RBF Neural Network Model Research Articles

Rapid discrimination of xiaokeng green tea geographical origin using surface-enhanced Raman spectroscopy coupled with chemometrics

Xiaokeng green tea (XKGT), named for its origin, brings high economic benefits to the region due to its superior quality, but it is susceptible to fraud. In this study, 131 roasted green teas from different origins in Anhui Province were used as samples to investigate the ability of surface-enhanced Raman spectroscopy (SERS) combined with chemometrics to accurately discriminate green tea origins in narrow regions. First, spherical Ag nanoparticles (AgNPs) were prepared as SERS substrates. A stratified 5-fold cross-validation method was used to divide the modelling samples. The radial basis function neural network (RBFNN), convolutional neural network (CNN), and random forest (RF) models were built, and four different preprocessing methods were compared. The results showed that the optimised RBFNN model with normalisation, as the preprocessing method, had an average prediction set accuracy of 97.69% in distinguishing samples from the Xiaokeng tea area from other tea areas. The RBFNN model was further used to differentiate tea samples from four different origins within Anhui Province, namely Xiaokeng Village (XK), Chizhou City except Xiaokeng Village (CZ), Lu'an City (L'A) and Huangshan City (HS), with an average accuracy of 91.85% for the prediction set. These findings point to the potential of combining SERS with chemometrics as an effective method for discriminating the geographic origins of XKGT in narrow regions.

Study of BP and RBF Neural Networks Applied to the Prediction of Vibration Characteristics in Static Blasting of Dry Ice Powder

The novel dry ice powder static blasting rock breaking lacked working standards, especially for the vibration safety assessment of the construction site for the protection of the building structure, in comparison with the traditional drill and blast method which had a proven operational process and safety specifications. The Sadovsky vibration velocity prediction formula could only predict the vibration velocity and was project specific. Oscillation parameters that needed to be considered in the vibration safety assessment, such as the dominant frequency of vibration, could not be obtained through empirical formulas. Using the five parameters of hole depth, blast center distance, dry ice powder mass and rock classification as the main influencing factors, BP and RBF neural network models were constructed by Matlab software to predict the peak vibration velocity, main frequency and maximum displacement of dry ice powder blasting. Projection results revealed that it is structurally simpler than the BP neural network and that the RBF was more accurate in predicting the target than the BP network. The results of the study had significant implications for the safe application of the new technology, and more samples of field data need to be obtained in the future, along with the use of more advanced predictive modelling.

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.

Design and performance analysis of motor phase change heat storage heat pump system for new energy vehicles

Based on the work of air conditioners for new powerful cars, the author created a heat pump air conditioner with various functions, such as cooling and heating passengers, cooling and preheating the battery, defogging, defrosting, and PTC heating. Developed basic components of AC heat pumps such as scroll compressor model, running condenser model, combined evaporator model, plate evaporator extended valve model, and radial basis function neural network model for predicting compressor volumetric efficiency and isentropic efficiency. The accuracy of the gas-flow, gas-flow, and plate evaporator simulation model was verified. The maximum error between the experimental and simulated values of compressor power, heat exchanger, and system COP in various operating models is 0.34~10.09, which is consistent with the experimental results. correct and can be used for ventilation pump research. Also, an operational simulation analysis platform was used to analyze the performance of the heat pump and study the system characteristics. It is clear that this system provides more warmth to the occupants in the defrost mode, and current research on the operation of the air conditioning pump is based on the first law of thermodynamics.

Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation

Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation

Abnormal sensing feature detection of DC high voltage power battery for new energy vehicles

Abstract As a kind of clean energy transportation, new energy vehicles are widely respected. This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using the basic structure of RBF neural network and the advantages of the reduced clustering algorithm, for a single power battery, the power battery power abnormality detection scheme based on the improvement of reduced clustering algorithm is proposed, and the power battery abnormality detection process is designed. Taking the sensing feature data of the battery management system of a new energy vehicle as an experimental sample, through the battery state estimation experiment and the example application of the model, it is found that the RMSE (0.0018) and MAPE (0.0206) of the model training are lower than that of the comparison model, and the average error rate of the abnormal battery identification is 0.833%. The model’s abnormality detection results in both instances are consistent with the actual maintenance results. The analysis indicates that the RBF neural network model with reduced clustering algorithm has superior accuracy and feasibility for detecting abnormal battery power.

