Radial Basis Function Neural Network Algorithm Research Articles

Research on Noise Reduction of Water Hydraulic Throttle Valve Based on RBF Neural Network and Multi-Island Genetic Algorithm

Excessive pressure drop within the internal flow channel of the water hydraulic throttle valve will generate severe noise. In order to reduce the noise of the throttle valve, in this paper, the model of the throttle valve was established, and the flow characteristics and acoustic characteristics of the valve were simulated. The simulation results showed that the parameters of the throat structure, such as the half angle, throat inlet angle and throat length, have a significant effect on the noise of the valve. Then, the three main structural parameters were used as optimization variables, and radial basis function (RBF) neural networks and multi-island genetic algorithms (MIGA) were used to reduce the noise of the valve. The approximate model of the relationship between the structural parameters of the valve and noise was established by RBF neural networks, and MIGA was used to optimize the approximate model. Finally, the optimal valve model was established based on the obtained optimal parameters, and its noise was analyzed through simulation and experiment. The research results indicated that the optimization method, which combines RBF Neural Network and MIGA, can effectively reduce the noise of hydraulic throttle valves.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, reviewstudies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determinationcoefficients (R,R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Origin Identification of Poria cocos Based on Terahertz Time-Domain Spectroscopic Techniques

Poria cocos is an important traditional Chinese medicinal material. The quality and efficacy of Poria cocos grown in different origin places are significantly different. The purpose of this study is to accurately identify the origin of Poria cocos using terahertz time-domain spectroscopy. The refractive index and the absorption coefficient spectra in the range of 0.4˜1.5 THz of Poria cocos samples from twelve provinces across China had similar change trends, with a little difference from the intensity. Combined with principal component analysis (PCA), the absorption coefficient spectrum was established to identify the origin of Poria cocos based on random forest (RF), radial basis function neural network (RBFNN) and convolutional neural network (CNN). The experimental results show that the RBFNN algorithm has the best identification effect among the 12 different origins of Poria, and the correct rate of the test set can be up to 98.2609%, which is higher than that of the RF algorithm (97.3913%) and the CNN algorithm (93.913%). Accurate identification of the origin of Poria cocos was realized with terahertz time-domain spectroscopy, providing a new idea and technical solution for the origin identification of Poria cocos.

Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing

Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC.

Fault-tolerant attitude tracking control for carrier-based aircraft using RBFNN-based adaptive second-order sliding mode control

This paper proposes a fault-tolerant attitude tracking control for carrier-based aircraft with external disturbances and actuator faults, which is based on backstepping control, second-order sliding mode control (SOSMC) and the radial basis function neural network (RBFNN) adaptation algorithm. First, the generic attitude dynamic model of carrier-based aircraft is presented. Based on backstepping method, the attitude control system is divided into two simple and low-order subsystems. Then, the basic SOSMC using the type of proportional integral (PI) sliding mode variable are designed respectively for two subsystems, which can guarantee the system stable with no chattering in the finite time. To compensate for disturbances and faults more accurately, we design RBFNN-based adaptive second-order sliding mode control (RASOSMC) where RBFNN algorithm is utilized to approximate the lumped perturbation, which attenuates the impact of disturbances and faults more effectively and improves attitude tracking performance. Furthermore, the adaptive online algorithms for the RBFNN weights are proposed based on Lyapunov theory. Finally, numerical simulation results are given to indicate the effectiveness and robustness of the SOSMC scheme and RASOSMC scheme.

Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.

Theoretical and experimental research on two-phase flow image reconstruction and flow pattern recognition.

Two-phase flow is a kind of complex fluid flow state, and the flow pattern characteristics are very difficult to obtain accurately. First, the principle of two-phase flow pattern image reconstruction based on electrical resistance tomography technology and the complex flow pattern recognition method are developed. Next, the back propagation (BP), wavelet, and radial basis function (RBF) neural networks are applied to the two-phase flow pattern image identification process. The results show that the RBF neural network algorithm has higher fidelity and faster convergence speed than the BP and wavelet network algorithms, and the fidelity is more than 80%. Then, deep learning of the pattern recognition algorithm fusing the RBF network and convolution neural network is proposed to improve the precision of the flow pattern identification. Additionally, the recognition accuracy of the fusion recognition algorithm is more than 97%. Finally, a two-phase flow test system is constructed, the test is finished, and the correctness of the theoretical simulation model is verified. The research process and results provide important theoretical guidance for the accurate acquisition of two-phase flow patterns.

