RBF Neural Network Model Research Articles

Bent pipe flow reconstruction based on improved ultrasound Doppler velocimetry and radial basis function neural network

In piping systems, the flow through a bend is typically undeveloped, unsteady, and complicated. In this study, we proposed a novel method to accurately predict downstream flow in bent pipes based on radial basis function neural network (RBFNN) and ultrasound Doppler velocimetry (UDV). An improved UDV method was developed for the measurement of complex flow in bent pipes. The downstream flow characteristics of three different bend configurations were investigated using computational fluid dynamics (CFD). The proposed RBFNN models established the relationships between the velocity profile along the symmetric axis and the velocity distribution over the pipe cross-section. Numerical data were used for model training and validation, whereas the combined datasets with UDV data as inputs and the corresponding CFD data as outputs were used for model modification. The model prediction performance was then evaluated with new UDV inputs by comparing the predicted velocities with the associated CFD results. The results indicated that the velocity profiles obtained using modified UDV agreed well with the present numerical simulation. Finally, the trained model exhibited satisfactory flow reconstruction performance and high flowrate prediction accuracy, with a maximum error of approximately 3%. This work contributes to the application of the UDV method in complex flow measurements and further demonstrates that artificial neural networks (ANNs) are promising for modeling fluid mechanics.

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach.

An Efficient Reliability Analysis Method Based on the Improved Radial Basis Function Neural Network

Abstract This paper develops an efficient reliability analysis method based on the improved radial basis function neural network (RBFNN) to increase the accuracy and efficiency of structural reliability analysis. To solve the problems of low computational accuracy and efficiency of the RBFNN, an improved RBFNN method is developed by transferring the sampling center of Latin hypercube sampling (LHS) from the mean values of random variables to the most probable point (MPP) in the sampling step. Then, the particle swarm optimization algorithm is adopted to optimize the shape parameters of RBFNN, and the RBFNN model is assessed by the cross-validation method for subsequent reliability analysis using Monte Carlo simulation (MCS). Four numerical examples are investigated to demonstrate the correctness and effectiveness of the proposed method. To compare the computational accuracy and efficiency of the proposed method, the traditional radial basis function method, hybrid radial basis neural network method, first-order reliability method (FORM), second-order reliability method (SORM), and MCS method are applied to solve each example. All the results demonstrate that the proposed method has higher accuracy and efficiency for structural reliability analysis. Importantly, one practical example of an industrial robot is provided here, which demonstrates that the developed method also has good applicability and effectiveness for complex engineering problems.

Research on the water quality detection method based on fluorescence spectrometry and PSO-RBF network

In this paper, we propose a model-based method for lake water quality testing that is used to detect and analyze the content of pollutants in lake. The known and unknown contaminants in mixed solutions are detected by fluorescence spectroscopy and spectrophotometry. A PSO-RBF neural network model is established to predict the content of pollutants in the lake. The prediction results of the PSO-RBF neural network model are compared with the ones of a RBF neural network model to verify the computational efficiency and the prediction accuracy of the model. The results show that the PSO-RBF neural network could be used to predict the pollutant content of lake water quality effectively, which provides a fast and practical method for water quality testing.

Agricultural products price prediction based on improved RBF neural network model

ABSTRACT The agricultural products price has been affecting people’s livelihood issues and economic and social security and stability. The cyclical fluctuation of the price of agricultural products often indirectly affects the inner psychological demand of agricultural consumers. If the prices of production and processing industries, which rely on cheap raw materials as basic raw materials, are subject to frequent and abnormal fluctuations, it is likely to cause further widespread concerns about people’s lives, and ultimately lead to a vicious cycle of falling commodity prices. In recent years, as a result of lack of timely authoritative information all the time, the market price of agricultural products appeared a lot of varieties before short-term rise and fall repeatedly phenomenon. This paper attempts to take the quantity of garlic produced and sold by pork in China as the key object of analysis and research, and analyzes the level of market price index and the main factors influencing the price of pork sales and edible garlic demand in China in recent ten years. In information economics, financial market theory, statistical methods and other relevant mathematical model as the main guidance, combined with China’s agricultural prices during the period of policy, select the RBF neural network theory and the analysis methods for technical improvements, in a variety of market factors affecting economic operation under the mixed operation China period in our country agricultural prices technology wave prediction rule, broken Based on the traditional artificial statistics and forecasting model method of agricultural product price, the short-term forecasting model of Chinese agricultural product market price theory based on information technology innovation method was established.