Research on the Management of Ecological Agricultural Characteristic Tourism Development Based on Artificial Intelligence Technology

Abstract Eco-agricultural characteristic tourism is one of the important contents of China’s tourism research, and is also one of the important initiatives to solve the “three rural problems” and realize comprehensive well-off. This paper proposes a particle algorithm-optimized RBF neural network model based on a neural network, which greatly overcomes the defects of its busy convergence speed and complex learning and calculation process. This paper uses this neural network to analyze and summarize the existing historical data on ecological and agricultural characteristics of tourism management, and presents the current problems of agricultural characteristics of tourism management to the tourist site managers in the form of data, to develop the ecological and agricultural characteristics of tourism management mechanism. The analysis of data focuses on passenger flow management, human resource structure management, and tourist satisfaction management in tourism management. The data calculation indicates that the staff at the scenic spot has an inadequate educational degree and lacks strong professionalism. In this regard, the eco-agricultural tourism attractions make corrective measures, the proportion of employees with a bachelor’s degree and above is pulled to 39%, and the average value of tourists’ experience of the attraction reaches 0.737. The neural network model proposed in this paper can effectively analyze and summarize the data of the characteristic tourism management, and provide data support for the improvement and development of the eco-agricultural characteristics of the management of tourism.

Evaluating the efficacy of recyclable nanostructured adsorbents for rapid removal of methylparaben from aqueous solutions

In this study, we evaluate the efficiency of two novel nanostructured adsorbents – chitosan-graphitic carbon nitride@magnetite (CS-g-CN@Fe3O4) and graphitic carbon nitride@copper/zinc nanocomposite (g-CN@Cu/Zn NC) – for the rapid removal of methylparaben (MPB) from water. Our characterization methods, aimed at understanding the adsorbents’ structures and surface areas, informed our systematic examination of influential parameters including sonication time, adsorbent dosage, initial MPB concentration, and temperature.We applied advanced modeling techniques, such as response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN), to evaluate the adsorption process. The adsorbents proved highly effective, achieving maximum adsorption capacities of 255 mg g−1 for CS-g-CN@Fe3O4 and 218 mg g−1 for g-CN@Cu/Zn NC. Through genetic algorithm (GA) optimization, we identified the optimal conditions for the highest MPB removal efficiency: a sonication period of 12.00 min and an adsorbent dose of 0.010 g for CS-g-CN@Fe3O4 NC, with an MPB concentration of 17.20 mg L−1 at 42.85 °C; and a sonication time of 10.25 min and a 0.011 g dose for g-CN@Cu/Zn NC, with an MPB concentration of 13.45 mg L−1 at 36.50 °C.The predictive accuracy of the RBFNN and GRNN models was confirmed to be satisfactory. Our findings demonstrate the significant capabilities of these synthesized adsorbents in effectively removing MPB from water, paving the way for optimized applications in water purification.

Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

This study presents a model predictive control of a 4 × 3 Multiple-Input Multiple-Output (MIMO) biomass control system that uses radial basis function (RBF) as an activation function to enhance the control performance. The biomass boiler model is developed from the system identification box in MATLAB by collecting measured input and output data from the 31.5 MW Wonji sugar mill and analyzing the data using black box modeling approach. The major goal of this study is to improve the MIMO control performance of a biomass boiler by utilizing Radial Basis Function Neural Network (RBFNN) to increase model accuracy which ultimately enhances the overall control system. Biomass boilers have four inputs and three outputs. The inputs are airflow 1, airflow 2, water flow and stocker speed (the speed of the motor which feeds bagasse or sugarcane pulp) and outputs are temperature, pressure and drum level. The RBFNN model was programmed using MATLAB R2021a software with system identification toolbox. Shorter settling times of 0.286 s, 1.873 s, and 0.637 s, as well as tolerable overshoots of 0.505%, 1.5%, and 15.698% for temperature, pressure, and level respectively, were achieved with the suggested model predictive controller using RBFNN model. However, when utilizing the identical controller and state space model, the settling times are 2.318 s, 5.461 s, and 6.147 s, with overshoots of 4.737%, 8.152%, and 38.194% for the three variables respectively. The suggested method was compared to the model predictive control (MPC) biomass boiler state space model, which can be considered as a model without RBFNN. The simulation results indicate that the RBFNN-based MPC performed better than the state space model-based MPC. This is because, the state space model is always constant, in contrast to the neural network's active monitoring of boiler dynamics.