Multi-objective optimization design in a centrifugal pump volute based on an RBF neural network and genetic algorithm

The hydraulic and acoustic performance of centrifugal pumps are interrelated and contradictory in hydraulic structure design. An optimization design method of a volute based on a radial basis function (RBF) neural network and genetic algorithm (GA) is proposed to solve this problem. The efficiency and total sound pressure level of the centrifugal pump are used as the optimization objectives. The diameter of the base circle, the width of the base circle, the installation angle of the volute tongue, and the height of the volute diffuser tube are used as the optimization variables. Latin hypercube sampling (LHS) is used to establish the sample space, the RBF neural network is used to build the agent model between the optimization variables and objectives, and the GA is used to multi-objective optimization. For the Pareto solution set obtained, two extremal individuals and initial individuals are selected for comparative analysis of hydraulic and acoustic performance under different working conditions. The result demonstrates that under the rated working condition, compared with the initial individual, the efficiency of the optimal individual of efficiency increases by 3.79%; the internal noise of the optimal individual of sound pressure level decreases by 5.5%, and the external noise decreases by 2.3%.

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.

Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network

The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.

Research on Internet Security Situation Awareness Prediction Technology Based on Improved RBF Neural Network Algorithm

With the increasing scale and complexity of the network, the network attack technology is also changing, such as malicious program attack, Trojan horse, distributed denial of service attack, worm, virus, web code injection, botnet, and other new network attack tools emerge in large numbers. As the core hotspot of network information security, network security situational awareness has received more and more attention. The traditional way of network security situational awareness prediction is relatively single. Usually, only one algorithm is used for perception and prediction, and its prediction accuracy is limited. To explore the application effect of intelligent learning algorithm, this study takes radial basis function (RBF) neural network as the main research object, optimizes RBF by simulated annealing (SA) algorithm and hybrid hierarchy genetic algorithm (HHGA), constructs RBF neural network prediction model based on SA–HHGA optimization, and carries out relevant experiments. The results show that the predicted situation value of the optimized RBF neural network in 15 samples is very close to the actual situation value. The neural network has good prediction effect and can provide assistance for the maintenance of network security.

Time-Domain Signal Reconstruction of Vehicle Interior Noise Based on Data-Driven Method

Background: In the process of high-speed driving, there are many source signals that affect the ear noise of passengers in the car. It is important to obtain the reference signal of Active Noise Control (ANC) of the vehicle at high speed condition. Objective: This paper introduces a method to study the time-domain signal reconstruction of interior noise based on data-driven method. Methods: Based on the noise signal collected in a car, the key point signals affecting the interior noise are determined by the acoustic transfer path analysis method. Considering the time-varying characteristics of the noise signal and the complex nonlinear relationship of interior noise, a noise reconstruction model based on wavelet decomposition Radial Basis Function (RBF) neural network is established. And the BP neural network noise reconstruction model is set up to compare the reconstruction effect. Results: According to the reconstruction comparison, the average absolute error (0.0072) of the proposed algorithm model is smaller than the average absolute error of the noise reconstruction BP network model based on wavelet decomposition (0.0280), and the accuracy is improved by 74.29%. The average absolute error between the reconstructed value of RBF neural network and the real-value is smaller than that of BP neural network, and the error of the proposed model is less than 0.01. The method proposed in this paper can reconstruct the interior noise signal of vehicle accurately and effectively. Conclusion: This paper proposes a reconstruction model of vehicle interior noise signal based on wavelet decomposition RBF neural network algorithm driven by data-driven, and verifies the effectiveness of the algorithm with the real vehicle test data. The reconstruction method of RBF neural network based on data-driven wavelet decomposition provides a certain reference value for ANC to obtain high-precision reference signal.

A Dynamic Neural Network Optimization Model for Heavy Metal Content Prediction in Farmland Soil

To improve the accuracy of soil heavy metal content prediction, this paper proposes a dynamic neural network optimization model (DNNOM). The model is based on a radial basis function neural network (RBFNN). The weights and bias of the output layer of the RBFNN were generated using the adaptive dynamic genetic optimization algorithm (ADGOA), and the center point of the hidden layer of the RBFNN was determined using an efficient density peak clustering algorithm (EDPC). An adaptive variance measure (AVM) was then used to generate the width vector of RBFNN hidden layer. The model was applied to the prediction soil heavy metal content in six new urban areas in Wuhan. Through comparison with support vector machine(SVM), light gradient boosting machine(LightGBM), RBFNN, and genetic algorithm optimizes the radial basis function neural network(GA-RBFNN), the experimental results demonstrate that the DNNOM is closer to the real value than the other four models, and the four error indicator values are also significantly lower than those of the other comparison models, which have higher prediction accuracy. Especially when compared with RBFNN, the MAPE and SMAPE of DNNOM have dropped by 3.98% and 3.9%, respectively.

