RBF Neural Network Model Research Articles

Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.

Prediction of Flood in Barak River using Hybrid Machine Learning Approaches: A Case Study

Abstract Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

Residual life prediction of mining cables based on RBF neural network

The RBF (Radial basis Function) neural network forecasting model is constructed for the mine cable life prediction problem. According to the mine cable in line with the characteristics of accelerated life test, the temperature and the dielectric loss factor are chosen as the model input. In order to solve the problem that the number of training samples is too small and detection samples cannot be constructed, the dielectric loss factor and aging time in each temperature segment collected are linearly interpolated to generate a large number of simulation data as training target vectors, and the RBF neural network is established for life prediction. Then the predicted life value is compared with the design life of the cable for verification. The result confirms that the RBF neural network model can reflect the relationship between the dielectric loss factor and residual life under a certain humidity and the different levels of temperature, which has certain practical significance for the prediction of cable insulation life.

RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.

An adaptive RBF neural network–based multi-objective optimization method for lightweight and crashworthiness design of cab floor rails using fuzzy subtractive clustering algorithm

To improve the computational cost and accuracy of approximate-based multi-objective optimization problems in engineering, an adaptive RBF neural network (ARBFNN) method integrating RBF neural network model, fuzzy subtractive clustering (FSC) sequence sampling method and non-dominated sorting genetic algorithm (NSGA-II) is proposed. To solve the problem that the number of new sample points is difficult to determine, FSC sequence sampling is proposed to select the clustering center points as newly added sample points. First, the ARBFNN method is verified by the five test functions. The results show that the ARBFNN method is significantly better than static RBFNN model based on multi-objective optimization (SRBFNN) in terms of global convergence and efficiency performance, and the other two adaptive approximate optimization methods in terms of overall efficiency. Based on the above, the accuracy and efficiency of ARBFNN method are very high. Finally, the method is applied to an engineering example: the lightweight and crashworthiness design of floor rails. The cab model coupled with implicit parameterized floor rails model is built using the SFE-CONCEPT software to achieve collaborative optimization design of shape-size-material. The optimization results show that the ARBFNN method can guarantee the error of the approximate optimal solution and the finite element solution (expensive solution) within 2%, so the accuracy of highly nonlinear (finite element analysis of collision conditions) approximate optimization is improved. Hence, the proposed ARBFNN method is feasible and effective in solving complex and expensive engineering optimization problems.

Research on Material Ordering and Transportation Issues Based on RBF Neural Network and 0-1 Planning Model

To select the best 50 from 402 suppliers, this article mainly establishes a principal component comprehensive evaluation model to formulate the five most important supplier service indicators. To reflect the impact of timeliness on the modeling results, time weights are introduced for dynamic weighing. The RBF neural network model is used to predict the supply and order situation in the next 24 weeks. According to its time-weighted average supply capacity, two forecast corrections are made to make the forecast results more reliable. Establish a 0-1 planning model to decide on the most economical ordering plan and the transfer plan with the least loss.

Differential evolution based radial basis function neural network model for reference evapotranspiration estimation

The present study is an effort to examine the capability of a differential evolution based radial basis function neural network (RBFDE) to model weekly reference evapotranspiration (ET0) as a function of climatic parameters in different agro-climatic zones (ACZs) of a moist sub-humid region in East-Central India. The ET0 computed using the empirical equation of Penman–Monteith suggested by the Food and Agricultural Organization (FAO56-PM) is considered as a target variable for investigation. The performance of the proposed RBFDE model is compared with particle swarm optimization based radial basis function (RBFPSO), radial basis function neural network (RBFNN), multilayer artificial neural network (MLANN) models and conventional empirical equations of Hargreaves, Turc, Open-Pan, and Blaney-Criddle. Weekly ET0 estimates that are obtained using RBFDE, RBFPSO, and RBFNN and MLANN are observed to be more consistent than equivalent empirical methods. For a critical analysis of simulation results, mean absolute percentage error (MAPE), root means square error (RMSE), determination coefficient (R2) and Nash–Sutcliffe efficiency factor (NSE) is computed. Low MAPE and RMSE values along with higher R2 and NSE close to 1, obtained with soft computing models exhibit that, soft computing models produce better estimates of ET0 than empirical methods. Among the soft computing models, RBFDE provides improved results as compared to RBFPSO, RBFNN, and MLANN models. This method can be extended for ET0 estimation in other ACZs.

Three‐Dimensional Force Prediction of a Flexible Tactile Sensor Based on Radial Basis Function Neural Networks

A flexible tactile sensor array with 6 × 6 N‐type sensitive elements made of conductive rubber is presented in this paper. The property and principle of the tactile sensor are analyzed in detail. Based on the piezoresistivity of conductive rubber, this paper takes full advantage of the nonlinear approximation ability of the radial basis function neural network (RBFNN) method to approach the high‐dimensional mapping relation between the resistance values of the N‐type sensitive element and the three‐dimensional (3D) force and to accomplish the accurate prediction of the magnitude of 3D force loaded on the sensor. In the prediction process, the k‐means algorithm and recursive least square (RLS) method are used to optimize the RBFNN, and the k‐fold cross‐validation method is conducted to build the training set and testing set to improve the prediction precision of the 3D force. The optimized RBFNN with different spreads is used to verify its influence on the performance of 3D force prediction, and the results indicate that the spread value plays a very important role in the prediction process. Then, sliding window technology is introduced to build the RBFNN model. Experimental results show that setting the size of the sliding window appropriately can effectively reduce the prediction error of the 3D force exerted on the sensor and improve the performance of the RBFNN predictor, which means that the sliding window technology is very feasible and valid in 3D force prediction for the flexible tactile sensor. All of the results indicate that the optimized RBFNN with high robustness can be well applied to the 3D force prediction research of the flexible tactile sensor.

Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada

Exploration for new mineral deposits has become increasingly difficult as new discoveries are being found under progressively deeper cover. To better understand and predict orogenic gold mineralization in the Archean Swayze greenstone belt, the essential ingredients of a mineral system are considered: 1) the source of gold and transport fluid ligands, 2) fluid pathways, 3) traps, and 4) the processes responsible for gold precipitation. The aim of this study is to use a mineral systems approach to help generate exploration targeting models using a spatial statistical method - weights of evidence (WofE) and data-driven machine learning tools, namely radial basis function neural networks (RBFNN) and support vector machine (SVM). The mineral prospectivity maps generated using the RBFNN and SVM machine learning methods were trained using the K-Fold cross-validation approach whereby 10 subsets of the data were used to train and test the model performance. The mean area under receiver operator curve after 10-fold cross-validations were 91% and 94% for the RBFNN models, and the SVM models obtained accuracies of 91% and 87%. Feature importance estimations obtained from both methods indicate that D2 and D3 high-strain zones, lithological contacts and D2 folds (i.e., synclines and anticlines) were found to be important predictor layers for targeting potential prospective zones of gold mineralization. The machine learning algorithms used in this study are novel and pragmatic methods that use the full potential of geoscience datasets in mapping orogenic gold prospectivity.

Robotic constant force grinding control based on grinding model and iterative algorithm

PurposeThe purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.Design/methodology/approachThe robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.FindingsThe experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.Originality/valueAs the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.

Flow pattern recognition in corrugated channels between circular plates

The path of the fluids in corrugated channels between circular plates is an arc of varying length, i.e. the flow direction firstly disperses and then converges. However, there is a shortage of data on the flow structures in these channels. In the present study, we experimentally investigate the flow patterns and pressure drop of two-phase flow in corrugated channels between circular platess. According to the observation, bubbly flow, slug flow, film flow and churn flow are sequentially defined. By analyzing the power spectral density of the flow patterns, the RBF neural network model is built for the flow pattern identification.

State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

Adaptive control of manipulator based on neural network

With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized.

Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

SummaryPath loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

Removal of hydrochlorothiazide from molecular liquids using carbon nanotubes: Radial basis function neural network modeling and culture algorithm optimization

The removal of hydrochlorothiazide (HCT) from molecular liquids on multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs) was studied in a batch system. In this present work, multiple linear regression (MLR) and radial basis function neural network (RBFNN) were utilized to forecast the adsorption removal percentage of HCT by both adsorbents. The influences of process variables on the removal efficiency were optimized by culture algorithm (CA) optimization. The results displayed the RBFNN was better than the MLR to simulate removal of HCT by two adsorbents. The optimal RBFNN model using the test dataset predicted HCT removal (%) with the coefficient of determination (R2) values of 0.8460 and 0.9438; mean squared error (MSE) values of 0.0117 and 0.0010, respectively for SWCNTs and MWCNTs. At the optimum value of parameters, the adsorption isotherms could be fitted well by the Langmuir model with adsorption capacity values of 66.225 mg g−1 for MWCNTs and 45.662 mg g−1 for SWCNTs. It was also found that the pseudo second-order and intraparticle diffusion models were more suitable for explaining the adsorption mechanism by CNTs.

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.

Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter.

Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00-28.50 °C), flow rate (272.16-768.96 m3/day), and influent COD (55.50-148.90 ppm). The results of the experimental study showed that the COD removal ratio had the highest value (65%) when two plastic biofilter media were used at the minimum flow rate (272.16 m3/day). The mathematical model results showed that the closeness between the measured and obtained COD removal ratios using the RBFNN indicates that the neural network model is valid and accurate. Additionally, the proposed RBFNN trained with the PSO method helped to reduce the difference between the measured and network outputs, leading to a very small relative error compared with that using the conventional RBFNN. The deviation error between the measured value and the output of the conventional RBFNN varied between + 0.20 and - 0.31. However, using PSO, the deviation error varied between + 0.058 and - 0.070. Consequently, the performance of the proposed PSO model is better than that of the conventional RBFNN model, and it is able to reduce the number of iterations and reach the optimum solution in a shorter time. Thus, the proposed PSO model performed well in predicting the removal ratio of COD to improve the drain water quality. Improving drain water quality could help in reducing the contamination of groundwater which could help in protecting water resources in countries suffering from water scarcity such as Egypt.

Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

A prediction model of milling force for aviation 7050 aluminum alloy based on improved RBF neural network

Semi-finishing is an important step to reduce the cutting vibration and deformation caused by the thin-walled stiffening rib parts of aeronautical inertial control products during the finishing process. In the semi-finishing process, the variation of cutting force is a key factor of machining deformation. A large number of samples considering the effect of cutting parameters, such as spindle speed, feed per tooth, and milling width were simulated by the finite element simulation software AdvantEdge to obtain the milling forces based on 7050 aluminum alloy (Al7050) for aviation. Moreover, the standardized Euclidean distance was introduced to the radial basis neural network model (RBF-NN) to develop an improved RBF neural network model (IRBF-NN), and a high-precision model to predict the effect of cutting parameters on the thin-walled semi-finishing milling forces was established based on the proposed IRBF-NN. Results show that the maximum relative errors of milling force obtained by the genetic algorithm–optimized BP neural network model (GA-BP-NN), RBF-NN, and IRBF-NN are 13.9%, 12.5%, and 4.1%, respectively. Accordingly, the proposed IRBF-NN has high accuracy and effectiveness to predict milling force for the semi-finishing of aerospace Al7050 thin-walled stiffening rib parts.