mathrm{SARIMA} \left(0,1,1\right){\left(2,1,2\right)}_{12}$ was considered the best model for the gold sales series. The empirical findings demonstrated that the SARIMA model outperforms neural network-based models, providing the South African government and its lenders with a more reliable and cost-effective tool for predicting foreign exchange earnings from gold. This study contributes to the literature by providing one of the first comparative evaluations of traditional time-series models and advanced neural network methods for forecasting South African gold sales. This study is novel as it is a first-of-its-kind comparative application of traditional SARIMA and GARCH models alongside GRNN and ANN-based ELM methods to South African gold sales, revealing the superior forecasting performance of a traditional SARIMA model over advanced ML approaches. Future research should explore the development and application of hybrid models that integrate the strengths of linear SARIMA frameworks with the pattern-recognition capabilities of nonlinear ANN-based ELM models. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Controlled-release systems must translate material design choices into predictable pharmacokinetic (PK) profiles, yet purely mechanistic or purely data-driven models often underperform when tuning complex polymer networks. Here, we develop tunable poly(vinyl formal) membranes (PVFMs) for diclofenac delivery and integrate classical kinetic analysis with a Generalized Regression Neural Network (GRNN) to connect formulation variables to release behavior and PK-relevant targets. PVFMs were synthesized across a gradient of crosslink densities by varying HCl content; diclofenac release was quantified under standardized conditions with geometry and dosing rigorously controlled (thickness, effective area, surface-area-to-volume ratio, and areal drug loading are reported to ensure reproducibility). Release profiles were fitted to Korsmeyer–Peppas, zero-order, first-order, Higuchi, and hyperbolic tangent models, while a GRNN was trained on material descriptors and time to predict cumulative release and flux, including out-of-sample conditions. Increasing crosslink density monotonically reduced swelling, areal release rate, and overall release efficiency (strong linear trends; r ≈ 0.99) and shifted transport from anomalous to Super Case II at the highest crosslinking. Classical models captured regime transitions but did not sustain high accuracy across the full design space; in contrast, the GRNN delivered superior predictive performance and generalized to conditions absent from training, enabling accurate interpolation/extrapolation of release trajectories. Beyond prior work, we provide a material-to-PK design map in which crosslinking, porosity/tortuosity, and hydrophobicity act as explicit “knobs” to shape burst, flux, and near-zero-order behavior, and we introduce a hybrid framework where mechanistic models guide interpretation while GRNN supplies robust, data-driven prediction for formulation selection. This integrated PVFM–GRNN approach supports rational design and quality control of controlled-release devices for diclofenac and is extendable to other therapeutics given appropriate descriptors and training data. Full article

This paper presents an Artificial Neural Network (ANN) approach for estimating total real and reactive power losses in electrical power systems. Three network architectures were explored: the Multilayer Perceptron (MLP), the Radial Basis Function (RBF) network, and the Generalized Regression Neural Network (GRNN). The main advantage of the proposed methodology lies in its ability to rapidly compute power loss values throughout the system. ANN models are especially effective due to their capacity to capture the nonlinear characteristics of power systems, thus eliminating the need for iterative procedures. The applicability and effectiveness of the approach were evaluated using the IEEE 14-bus test system and compared with the continuation power flow method, which estimates losses using conventional numerical techniques. The results indicate that the ANN-based models performed well, achieving mean squared error (MSE) values below the predefined threshold during both training and validation (0.001). Notably, the networks accurately estimated the total power losses within the expected range, with residuals on the order of 10⁻⁴. Among the models tested, the RBF network showed slightly superior performance in terms of error metrics, requiring fewer centers to meet the established criteria compared to the MLP and GRNN models (11 centers). However, the GRNN achieved the shortest processing time; even so, all three networks produced satisfactory and consistent results, particularly in identifying the critical points of electrical power systems, which is of fundamental importance for ensuring system stability and operational reliability. Full article

The online diagnosis technology used to determine the internal thermal status and defects of GIS equipment is important. In the existing GIS bus thermal defect fault diagnosis methods, sensors are usually installed on the highest and lowest temperature areas of the enclosure surface, and then an artificial neural network is established to obtain the highest temperature inside the GIS. These methods only consider the temperature under steady-state conditions, and the temperature signals collected by sensors are different, which leads to low accuracy and weak generality. This paper investigated the transient temperature evolution characteristics defined as a sequence of temperature values over time, and adopted them as new features. The steady and transient enclosure and environment temperature data were used to train the Generalized Regression Neural Network (GRNN) for the inside busbar temperature inversion. Experimental tests proved that the proposed method has a higher accuracy compared to traditional characteristic parameters, especially for the less significant temperature rise. This article provides a technical means for determining the internal temperature rise status of GIS equipment through external temperature monitoring in practical applications. Full article