Multi-objective parameter optimization of turbine impeller based on RBF neural network and NSGA-II genetic algorithm

In order to improve the efficiency of centrifugal pump as turbine(PAT), it is proposed to use Radial Basis Function (RBF) neural network combined with NSGA-II genetic algorithm to perform multi-objective optimization of the impeller of PAT. The Plackett–Burman screening test was used to screen out the geometric parameters of the turbine impeller that have a great impact on the performance of the turbine, and the Latin test design method was used to sample the selected significant influencing factors. The RBF neural network was used to fit the mapping relationship between the optimization variables and the optimization targets, and the NSGA-II genetic algorithm was used for multi-objective optimization. The results show that the efficiency and head of the optimized model are improved by 5.74% and 4.85% respectively compared with the original model.

Analyzing the Internet financial market risk management using data mining and deep learning methods

PurposeTo identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

Intelligent Bamboo Part Sorting System Design via Machine Vision

Abstract The defect rate of initially produced block bamboo (Bambusoideae) parts is >20 percent. Sorting out these defective parts manually is a highly time-consuming and tedious process. An intelligent sorting system was developed based on machine vision using a Radial Basis Function (RBF) neural network learning algorithm in this study. First, a high-speed charge-coupled device camera was used to obtain a series of images of perfect and defective block bamboo parts. Next, the RBF neural-network learning algorithm was applied to obtain defect characteristics and to locate defective parts moving forward on a conveyor belt. An array of air jets was designed to force defective parts off the belt. Experimental results showed that the average defective part removal rate of the proposed system was 91.7 percent.

Identification of Epileptic Electroencephalograms Signals Using an Integrated Transfer Radius Basis Function Neural Network

As a common brain disease, epilepsy is rapidly increasing in terms of the number of patients. Long-term repeated sudden seizures seriously affect the physical and mental health of patients. Epileptic electroencephalogram (EEG) signals are an effective tool in the hands of clinicians for diagnosing epilepsy, and how to use computer technology to automatically analyze and detect epileptic EEG signals has become very meaningful. This article proposes a method for effectively identifying epileptic EEGs for further diagnosis of epilepsy. The traditional modeling method default is to train on training samples and test samples that obey the same distribution, which usually does not match the actual situation. Therefore, a transfer learning (TL) mechanism is introduced to a classical radial basis function neural network (RBFNN). Considering the limited stability of a single classifier, this article introduces an integration strategy and proposes an integrated transfer RBFNN (ITRBFNN) algorithm. Experimental results of EEG signal recognition for epilepsy show that the algorithm has better adaptability of scene transfer and stability.

IOT and cloud computing based parallel implementation of optimized RBF neural network for loader automatic shift control

One of the key issues in automatic shift control of V-type cyclical loaders is determining how to find the best gear for the current conditions according to a certain mapping relation, but this complex and nonlinear mapping is difficult to express by a mathematical relation. However, to solve such nonlinear problems, a radial basis function (RBF) neural network is the best choice. In this paper, a certain type of wheel loader is taken as the research object, and an RBF neural network algorithm based on an improved genetic algorithm (GA) optimization is proposed. The global search ability of the GA is improved by adaptively adjusting the crossover probability and mutation probability. The RBF neural network expansion coefficient is optimized by an improved GA. Using industrial IOT technology, an optimized RBF neural network based on Map-Reduce on a cloud computing cluster is designed. The diesel engine computer and transmission computer on the loader are integrated to achieve dual-processor distributed parallel data processing and calculation. Then the loader automatic variable speed control algorithm model of improved GA optimized RBF neural network based on IOT cloud computing is established. The network model is trained and simulated using real vehicle automatic shift test data. The simulation results show that the improved GA-RBF neural network algorithm can achieve a correct recognition rate of 97.92%. The error matrix norm reaches the minimum value when the algorithm is iterated to the 17th generation. The improved algorithm has the advantages of a high gear recognition rate, fast convergence speed and strong real-time shift performance and is an effective new shift control method. The test results show that the shift boost time is less than 0.15 s and has a certain gradient. Compared with the manual shift process performed in the past, some improvements are achieved in the optimal shift time, shift response speed and shift quality. Compared with the traditional single computer based on serial training RBF neural network learning algorithm, whether it is Great progress has been made in convergence speed, training time, recognition rate, and data processing capabilities. Through the simulation and test, the validity of the intelligent shift control method of the improved GA optimized RBF neural network based on IOT cloud computing is verified. It has better engineering application value.

Research on fault tolerant control system based on optimized neural network algorithm

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, “stuck” fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19.