Radial Basis Function Network Model Research Articles

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Machine vision-based online detection method for color characteristics of cobalt extraction solution

Cobalt, as a rare metal, plays an important role in strategic resources due to its physical, chemical, and mechanical properties in applications. In order to achieve automatic control of the cobalt extraction process, it is necessary to conduct online detection of the color characteristics of the cobalt extraction solution. Unfortunately, due to the lack of online automatic detection methods for the color of cobalt extraction solution, the automatic control of the extraction process has not yet been achieved. Currently, the color of cobalt extraction solution needs to be observed and judged by operators based on experience. This will result in significant detection errors and time lag. To change this situation, an online detection method for color features of cobalt extraction solution based on machine vision is proposed in this article. Firstly, we establish a real-time image acquisition environment for detecting object colors, and then use the Gaussian filters and K-means algorithm to denoise and segment the image. In order to correct color distortion in images caused by ambient light, we measure the color feature values of a set of standard color cards using the machine vision system and a standard spectrophotometer. Based on this test data, we establish chromaticity correction models for the extraction solution using back propagation (BP) network and radial basis function (RBF) network, respectively. The models can correct the color characteristic values of the extraction solution, overcome environmental interference, and thus obtain more accurate color characteristic values of the extraction solution. The established model can be used for online automatic color detection of cobalt extraction solution. The modeling results indicate that in the established color measurement environment, the RBF network model outperforms the BP network model in color feature detection accuracy.

Eligibility of village fund direct cash assistance recipients using artificial neural network

<p>Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.</p>

Actual Shear Rate Prediction Associated with Wall Slip Phenomenon Using Radial Basis Function Network

Wall slip can be defined as a phenomenon where the particles in a suspension moving away from the wall boundary, leaving a thin liquid-rich layer adjacent to the boundary. Such a phenomenon may induce a significant impact on rheological measurements, particularly to viscosity, shear stress, and shear rate. Suspension has wide applications such as food processing, personal care products, pharmaceuticals, paints, medicine, and agrochemical. The traditional technique for the actual shear rate prediction is challenging yet difficult and non-favourable from the perspective of time and cost-effectiveness. Therefore, development of a mathematical computational model which has the ability to perform the prediction task with an acceptable level of accuracy is highly in need. Since radial basis function network (RBFN) has the ability to perform input-output mapping in a highly accurate manner, both of the approaches are employed to generate the actual shear rate prediction model. Through the model evaluation using a series of statistical analyses, it was found that RBFN model V is the best model as it shows the highest coefficient of determination (0.9998), lowest mean squared error (0.001058) and root mean squared error (0.001447), most negative Akaike information criterion (-18426.5) and Bayesian information criterion (-18415.5), and the smallest percentage error.

Stochastic buckling response of variable fiber spacing composite plate under thermal environment

The effectiveness of variable fiber spacing composite (VFSC) laminates subjected to thermal loading is compared with uniformly distributed fiber composite (UDFC) laminates. The higher-order shear deformation theory (HSDT) is adopted to develop a FE model for estimating critical thermal buckling load. Three types of fiber distribution patterns are considered to study the effect of the non-uniform spacing distribution of fiber on thermal buckling, and the results are compared with UDFC. Further, a surrogate model based on the radial basis function network (RBFN) is developed to examine the impact of composite properties uncertainty on the critical buckling response of UDFC and VFSC laminates under thermal loading. The RBFN model is found to be highly efficient and possesses a close match with Monte Carlo simulation (MCS). Though the VFSC laminates possess a large deviation (standard deviation) in the buckling temperature, the stochastic effect (standard deviation/mean) shows relatively minimal variation in VFSC and UDFC laminates. The effect of each composite property is investigated for different lamination sequences and boundary conditions. From the sensitivity analysis, the thermal expansion coefficient of matrix [Formula: see text] is found to be the most sensitive property for all the investigated cases. Lastly, the RBFN model is utilized to quantify reliability parameters for multiple stochasticity levels.

Combining varying training data-based artificial intelligence models for energy price forecasting

ABSTRACT In this work, the price-forecasting accuracy of an ensemble of seasonal auto-regressive integrated moving average (SARIMA), multiple linear regression (MLR), feed-forward neural network (FFNN), and radial basis function (RBF) network models has been assessed in a single series modeling framework. The methodology involved training the individual models using past data windows of varying sizes and making the forecast for the next five days on a daily rolling basis. The hourly spot price data of the Iberian Electricity Market (MIBEL) is selected as the test case system. All the models have been tested on high and low-volatile data sets to assess their forecasting abilities. The forecast performance has also been assessed by participating in a real-time forecasting competition and comparing it with the earlier models proposed in the literature. The results show that the ensemble model is effective in producing better forecasts. But its forecast accuracy is greatly affected by the size of the training window and the combination of various models. The ensemble of the FFNN and RBF network models performs best when conditions are volatile. Moreover, during volatile periods it is better to use a small window size for training as compared to a large window size.