Application of RBF and GRNN Neural Network Model in River Ecological Security Assessment—Taking the Middle and Small Rivers in Suzhou City as an Example

The analytic hierarchy process is used to construct the health evaluation index system and grading standard of small- and medium-sized rivers in the region. Based on the principles of RBF and GRNN neural network algorithms, the river health evaluation models of radial basis function neural network (RBF) and general regression neural network (GRNN) algorithms are constructed, respectively. The network training samples are constructed by the interpolation method. The standard value of river health classification evaluation is taken as the “prediction” sample to “predict”. Then the results are applied as the division basis of the river health classification evaluation, which is to evaluate and analyze the health status of small and medium rivers in Suzhou Prefecture. The results indicate that: (1) the RBF and GRNN neural network models have exactly the same results in evaluating the health of small and medium rivers in the region, and are basically the same as the back propagation (BP) neural network evaluation results. RBF and GRNN neural network models have the advantages of fast convergence speed, high prediction accuracy, harder to fall into local minima, less adjustment parameters, and only one spread parameter, which can predict and evaluate the network faster, which is a large calculation advantage. (2) The health evaluation level of the main rivers in Suzhou Prefecture is from grades II to III, that is, between healthy and sub-healthy. This grade objectively reflects the health status of small- and medium-sized rivers in the region, which can provide a reference for the sustainable management of regional rivers and ecological environment construction.

Non Intrusive Power Load Monitoring Method Based on Machine Learning

In order to improve the accuracy of non-invasive power load monitoring and shorten the monitoring time, a new non-invasive power load monitoring method based on machine learning is proposed. Firstly, the power load data is collected by sensors and normalized. Secondly, based on the normalized results of power load data, the RBF neural network model in machine learning is used for iterative training to extract power load characteristics. Finally, according to the characteristics of power load, the hidden Markov model is used to transform the non-invasive power load monitoring problem into a decoding problem, and the improved Viterbi algorithm is used to solve the hidden Markov model, and the non-invasive power load monitoring is completed according to the obtained state sequence.

Analysis of motion interference characteristics of underwater vehicles salvo based on the RBF Neural Network

Compared with the launch process of a single underwater vehicle, the salvo of multiple vehicles is more complex and needs to be more deeply studied. When the first vehicle is launched, it will disturb the flow field and affect the attitudes and trajectories of the subsequent vehicles. If the impact range is not clearly defined, the launch safety will be threatened. In this paper, we outline the underwater salvo process of two vehicles. A RBF (radial basis function) neural network model is established, which is driven by data generated through numerical simulation. The model is applied to predict the interference variation law of motion characteristic variables that the first vehicle to the second under different launch conditions. The results show that the RBF neural network model can predict the interference of the motion characteristic variables that the first vehicle to the second vehicle in the process of salvo, which is more effective and accurate than the numerical simulation and experimental methods. In addition, the results reveal the influence of the first vehicle on the deflection angle and displacement of the second vehicle under different launch parameters, in which the launch time interval is the most sensitive factor affecting the motion characteristics of the vehicle.

Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network

After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.

Noncontrast MRI in assessing venous reflux of legs using QFlow analysis and radial basis function neural network technique

Since venous reflux is difficult to quantify, triggered angiography non-contrast-enhanced (TRANCE)-magnetic resonance imaging (MRI) is a novel tool for objectively evaluating venous diseases in the lower extremities without using contrast media. This study included 26 pre-intervention patients with superficial venous reflux in the lower extremities and 15 healthy volunteers. The quantitative flow (QFlow) analyzed the phase shift information from the pixels within the region of interest from MRI. The fast and simple radial basis function neural network (RBFNN) learning model is constructed by determining the parameters of the radial basis function and the weights of the neural network. The input parameters were the variables generated through QFlow, while the output variables were morbid limbs with venous reflux and normal limb classification. The stroke volume, forward flow volume, absolute stroke volume, mean flux, stroke distance, and mean velocity of greater saphenous veins from QFlow analysis could be used to discriminate the morbid limbs of pre-intervention patients and normal limbs of healthy controls. The neural network successfully classified the morbid and normal limbs with an accuracy of 90.24% in the training stage. The classification of venous reflux using the RBFNN model may assist physicians in clinical settings.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

MODELING THE TENSILE STRENGTH OF CONCRETE WITH POLYETHYLENE TEREPHTHALATE (PET) WASTE AS REPLACEMENT FOR FINE AGGREGATE USING ARTIFICIAL NEURAL NETWORK

Tensile strength of concrete made with polyethylene terephthalate (PET) waste as replacement for fine aggregate was modelled using artificial neural network. A multilayer feedforward neural network (MLFFNN) and radial basis function (RBF) methodology were compared to see which was more accurate. The MLFFNN modelling results showed a predictive accuracy of 95.364% and a root mean square error value of 4.4409 × 10-16 while RBF neural network modeling results showed a higher predictive accuracy (99.509%) with a lower root mean square error value (1.6653 × 10-16). It is concluded that ANN models accurately predicted the tensile strength of PET concrete.

Analysis of Factors Affecting Import and Export of Underdeveloped Regions Based on RBF Neural Network - A Case Study of Guizhou Province

Guizhou Province, as an inland province of China without borders and seas, has relatively backward economic development, but its economic growth is fast and its development potential is great. Studying its import and export trade is important to improve residents ' income and accelerate internationalization. This paper analyzes and establishes the indicator system of regional import and export influencing factors through literature analysis, and then uses ( radial basis function ) RBF neural network to analyze the influencing factors of import and export in Guizhou Province. Taking the economic data of Guizhou Province from 2001 to 2020 as samples, RBF neural network is used as a tool to analyze the relationship between various influencing factors and total import and export volume. The results show that when the number of hidden layer neurons is 14 or more, the RBF neural network model accuracy is the highest, R2 is 0.7005, and MAPE is 0.0834. Among the first-level indicators affecting import and export, the importance of the regional internal economic level is the highest. Among the second-level indicators, the importance of scientific and technological progress is the highest, followed by GDP.