Using artificial intelligence methods to predict the compressive strength of concrete containing sugarcane bagasse ash

Sugarcane bagasse ash is an agricultural and industrial waste material produced in millions of tonnes annually. While traditionally used as a fertilizer or buried underground, incorporating this material into concrete production not only reduces cement usage and addresses environmental concerns but also enhances the mechanical properties of concrete. This research aims to investigate the predictive capabilities of various Artificial Intelligence (AI) models, including radial basis function neural network (RBFNN), artificial neural network (ANN), and support vector regression (SVR), for estimating the compressive strength of concrete containing sugarcane bagasse ash (SCBA). A dataset comprising 1819 data points from previous studies was collected, consisting of three main groups: 340 data of concrete with SCBA, 459 data of concrete without SCMs, and 1020 concrete data incorporating other supplementary cementitious materials (SCMs). The dataset was utilised in three different ways to evaluate the influence of data on model performance. Firstly, models were trained solely on SCBA data (Group A); subsequently, SCBA data was combined with concrete data without any SCMs (Group B); finally, the entire dataset was utilised (Group C). Statistical analysis revealed that the SVR model trained on SCBA data and the RBFNN model trained on group B data demonstrated the best results and accuracy. However, further refinement of the concrete without SCMs data in group B led to improved model performance, surpassing even that of the SVR model trained on group A data.

Multi-step ahead groundwater level forecasting in Grand Est, France: Comparison between stacked machine learning model and radial basis function neural network

In recent years, the increasing influence of climate change on the water cycle has emphasized the significance of analyzing and forecasting groundwater level (GWL) for effective water resource planning and management. This study proposes a comparative analysis of multi-step ahead daily GWL prediction by means of two different models. The former is an ensemble model, based on the stacking of two Machine Learning algorithms, Multilayer Perceptron (MLP) and Random Forest (RF). The second model is represented by a Radial Basis Function Neural Network (RBF-NN). For the modelling, a mid-term forecast horizon of up to 30 days was considered, while the precipitation and evapotranspiration were included as exogenous inputs. Three different wells located on the chalk aquifers of the northeastern region of France referred to as PZ, S1, and LS4, were selected for this study. The RBF-NN model demonstrated superior performance compared to the stacked MLP-RF model for wells PZ and S1. Conversely, for well LS4, both models displayed similar performance, albeit with a marginally higher accuracy observed in the stacked MLP-RF model. However, both models yielded accurate predictions of the GWL across all three wells, with R2 values exceeding 0.87 for all the wells and forecasting horizons. Furthermore, the RBF-NN model showed fewer reductions in performance as the forecasting horizon increased compared to the stacked MLP-RF model, leading to more reliable predictions even for a 30-day forecast horizon. An evident trend of decreasing Mean Absolute Percentage Error (MAPE) was observed from the 1st quartile to the 4th quartile of forecasted values. This highlights the models’ improved ability to provide accurate forecasts for deeper GWL values. The future developments of this research will be aimed at overcoming some limitations of the study, including lagged values of the exogenous variables precipitation and evaporation among the predictors, and considering aquifers with different hydrogeological characteristics.

Research on the Prediction Model of Loess Collapsibility in Xinyuan County, Ili River Valley Area

Collapsibility is a unique engineering geological property of loess. Choosing appropriate parameters to build the prediction model of loess collapsibility is an essential step toward solving the loess collapsibility problem. A case study was performed for the loess in Xinyuan County of the Yili River Basin, China. A large amount of data was collected from preliminary geotechnical tests in this region. Mathematical statistics were applied to analyse the correlations between the loess collapsibility and soil parameters. Multiple linear regression and neural network theories were adopted to build this region’s prediction model of loess collapsibility. The results showed that microscopically, the soils in this region were predominantly flocculated structures. The soil particles were flaky and in bracket contact, and the pores were round or irregularly shaped. Regarding the material composition, the soils were primarily composed of quartz and albite, with a low hematite content. In the study area, the correlation coefficients between the collapsibility coefficient of the loess vs. the density, dry density, saturation, porosity ratio, and porosity varied between 0.628 and 0.857, indicating a strong or very strong correlation. In terms of predicting loess collapsibility, the effectiveness of neural networks based on RBF (radial basis function) and multiple linear regression models was contrasted. The latter was discovered to be more appropriate, dependable, and accurate, with an accuracy percentage of 94.42%. Simultaneously, the model’s assessment index is 0.014 for the root mean squared error (RMSE), 0.962 for the correlation coefficient (CC), 0.919 for the Nash–Sutcliffe efficiency coefficient (NSE), and −1.494 percent for the percent bias (PBIAS). It works well for estimating whether local loess may collapse. Therefore, the RBF neural network model built in the present study has adequate precision and meets the engineering requirements. Our research sheds new light on loess collapsibility assessment in this region.