Load forecasting is a key component of power network planning and an essential approach to achieving the efficient cooperative optimization of integrated economic energy services. To improve the accuracy of the power load prediction and ensure the stable dispatch of power grid, this paper takes County A as a case study. The fish bone diagram method is applied to analyze the influence of four categories of factors on the county’s power load, and stepwise regression, the unit energy consumption method, and an optimized grey model are adopted to forecast and analyze the planned load of the county over the past 5 years. In addition, the spatial load density method, the optimized grey prediction model, and the General Regression Neural Network (GRNN) are used to predict and analyze the county’s planned power grid load based on data from the past ten years. The Ordered Weighted Averaging (OWA) operator is then applied to integrate the results, and the predictive performance of different methods is assessed with an evaluation function. The results show that this combined multi-method approach achieves a higher accuracy. It also accounts for the evolving political, economic, and social conditions of the country, making the predictions more useful for power grid planning. Based on these findings, corresponding countermeasures and suggestions are proposed to support the improvement of spatial planning for electric power facilities in County A. Full article

With the integration of numerous distributed energy resources (DERs) and buildings with diverse energy demands, the inherent vulnerability of agricultural microgrids poses escalating security threats. Harnessing the regulatory capabilities of diverse building loads and energy storage systems to mitigate voltage excursions caused by DER generation in microgrids is of significant importance. Therefore, a data mechanism fusion-driven microgrid planning method is proposed in this paper, aiming to enhance the security of microgrids and optimize the utilization of DERs. A comprehensive agricultural microgrid model that incorporates intricate constraints of various types of buildings is established, including greenhouses, refrigeration houses and residences. Based on this model, a site selection and capacity determination planning methodology is proposed, taking into account wind turbines (WTs), photovoltaics (PVs), electric boilers (EBs), battery energy storage systems (BESSs), and heat storage devices. To address the limitations of traditional greenhouse models in accurately predicting indoor temperatures, a temperature field prediction method for greenhouses is proposed by leveraging a generalized regression neural network (GRNN) to train and modify the model indicators. Case studies based on a modified IEEE 33-bus system verified the effectiveness and rationality of the proposed method. Full article

The assessment of ship dynamic stability in waves is crucial for navigation safety. To mitigate accidents, the International Maritime Organization (IMO) has formulated corresponding technical standards. However, evaluating the dynamic stability performance of ships involves complex numerical simulation or model experiments based on hydrodynamic methods, which demands professionalism, substantial time, and significant financial cost. This paper analyzes the feasibility of using the Generalized Regression Neural Network (GRNN) method to build a surrogate model for ship dynamic stability performance calculation. Comparisons with hydrodynamics-based simulations reveal that the surrogate model matches the trends well, yet the root-mean-square error (RMSE) remains non-negligible. Therefore, an improved GRNN surrogate model is proposed to solve this problem. By incorporating enhanced feature preprocessing and clustering techniques, the improved model not only increases predictive accuracy but also achieves significant efficiency gains, reducing the computational time from days or weeks for numerical simulations to seconds or minutes. Experimental results show that the improved surrogate model outperforms the baseline GRNN model, and this framework can serve as a practical surrogate for hydrodynamics-based numerical models to rapidly assess pre-voyage dynamic stability. Full article

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending. Full article

Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN). An improved population-based metaheuristic optimization algorithm, the artificial lemming algorithm (ALA), is employed to select the hyperparameters of these machine learning models, enhancing their performance. All developed models are trained and tested using experimental data from multiple studies. The performance of the models is evaluated using various statistical metrics, complemented by regression plots, error analysis, and Taylor graphs to further identify the most effective predictive model. The results show that the ALA-RF model obtains the best performance in predicting hydrogen storage, with optimal values of coefficient of determination (R²), root mean square error (RMSE), Willmott’s index (WI), and weighted average percentage error (WAPE) in both training and testing phases (0.9845 and 0.9840, 0.2719 and 0.2828, 0.9961 and 0.9959, and 0.0667 and 0.0714, respectively). Additionally, pressure is identified as the most significant feature for predicting hydrogen storage in MOFs. These findings provide an intelligent solution for the selection of MOFs and optimization of operational conditions in hydrogen storage processes. Full article

The internal response mechanism of vegetation change in fragile high-altitude ecosystems is pivotal for ecological stability. This study focuses on the Lhasa River Basin (LRB) on the Tibetan Plateau (TP), a typical high-altitude fragile ecosystem where vegetation dynamics are highly sensitive to climate change and human activities. Utilizing MODIS surface reflectance data (MOD09Q1), a general regression neural network (GRNN) was applied to create a 250 m resolution fractional vegetation cover (FVC) dataset from 2001 to 2022, whose accuracy was verified with field survey data. Through methods like the Theil–Sen Median trend analysis, Mann–Kendall significance test, Hurst exponent, and geographical detector, the collaborative mechanism of 14 driving factors was systematically explored. Key conclusions are as follows: (1) The FVC in the LRB evolved in stages, first decreasing and then increasing, with 46.71% of the basin area expected to show an improvement trend in the future. (2) Among natural factors, elevation (q = 0.480), annual mean potential evapotranspiration (q = 0.362), and annual mean temperature (q = 0.361) are the main determinants of FVC spatiotemporal variation. (3) In terms of human activities, land use type has the highest explanatory power (q = 0.365) for FVC. (4) The interaction of two factors on FVC is stronger than that of a single factor, with the elevation–land use interaction being the most significant (q = 0.558). These results deepen our understanding of the interactions among vegetation, climate, and humans in fragile high-altitude ecosystems and provide a scientific basis for formulating zoned restoration strategies on the TP. Full article

Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics. Full article