RBF Neural Network Research Articles

Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network

This paper solves the practical consensus tracking control problem for networked Euler–Lagrange (EL) systems using the uncertainty and disturbance estimator (UDE) in combination with the radial basis function (RBF) neural network. An integrated consensus algorithm is proposed for the networked EL systems, where the RBF neural network is first utilized to generate online estimation of the uncertain EL dynamics for enhancing the system adaptability, and then the UDE is applied to compensate external disturbances dynamically for improving the system robustness. Furthermore, a unified analysis framework for the closed-loop control system is established through performing the proper filter design and Lyapunov-like analysis. The proposed consensus tracking algorithm possesses a simple system structure for implementation and has a good robust performance in dealing with both uncertainties and disturbances. Numerical simulations are used to verify the effectiveness of the developed integrated consensus scheme.

Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.

An RBF neural network–based parameter tuning for an ADRC regulator of electrode wire feed mechanism: arc welding applications

An RBF neural network–based parameter tuning for an ADRC regulator of electrode wire feed mechanism: arc welding applications

The Application and Research of Particle Swarm Optimization Radial Basis Function Network Driven by Edge Computing in Bogie Fault Diagnosis

The bogie system is a key system that affects the safety and quality of high-speed train operation. Constructing a fault diagnosis model for the bogie system can effectively improve the safety and comfort of high-speed train operation. The traditional modeling method uses the BP neural network to fit the temperature and other parameters of the bogie system. However, because the BP neural network is prone to fall into local minima, slow convergence and poor diagnostic accuracy, this paper proposes a radial basis function network bogie fault diagnosis model based on particle swarm optimization based on edge computing. The model combines edge computing and particle swarm optimization algorithm to improve the accuracy and real-time of bogie fault diagnosis. By implementing model training and inference on edge devices, data transmission latency has been reduced and diagnostic efficiency has been improved. A particle radial basis neural network (PSRB) was designed using a highly convergent particle swarm optimization algorithm, and the parameters of the radial basis neural network (RBF Neural Networks) were optimized using the Particle Swarm Optimization algorithm. Based on the complexity of the input parameters of the bogie system, determine the input and output parameters of the model, and use particle swarm optimization algorithm to search for the optimal values of the basis function center, width, and output layer weight threshold of the RBF neural network. The composite algorithm was applied to the fault diagnosis of the bogie system, and a particle radial basis neural network bogie fault diagnosis model was designed. The simulation and experimental results of the model show that the diagnostic model can effectively improve the identification accuracy of fault diagnosis, with a minimum error accuracy of 0.0255, saving computation time. The computation time is reduced to 2.9 seconds, eliminating the influence of non target parameters on the inversion results. This model can also be used in other systems of high-speed trains and has practical application value.

Analysis and prediction of compressive and split-tensile strength of secondary steel fiber reinforced concrete based on RBF fuzzy neural network model.

Accurate analysis of the strength of steel-fiber-reinforced concrete (SFRC) is important for ensuring construction quality and safety. Cube compression and splitting tensile tests of steel fiber with different varieties, lengths, and dosages were performed, and the effects of different varieties, lengths, and dosages on the compressive and splitting properties of secondary concrete were obtained. It was determined that the compression and splitting strengths of concrete could be effectively improved by the addition of end-hooked and milled steel fibers. The compressive and splitting strengths of concrete can be enhanced by increasing the fiber length and content. However, concrete also exhibits obvious uncertainty owing to the comprehensive influence of steel fiber variety, fiber length, and fiber content. In order to solve this engineering uncertainty, the traditional RBF neural network is improved by using central value and weight learning strategy especially. On this basis, the RBF fuzzy neural network prediction model of the strength of secondary steel fiber-reinforced concrete was innovatively established with the type, length and content of steel fiber as input information and the compressive strength and splitting tensile strength as output information. In order to further verify the engineering reliability of the prediction model, the compressive strength and splitting tensile strength of steel fiber reinforced concrete with rock anchor beams are predicted by the prediction model. The results show that the convergence rate of the prediction model is increased by 15%, and the error between the predicted value and the measured value is less than 10%, which is more efficient and accurate than the traditional one. Additionally, the improved model algorithm is efficient and reasonable, providing technical support for the safe construction of large-volume steel fiber concrete projects, such as rock anchor beams. The fuzzy random method can also be applied to similar engineering fields.

Fuzzy RBF neural network fault diagnosis method based on knowledge and data fusion for the recoil system

To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and can effectively identify faults in recoil system.

Structure optimization of DTH hammer piston based on RBF neural network and WSO algorithm

To address the issue of piston fracture due to insufficient strength in the Down-The-Hole (DTH) hammer. The effect of the pressure state of the gas chamber on the piston motion is considered. A piston motion model is established based on the steady flow energy equation and the working principle of the piston. The finite element method is used to analyze the dynamic response of the piston during the impact of the drill bit. RBF neural network is employed to construct a surrogate model for the relationship between piston structural parameters (front-end diameter, front-end length, and center bore) and piston strength and performance (impact energy, frequency, and gas consumption). By combining this model with the white shark optimization search algorithm, the design of the DTH hammer’s piston structure parameters is optimized. The minimum piston mass is the objective function. Impact energy, frequency, air consumption and stress are constraint factors. The effect of piston life is considered while meeting rock drilling efficiency. The optimization results show that the stresses in the optimized piston structure are reduced by 13%. The proposed RBF performance prediction model combined with the optimal search algorithm can significantly improve the optimization efficiency.