Development process and height of the mining-induced water fractured zone over the longwall goaf

Abstract Providing accurate prediction methods for mining-induced water fractured zones over the longwall goaf has become increasingly significant because of the grand economic and technological challenges of deeper extraction. Taking the No. 839 coalface in the Qingdong mining area as a case study, a comprehensive study of the water-flowing fractured zone (WFFZ) formation process and peak height were carried out by the radial basis function (RBF) neural network model, parallel electrical monitoring (PEM) technology, numerical simulation model and the empirical formula method. The results show that the destruction does not occur simply layer-by-layer from the bottom up. Rather, the fractured areas appear first in soft layers above the goaf. With the advance of footage, the destruction gradually extends from soft layers to hard layers, and the WFFZ is formed when the fractured areas fit closely together. Also, the change process of the WFFZ shape is closely related to rock mass heterogeneity. In addition, the results of the comparison suggest that the RBF neural network model had more accuracy in predicting the height of the WFFZ than the numerical simulation model and empirical formula method.

An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation. To achieve safe management and optimal control of batteries, the state of charge (SOC) is one of the important parameters. The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years. However, a common problem with these models is that their estimation performances are not always stable, which makes them difficult to use in practical applications. To address this problem, an optimized radial basis function neural network (RBF-NN) that combines the concepts of Golden Section Method (GSM) and Sparrow Search Algorithm (SSA) is proposed in this paper. Specifically, GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model, and its parameters such as radial base center, connection weights and so on are optimized by SSA, which greatly improve the performance of RBF-NN in SOC estimation. In the experiments, data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model, and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.

Research on a nondestructive model for the detection of the nitrogen content of tomato.

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-mean-square error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (Rc 2) and prediction set (Rp 2) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the Rc 2 and Rp 2 values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves.

Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems.

The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV).

Measurement and Prediction of Micronewton Class Thrust of Electric Propulsion Based on the Torsional Pendulum and Machine Learning Technique

Accurate and direct measurement of thrust is the most fundamental requirement in the evaluation of electric thruster performance. This article performed the measurement and prediction of the micronewton class thrust of micro gridded ion thrusters (μGITs) based on a novel design of torsional pendulum and high precision neural network models. The developed torsional pendulum adopts specially designed liquid metal connectors to eliminate the interference caused by the power supply cables and propellant feeding lines, realizing an accurate thrust measurement of radio frequency (RF)- and dc-μGIT from micronewton level to millinewton level with a measuring range of 0–10 mN and a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.4~\mu \text{N}$ </tex-math></inline-formula> . Results show that the RF-μGIT generates a thrust of 0.46–2.43 mN for the RF power from 100 to 120 W and the xenon flow rate from 0.75 to 1.50 sccm. The dc-μGIT generates a thrust of 0.016–0.310 mN for the acceleration voltage from 700 to 1050 V and the flow rate from 0.2 to 2.0 sccm. Both the artificial neural network (ANN) and the radial basis function neural network (RBF-NN) are adopted to predict the relationship between the thrust and input parameters of RF-μGIT. The predicted thrusts by ANN deviate about 10% from the measured data, and the maximum relative deviation using RBF-NN model is about 2%. Furthermore, the RBF-NN model is used to obtain the distribution maps of thrust and specific impulse under 600 different working conditions and provides a solution for intelligent regulation of thruster performance. For the first time, the combination of thrust measurement and machine learning (ML) provides a new approach for the fast performance evaluation, prediction and regulation of electric thrusters.

Non-destructive Detection of Fatty Acid Content of Camellia Seed Based on Hyperspectral.

As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R2) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (RC2) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (R2P) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.

Prediction of the hydrodynamic disturbance characteristics for two projectiles launched successively underwater based on the radial basis function neural network

When two projectiles are successively launched under different launch parameters, the motion of the first projectile affects the hydrodynamic characteristics of the second projectile. To predict and study such disturbances, a radial basis function (RBF) neural network model is established in this paper. Compared with the underwater launch of a single projectile, the hydrodynamic loads for two projectiles successively launched are more complex and severe. When the first projectile is launched, it will affect the forces and moments of subsequent projectiles, leading to launch failure. Thus, we apply a numerical simulation method that is verified through experiments to simulate two projectiles successively launched underwater. Then, we use the generated data to train the RBF neural network. The results show that vortices will form at the tail of the first projectile after launch due to viscous effects, which is the main reason for the hydrodynamic disturbance that affects the second projectile. Compared with numerical simulations and experimental methods, the RBF neural network model can more effectively predict the disturbance of the hydrodynamic characteristic variables of the first projectile to the second projectile. This disturbance can be reduced by increasing the spatial distance of the two projectiles, increasing the time interval between launches, and reducing the platform velocity. However, the launch time interval is the most sensitive factor affecting the hydrodynamic characteristics of projectiles.