Basis Function Neural Network Algorithm Research Articles

Charging infrastructure facilitate a large-scale Introduction of electric vehicle in urban areas using hybrid technique: A RBFNN-SPOA approach

A hybrid technique is proposed for the effects of different roll-out techniques for charging the infrastructure that enables large-scale introduction of electric vehicles (EVs). The proposed hybrid technique is the combined execution of both Radial Basis Function Neural Network (RBFNN) and Student psychology optimization algorithm (SPOA), together called as RBFNN-SPOA strategy. The proposed method contains 3 kinds of agents operate in the environment from which charging stations is located. The communication among these agents are simulated in 3 ways. They are (1) charging process from which the EV driver cooperates with available charging stations and other electric vehicle drivers, (2) the process of purchasing a vehicle of non-electric vehicle car owners from which they take current charging infrastructure utilization into account, (3) the installment process of new charging stations through charging point operator (CPO) is based on placement tactic depends on charging station (CS) utilization. Firstly, a larger dataset on real charging patterns are utilized to design the charging behaviour of agents by RBFNN. Secondly, the proposed approach contains significantly more communication among electric vehicle drivers compared to existing approaches and also more precisely specifies the difficult system of on-street electric vehicle charging at an urban context. Thirdly, the relation among charging infrastructure and electric vehicle adoption based on experimental choice, while the existing methods provides assumptions by using SPOA. Finally, the proposed approach is implemented in MATLAB or Simulink platform and its performance is compared with existing approaches.

Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm

Abstract. This paper will study a trajectory tracking control algorithm for electric vehicles based on a terminal sliding mode controller. First, a 3 degrees of freedom nonlinear vehicle model and a controller-oriented 2 degrees of freedom vehicle model are established. The preview time is adaptively adjusted based on the preview model. Then, the vehicle trajectory tracking controller, which uses the terminal sliding mode algorithm, is designed. The radial basis function (RBF) neural network algorithm is used to approximate the system variable parameters in the control model online. At the same time, fuzzy logic is used to control the gain parameters of the controller to reduce the chattering of the control system. Finally, the designed controller is verified by simulation. The maximum deviation of path tracking under different speeds is 0.6 m, and the target path can also be well followed under different road friction coefficients. The simulation results show that the controller designed in this paper can effectively carry out the vehicle trajectory tracking and lateral control and reduce the chattering to a certain extent.

Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm.

Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.

Smart energy management for optimal economic operation in grid-connected hybrid power system

ABSTRACT This manuscript presents a hybrid method for smart energy management (EM) in grid connected microgrid (MG) system. The grid connected micro grid system consists of photovoltaic (PV), wind turbine (WT), micro turbine (MT), and battery. The proposed method is the combined execution of Radial Basis Function Neural Network (RBFNN) and Squirrel Search Algorithm (SSA), hence it is called RBFNN-SSA method. Here, the necessary load demand of grid-connected MG system is constantly monitored by AI strategy. SSA has developed the perfect combination of MG considering the forecast load demand. The major intention of the RBFNN-SSA method is fuel cost involvement, grid power hourly power variation, operation with maintenance cost of grid connected micro grid system. The constraints are the accessibility of renewable energy sources (RES), power requirement and state of charge (SoC) of storage elements. Batteries are used as an energy source, to stabilize and allow the renewable power system units for maintaining constant output power. The proposed method is activated in MATLAB/Simulink working site. Then, the efficiency is assessed with existing methods such as improved artificial bee colony (IABC), bacterial foraging optimizer and artificial neural network (BFOANN), ant lion optimizer (ALO), grasshopper optimization algorithm with particle swarm optimization including artificial neural network (GOAPSNN). The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE) of proposed and existing methods under 50 and 100 count of trails are also analyzed. The proposed technique achieves the RMSE is 9.3, MAPE is 4.2, and MBE is 2 for 50 number of trails. For 100 count of trails, the proposed method achieves the RMSE is 13.5, MAPE is 3.9 and MBE is 5.7. The mean, median and standard deviation of RBFNN-SSA method achieves 0.9681, 0.9062 and 0.1099. The elapsed time of RBFNN-SSA method attains 30.15 s.