Structural properties and diameter prediction of fine denier polyester fiber based on machine learning algorithms

Fine denier polyester fibers have attracted attention in electronic information industry owing to their unique structural properties and ultra fine diameters. Since there is a close connection between melt-spinning process parameters and their structural properties and diameters, the influence of process parameters on fiber structural properties and diameters is an issue worth further exploration. This study aims to construct prediction models based on the Gaussian process regression (GPR), radial basis function neural network (RBFNN) and extreme learning machine (ELM) algorithms to predict the structural properties and diameter of fine denier polyester fibers. For this purpose, firstly, fine denier polyester fibers with different structural properties (i.e., orientation degree and crystallinity) and diameters are produced by changing the key melt-spinning parameters, namely spinning temperature, take-up speed and metering pump speed. Then GPR, RBFNN and ELM methods are applied to predict the structural properties and diameter of fine denier polyester fibers based on the experimental data. Lastly, different predictive performance indicators, namely mean square error (MSE) and mean absolute error (MAE) are imported to evaluate the above three methods. The predicted results which the MSE values of GPR, RBFNN and ELM models are respectively 0.461, 0.358, 0.136, and the MAE values of GPR, RBFNN and ELM models are respectively 0.625, 0.564, 0.320 show that ELM model provided superior performance over the GPR and RBFNN models in predicting the structural properties and diameter of fine denier polyester fibers.

Research on rainfall prediction based on RBF neural network model and stormwater inundation risk in scenic areas: A case study of the Yesanpo Scenic Area, Baoding, China

Research on stormwater inundation risk and rainwater management in scenic areas has a lot to do with rainfall during the flood season. When the measured rainfall data is limited, an artificial network model with nonlinear mapping capability can be applied to predict rainfall data during the flood season, which increases the sample size of rainfall data and improves the accuracy of research results. Based on a radial basis function (RBF)neural network model, this paper takes the Yesanpo Scenic Area in Baoding City, Hebei Province as an example to estimate the monthly maximum rainfall data during the flood season (July–September) of 2022, 2023, and 2024 in the study area. On this basis, the Pearson III frequency curve is used to calculate the design rainfall corresponding to the rainfall frequency of 20%, 5%, and 2%. With the help of SCS-CN model and ArcGIS spatial analysis tools, the stormwater inundation areas are simulated in the study area, which are divided into three risk levels: high, medium, and low, providing a reference for the stormwater management in the Yesanpo Scenic Area.

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning neural network control approach

This paper addresses the robust attitude synchronization issue in a multi-spacecraft formation system subjected to limited communication, space disturbances, modeling uncertainties, and actuator faults. To accommodate limited inter-spacecraft communication, a dynamic event-triggered mechanism is designed to reduce the communication trigger frequency by dynamically adjusting the trigger threshold. Moreover, an event-based distributed self-learning neural-network control (SLN2C) law is developed to guarantee robust attitude synchronization during multi-spacecraft formation. In the SLN2C scheme, a learning radial basis function neural network (RBFNN) model is proposed to online approximate and compensate for lumped disturbances, in which an iterative learning algorithm with a variable learning intensity is adopted to update the weight matrix of the RBFNN model. Compared with the traditional fixed learning intensity, a variable one can reduce initial oscillation and weaken the saturation response. Numerical simulations and comparisons are performed to illustrate the effectiveness and superiority of the proposed event-based spacecraft attitude synchronization control method.

Transformer fault diagnosis based on Chaotic Particle Swarm Optimization RBF Neural Network

This article uses logistic chaotic mapping to improve the particle swarm algorithm parameters and construct the chaotic particle swarm optimization (CPSO) algorithm. Then, the CPSO algorithm is used to optimize the width, weight, and center values of the Radial Basis Function Neural Networks (RBFNN) to improve the RBFNN model used to diagnose transformer fault types. Results show that the CPSO-RBFNN model has a small mean square error and high accuracy in diagnosing transformer faults.