Entropy generation analysis on condensation upward flow for hydrocarbon mixture in two types of helical wavy pipes

This paper proposed the application of two types of wavy structures for spiral wound heat exchanger, and conducted numerical simulations on the condensation flow of a hydrocarbon mixture with methane, ethane, and propane in them. The effects of saturation pressure and mass velocity are also studied. It was found that both types of helical wavy pipes produce higher condensation heat transfer coefficients and frictional pressure drop than those in helical smooth one, and both of them increase with the rise in mass velocity and the decrease of saturation pressure. The influence of saturation pressure on heat transfer and pressure drop is greater than that of mass velocity. Meanwhile, the comprehensive performance of the two types of helical wavy pipes is analyzed using entropy generation. It can be found that the horizontal wavy pipe has a better overall performance than vertical wavy one with a lower irreversible loss. Finally, prediction models for the condensation heat transfer coefficients and frictional pressure drops are established using RBF neural networks, with 95 % of the predicted results having a relative deviation within 10 %.

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

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

A Study on the Impact of Corporate Financial Accounting Management System on Corporate Innovation Under Sustainable Development Strategy

In the complex and changeable macro-economy, the sustainable development of enterprises is closely related to their ability to resist risks. To analyze the influencing factors of listed enterprises’ anti risk ability, this study constructs an indicator system of influencing variables through principal component analysis. The financial risk early warning method based on improved RBF neural network is constructed. Afterwards, the relevance of the indicator system was confirmed by the Kaiser-Meyer–Olkin (KMO) test. And in simulation experiments, it compared the classification performance of the BP neural network and the improved RBF model combined with the clustering algorithm. In the training dataset, the classification accuracy of the BP neural network was 79.6% while that of the improved RBF model was as high as 93.6%. In the 30 test datasets, the BP neural network appeared to have seven false positives. In the 30 test datasets, the BP neural network produced seven false judgments, while the proposed method only had three judgment errors. Several experiments showed that the BGD-RBF financial risk early warning model effectively maintained a high performance in the classification accuracy of enterprise financial status. It is more reliable than the traditional BP neural network method.

Retracted: Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Retracted: Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1–2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks

In underground space technology, the issue of tunnel stability is a fundamental concern that significantly causes catastrophe. Owing to sedimentation and deposition processes, the strengths of clays are anisotropic, where the magnitudes of undrained shear strengths in the vertical and horizontal directions are different. The anisotropic undrained shear (AUS) model is effective at considering the anisotropy of clayey soils when analyzing geotechnical stability issues. This study aims to assess the stability of rectangular tunnels by adjusting the dimensionless overburden factor, cover-depth ratio, and width-depth ratio in clay with various anisotropic strength ratios. The stability analysis of these tunnels involves employing finite element limit analysis and the AUS model to identify the planes of soil collapse in response to the aforementioned variations. In addition, this study presents the development of soft-computing models utilizing artificial neural networks (ANNs) to forecast the stability of rectangular tunnels across various combinations of input parameters. The findings of this study are presented in the form of design charts, tables, and soft-computing models to facilitate practical applications.

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues.

Enhanced Water Quality Prediction in the Yellow River Basin: Application of the SSA-RBF Model

Watershed water quality monitoring is of great significance in water environment protection and management, and this paper proposes a water quality prediction model based on RBF neural network. Aiming at the parameter optimisation of RBF water quality prediction model, we propose to apply the sparrow search algorithm to optimise the parameters of the RBF model, aiming to improve the global search ability and convergence speed of the model. The characteristics of water quality parameters in the Shaanxi section of the Yellow River Basin were analysed using the model. Comparison with the RBF prediction algorithm is made, and the SSA-RBF prediction algorithm can significantly improve the prediction performance of the RBF model.

An Analysis of the Values of Project-Based Learning in English Language Teaching and the Cultivating Functions it Displays

Abstract In this paper, an English teaching cultivation evaluation model based on RBF neural network is designed to solve the problems of poor generalization ability, low evaluation accuracy, and correlation coefficient of the current teaching evaluation model. Before establishing the evaluation model, conditional information entropy is used to screen the evaluation indexes of the English teaching cultivation function, and six dimensions of core literacy and five dimensions of thinking ability are selected as evaluation indexes. A new relative merit matrix is constructed under the constraint of relative merit by using the screened conditional attributes. Selection, crossover, and mutation operations are used in the RBF neural network to select the optimal parameters through the introduction of the genetic algorithm. Finally, the assessment data of students after project-based English teaching are analyzed for values and nurturing functions using the evaluation model. According to the findings, project-based English teaching led to the highest average score of 29.15 for students’ values compared to the dimensions of interest, ability, and personality. In terms of the six dimensions of core literacy and the five dimensions of thinking ability, the score difference between the experimental class and the control class is more than 1.2 points, and the Sig value of each dimension is less than 0.05, which indicates that English teaching based on Project-Based Learning promotes students’ values, core literacy and thinking ability.

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

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

Research on Modeling of Influencing Factors of New Eco-tourism Management Based on Economic Fluctuation Prediction

This paper begins by analyzing the effect of market economic fluctuations on the digital economy's tourism economy. Second, this paper uses a city cluster in the Yangtze River Delta in China as an example, selects the tourism economic data of the region from 2011 to 2021, and forecasts the tourism development potential of the region from 2022 to 2026 using the conventional methods for predicting economic fluctuations. However, conventional models for predicting economic fluctuations, such as ARIMA, have large prediction errors. This paper combines the RBF neural network and exponential smoothing method and compares them to GM(1, 1), ARIMA, and other models. The findings indicate that the tourism economy in this region is spatially distinct and relatively stable. Therefore, the methodology presented in this paper is capable of accurately predicting tourism demand and growth potential.