Adaptive Basis Function Selection Enhanced Multisurrogate-Assisted Evolutionary Algorithm for Production Optimization

Summary Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular approach for solving reservoir production optimization problems. The radial-basis-function network (RBFN) is a robust surrogate model technology suitable for reservoir development with numerous wells and a long production lifetime. There are several types of basis functions for constructing RBFN models. However, existing research shows that selecting the basis function with competitive performance for the current optimization problem is challenging without prior knowledge. In conventional SAEAs, the basis function is often predetermined, but its prediction accuracy for the problem at hand cannot be guaranteed. Furthermore, canonical SAEAs usually employ only one surrogate model for the entire optimization process. However, relying on a single surrogate model for optimization increases the probability of search direction misdirection due to prediction deviations. In this paper, a novel method named adaptive basis function selection enhanced multisurrogate-assisted evolutionary algorithm (ABMSEA) is introduced for production optimization. This method mainly includes two innovations. First, by training and testing different types of basis functions, the one with the best prediction performance is adaptively selected. Second, the ensemble model is constructed using the bootstrap sampling method, comprising multiple global surrogate models based on the selected best basis function. To search for a set of solutions that perform well on multiple surrogates, we employ an efficient multiobjective optimization (MOO) algorithm called nondominated sorting genetic algorithm II (NSGA-II). This algorithm uses the surrogates themselves as objective functions, aiming to find solutions that yield favorable results across multiple surrogates. The proposed method improves the efficiency of production optimization while enhancing global search capabilities. To evaluate the effectiveness of ABMSEA, we conduct tests on four 100D benchmark functions, a three-channel model, and an egg model. The obtained results are compared with those obtained from differential evolution (DE) and three other surrogate-model-based methods. The experimental results demonstrate that ABMSEA exhibits an accurate selection of competitive basis functions for the current optimization period while maintaining high optimization efficiency and avoiding local optima. Consequently, our method enables optimal well control, leading to the attainment of the highest net present value (NPV).

Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Breast cancer is one of the most common types of cancer that kills women. When cells become uncontrollably large, cancer develops. As a result, detecting and classifying breast cancer in its early stages is essential so that patients can take the appropriate precautions. On the other hand, mammography images have relatively low sensitivity and effectiveness in detecting breast cancer. Furthermore, MRI (Magnetic Resonance Imaging) has higher detection sensitivity for breast cancer than mammography. In this research, a novel Radial Basis Function Networks model (RBFN) with a Mayfly Optimization Algorithm (MAO) mechanism has Breast MRI scans aid in the early detection of breast cancer. Following the system's training on Magnetic Resonance Imaging (MRI) breast images, a unique Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter is developed for pre-processing noisy MRI image material. Backgrounds were removed before recovering breast cancer photos with a Contrast Limited Histogram Equalization (CLAHE) filter. Furthermore, the new study effort's performance is compared to earlier studies and this model is simulated using Python. The proposed model, RBFN-MAO, also outperforms previous models in terms of performance and precision with an accuracy of 97.54%. In comparison, it is 85.28, 80.95, 76.94, 85.39 and 90.32% for Convolution Neural Networks You Only Look Once (CNN-YOLO), Residual Networks (ResNet50), Diffusion Convolution Neural Networks (DCNN), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models, respectively.

Live Pig-Weight Learning and Prediction Method Based on a Multilayer RBF Network

The live weight of pigs has always been an important reference index for growth monitoring and the health status of breeding pigs. An accurate weight acquisition of breeding pigs is the key to guide the scientific feeding of breeding pigs and improve economic benefits. Compared with the traditional contact measurement method, the non-contact weighing method of live pigs can greatly reduce human–pig contact and measurement errors. In this paper, a deep neural network is constructed which can automatically and accurately predict the weight of live pigs by measuring multiple body parameters. Because of the good generalization ability of the radial basis function (RBF) neural network and the better fitting ability of multilayer network than the traditional single-layer network, this paper introduces a full-connection model in the middle layer, connects multiple RBF layers, builds a multilayer RBF network, and invents the automatic learning method of pig weight based on the network. In this method, the body length, body height, body width, and five other body parameters are input, after normalization, into the multilayer RBF network model for training, and resultingly the network gives a predicted weight. Among our 4721 live pigs, there are 2452 sows and 2269 boars, among which 2000 samples of sows are randomly selected as training sets and 452 samples as test sets; 1930 samples of boars are taken as training sets and 339 samples as test sets. The test shows that the performance of the network structure is as follows: R2 is 0.63, MAE is 1.85, RMSE is 5.74, and MAPE is 1.68.

Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning

The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer perceptron (MLP) and radial basis function (RBF) models. A DRI operation takes place inside the shaft furnace. A shaft furnace reactor is a gas-solid reactor that transforms iron oxide particles into sponge iron. Because of its low environmental pollution, the MIDREX process, one of the DRI procedures, has received much attention in recent years. The main purpose of the shaft furnace is to achieve the desired percentage of solid conversion output from the furnace. The network parameters were optimized, and an algorithm was developed to achieve an optimum NN model. The results showed that the MLP network has a minimum squared error (MSE) of 8.95 × 10−6, which is the lowest error compared to the RBF network model. The purpose of the study was to identify the shaft furnace solid conversion using machine learning methods without solving nonlinear equations. Another advantage of this research is that the running speed is 3.5 times the speed of mathematical modeling.

Research on stamping forming prediction of aluminum alloy sheet based on RBF neural network

In order to accurately predict and reduce the possible defects in the stamping process of an aluminum alloy sheet, the simulation data of the sheet thickness for the 6016 aluminum alloy in the stamping process were obtained by the Hill’48 yield criterion based on finite element ABAQUS/Explicit solver. Taking blank holder force, friction coefficient, stamping speed, and die clearance as input parameters, the radial basis function (RBF) network model for predicting the maximum thinning rate of the stamping aluminum alloy sheet was established. The results show that the RBF network model constructed in this paper has high precision and can reflect the complex relationship between the stamping process parameters and the maximum thinning rate well by comparing the finite element simulation and neural network prediction results. It is of great significance to improve the optimization efficiency of the stamping process of the aluminum alloy sheet and reduce the actual experimental cost.

Reducing the number of centers in a probabilistic neural network via applying the first neighbor means clustering algorithm

Probabilistic neural network is a variant of feedforward neural network models and has been successfully applied for various pattern classification purposes. Unlike other feedforward neural network models, probabilistic neural network, a type of radial basis function network models, has essentially only two types of the network-parameters to choose in advance, i.e. the locations of the centers and a single value of radius; a central issue relevant to the application of probabilistic neural network is therefore to determine an appropriate number of the centers accommodated within the network. In the original probabilistic neural network framework, all the training data are allocated to the respective centroid vectors, and thus the network size generally tends to be large, resulting in demanding computational resource. To alleviate this problem, clustering algorithms are commonly employed to shrink the size of the training data. In this work, reduction in the number of centers in a probabilistic neural network is addressed, via the utility of first neighbor means clustering algorithm that is non-iterative and requires only a single algorithmic hyper-parameter; such a choice is desirable in practice. Simulation results using seven publicly available databases for pattern classification tasks show that the first neighbor means clustering algorithm can yield a relatively compact-sized network within short computation time, while exhibiting a reasonably high classification performance, in comparison with communication with local agents, k-means, orthogonal least squares, resource allocating network, and resource vector machine algorithms.

Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler

<span>The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.</span>

Computational methods for predicting the outcome of thoracic transplantation

Cardiac disease and the death rates due to coronary heart failure and cardiomyopathy are increasing. Thoracic transplantation is now a widely accepted therapeutic option for end-stage cardiac failure. The survival rate after the organ transplantation is crucial. Survival prediction after heart transplantation is a hot area of research. The use of conventional statistical techniques is computationally expensive and does not provide reliable solutions. Artificial Neural Networks based survival prediction helps surgeons make precise decisions and predict the best outcomes. The proposed system implements multi-layer perceptron algorithm, which shows good performance in survival prediction. We also implemented our work in the Radial Basis Function Network model to prove the accuracy of proposed model. For this research study, data were collected from United Network for Organ Sharing database and extracted the relevant thoracic transplantation survival prediction attributes with the help of suitable data mining techniques. We obtained an accuracy of 97.1% from the multi-layer perceptron model with the evaluation of various performance measures. In order to assure the validity of the proposed model we implemented the Radial Basis Function model and obtained an accuracy of 92.37%. We collated the accuracy of proposed survival prediction models with existing systems and proved that the proposed system appeared to be best for survival prediction with higher accuracy compared to 85.9% in the existing system. The outcome of the model will be an asset for the lifesaving procedures in the medical field.