CFD analysis and RBFNN-based optimization of spraying system for a six-rotor unmanned aerial vehicle (UAV) sprayer

To reduce pesticide waste caused by low spray droplet coverage on target surface, the spraying system parameters of a six-rotor UAV sprayer are optimized. Computational Fluid Dynamics (CFD) is employed to simulate the downwash airflow distribution and droplet deposition for the UAV sprayer. Latin hypercube sampling method is used to obtain 15 sample points to train the surrogate model with CFD results. The validated CFD model is coupled with radial basis function neural networks (RBFNN) and genetic algorithm (GA) to optimize the spraying system parameters, including numbers of nozzles, horizontal position of nozzles and vertical position of nozzles. The overall correlation coefficient (R) of this RBFNN model is about 0.949, indicating good fitting performance. The optimal nozzles number of a six-rotor UAV sprayer is 4. The spraying droplet coverage is increased by 16.27%, 23.25% and 18.24% at a rotor height of 1 m, 2 m and 3 m above from the target surface, respectively, compared with the original six-rotor UAV spraying system structure. The spraying droplet coverage and pesticide utilization of the six-rotor UAV sprayer are improved through optimizing the mounting number and mounting positions of the nozzles in the spraying system. The research results provide a theoretical basis for further design and optimization of the multi-rotor UAV sprayers.

Predicting quality of the whole egg powder during storage: Based on Arrhenius and radial basis function model

The quality of whole egg powder (WEP) deteriorates during long-term storage due to oxidation, which causes caking, discoloration and reduction of functional properties. The purpose of this study is to investigate the changes in lipid oxidation, protein oxidation, and functional characteristics of WEP during accelerated storage and to establish an Arrhenius model and radial basis function neural network (RBFNN) model to predict the oxidative changes in WEP. During storage, the peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) of lipid oxidation significantly increased, and the Fourier transform infrared spectroscopy results showed an increasing trend in the characteristic absorption peaks of lipid hydroperoxides, free fatty acids, and cis fatty acids. The carbonyl groups, surface hydrophobicity, and fluorescence intensity significantly decreased, and the functional characteristics showed a decreasing trend in the solubility, foaming, and emulsifying properties of WEP. Most importantly, compared to the Arrhenius model, the RBFNN model could predict and assess the lipid and protein quality of WEP more accurately, with a small mean squared error (MSE) (0.00048) for storage at 20–60 °C.

Experimental and numerical investigation of heat transfer and flow of water-based graphene oxide nanofluid in a double pipe heat exchanger using different artificial neural network models

In this study, a water-based graphene oxide nanofluid was utilized in a counter-current flow double pipe heat exchanger (DPHE). The inner pipe carried the hot fluid (deionized water-based fluid). In the outer pipe, the cold fluid was flown, in which the experiments were performed once for deionized water-based fluid and once for graphene oxide nanofluid. These experiments were conducted at various inlet hot fluid temperatures of 35, 45, and 55 °C, volumetric concentrations of 0.01, 0.055, and 0.1% for nanofluid, and cold fluid flow rates of 14.4, 18.9, and 23.4 ml/s to evaluate the thermohydraulics of the DPHE. The results demonstrated that an increase in the flow rate and concentration of nanoparticles led to an improvement in the heat transfer coefficient (HTC) . It is also observed that inlet hot fluid temperature had a negligible effect on this coefficient compared to the concentration and flow rate. Additionally, the results showed that the friction factor and pressure drop of nanofluid were higher than those of the basefluid and their values were increased by increasing the concentration. The maximum increase compared to the basefluid was 72% for the friction factor and 111%, for the pressure drop. Besides, radial basis function neural network (RBFNN) and multi-layer perceptron neural network (MLPNN) models were designed for estimating the HTC. Both models accurately predicted the coefficient, but the RBFNN model exhibited higher accuracy than the MLPNN model. The results further indicated that the nanofluid outperformed the basefluid in terms of HTC. Due to the exceptionally high thermal conductivity of graphene nanoparticles, the HTC of the nanofluid was improved by up to 85% compared to the basefluid. The optimum value of HTC was achieved at a temperature of 55 °C, a volume concentration of 0.1%, and a flow rate of 23.4 ml/s.

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.