Application of medical supply inventory model based on deep learning and big data

The existing management structure of medical supply inventory (MSI) is not sufficiently effective, and it is incompetent to solve the problems of medical supply stock control in public security emergencies. Therefore, deep learning and big data technology are employed in this work to optimize the stock control structure and enhance management efficiency, so that the optimized management structure can play an excellent role in the material supply of emergencies. After browsing copious literature, the economic ordering models with infinite/limited supply rate and without shortage are innovatively constructed to realize efficient management of emergency supplies inventory. Besides, the optimized fixed-point and quantitative ordering method of safety stock is employed to construct the MSI models for scarce emergency supplies and the time-sensitive emergency supplies, respectively. Then, an earthquake-related emergency is taken as a case and data source to evaluate the solution results of the emergency MSI model. Moreover, the stacked auto-encoders (SAE) algorithm is used to build the demand prediction model for MSI. Finally, a simulation experiment compares the SAE-based demand prediction model for MSI with a back propagation neural network (BPNN) model and radial basis function network (RBFN) model to verify the model’s performance. The experimental results demonstrate that after 150 times of training, the error between the predicted value and the actual value of each model is within 30, and the prediction accuracy is significantly improved. After 170 times of network training, the mean absolute error (MAE) values of BPNN model and RBFN model are 31.98 and 73.73, respectively. In contrast, the MAE value of the SAE-based model is 21.32, which is superior to the other two models. Evidently, the management structure of MSI is optimized by dividing the emergency MSI into three MSI models for the critical emergency supplies, scarce emergency supplies, and the time-sensitive emergency supplies. The research outcome can provide essential logistical support for dealing with public security emergencies.

GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones.

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

SummaryAccurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.

Vibration and Uncertainty Analysis of Functionally Graded Sandwich Plate Using Layerwise Theory

In the present study, free vibration analyses of porous functionally graded material (FGM) sandwich plates are carried out in thermal environment. Two types of sandwich plates such as sandwich with FGM face sheets and homogeneous core and sandwich with homogeneous face sheets and FGM core are considered for free vibration analysis with nonlinear temperature variation over the thickness direction. The metal and ceramic components are assumed to be temperature dependent. The higher-order layerwise theory is adopted with an eight-noded rectangular element to build a finite element model for free vibration analysis. A radial basis function network (RBFN) based surrogate model is developed to investigate the stochastic frequency response due to the uncertain material properties. The accuracy and the efficiency of the RBFN model are established by comparing the results with that of Monte Carlo simulation. Further, the developed surrogate model is implemented to perform various parametric studies and sensitivity analyses. The influence of the volume fraction index, types of porosity, and thermal gradient on the stochastic frequency response are analyzed. Though the influence of different stochastic input parameters is strongly dependent on the volume fraction index, the mass density of metal is found to be the most sensitive parameter for the sandwich plate with FGM face sheets and homogeneous core.

Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm

A nonlinear switching control method was proposed based on an improved quasilinear autoregressive with exogenous inputs (quasi-ARX) radial basis function (RBF) network model and the support vector regression (SVR) algorithm. The RBF network was chosen as the nonlinear part of the improved quasi-ARX prediction model. The proposed controller design method was divided into 3 steps: firstly, the nonlinear parameters of the model were determined with the clustering method; secondly, the linear SVR algorithm was used to solve the robustness problem of the control system; thirdly, the switching criterion function was given based on the control error, and the control sequence were determined according to the switching law. Finally, a numerical example was given to verify the effectiveness of the proposed method.

Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures

Steel-Concrete Composite floor systems are one of the essential components in the construction industry. Recent studies have shown that fire-induced problems damage shear connectors and change the behavior of composite systems. To predict the performance of connectors, experimental tests are generally conducted at elevated temperatures or fire conditions. However, these tests need plenty of time, cost, and effort. This paper aims to propose a soft computing (SC) approach to predict the behavior of angle shear connectors at elevated temperatures. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS), a particle swarm optimization (PSO) algorithm, and a genetic algorithm (GA) are hybridized and a novel ANFIS-PSO-GA model is proposed. To evaluate the performance of the ANFIS-PSO-GA model, a radial basis function network (RBFN) along with an extreme learning machine (ELM) are also developed. Finally, the performance of the ANFIS-PSO-GA, RBFN, and ELM are compared in the terms of different statistical indicators. The results of the paper show that the SC approach is applicable in the behavior prediction of angle connectors at elevated temperatures. Besides, it was concluded that the ANFIS-PSO-GA model can provide better estimations of load and slip in comparison with those of RBFN and ELM models.