Research on Improved Shallow Neural Network Big Data Processing Model based on Gaussian Function

The application of the current new generation communication technology is gradually diversified, and the global Internet users are increasing, leading some large enterprises to increasingly rely on faster and more efficient big data processing technology. In order to solve the shortcomings of the current big data processing algorithms, such as slow computing speed, computing accuracy to be improved, and poor online real-time learning ability, this research combines incremental learning and sliding window ideas to design two improved radial basis function (RBF) neural network algorithms with Gaussian function as the kernel function. The Duffing equation example and the data of "Top 100 single products for Taobao search glasses sales" were used to verify the performance of the design algorithm. The experimental results of Duffing equation example show that when the total sample is 100000, the mean square errors of IOL, SWOL, SVM and ResNet50 algorithms are 1.86e-07, 1.59e-07, 3.37e-07 and 2.67e-07 respectively. The experimental results of the data set of "Top 100 SKUs for Taobao Search Glasses Sales" show that when the number of samples in the test set is 800, the root mean square errors of IOL, SWOL, SVM and ResNet50 algorithms are 0.0060, 0.0056, 0.0069 and 0.0073 respectively. This shows that the RBF online learning algorithm designed in this study, which integrates sliding windows, has a stronger comprehensive ability to process big data, and has certain application value for improving the accuracy of online data based commodity recommendation in e-commerce and other industries.

Development of Trees Management System Using Radial Basis Function Neural Network for Rain Forecast

Agriculture and farming are mainly dependent on weather especially in Malaysia as it received heavy rainfall throughout the years. An efficient crop or tree management system with a weather forecast needed for suitable planning of farming operation. Radial Basis Function Neural Network (RBFNN) algorithm was used in this study to predict rainfall and the main focus of this study is to analyze the factor that affects the performance of neural model. This study found that the model works better the more hidden nodes and the optimum learning rate is 0.01 with the RMSE 49% and the percentage accuracy is 57%. Besides that, it is found that the meteorology data also affect the model performance. Future research can be conducted to improve the rainfall forecast of this study and improve the tree management system.

Prediction of air voids of asphalt layers by intelligent algorithm

The objective of the study was to give a suitable prediction method of air voids of asphalt layers in the process of construction. Seven different methods are utilized to predict the air voids of asphalt layers: nonlinear data fitting, Back Propagation neural network (BPNN) algorithm, Radial Basis Function neural network (RBFNN) algorithm, support vector machine for regression (SVR), Gaussian process regression (GPR), regression trees, and random forest regression. The results of laboratory experiments and field tests showed that the intelligent algorithm of SVR is more accurate and suitable for estimating the air voids of asphalt layers. The compaction quality of asphalt layers can be evaluated by this proposed new prediction method of air voids.

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 Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization.

Objective The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS). Methods 551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis. Result The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) (P > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences (P > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours (P > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) (P > 0.05). Conclusion The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.

Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm

Track and field events are the foundation of sports events. To learn other sports events well, you must also have a certain grasp of track and field events. As the movements of each track and field event become more and more complex, the competition becomes more and more intense, and the technical requirements are getting higher and higher. Therefore, sports injuries are gradually increasing. The prevention of sports injuries is a hot spot in sports medicine. Studies have found that as many as twenty factors can directly or indirectly cause track and field sports injuries. Based on the collected data on the injury-causing factors of elite athletes’ track and field sports, and on the basis of in-depth research on mobile data processing technology and early warning risks, a mobile early warning system and radial system using sports injury risk early warning algorithms, functional design and architecture are proposed. The basis function (RBF) neural network algorithm is released. Research on Early Warning Model of Sports Injury. This article analyzes and evaluates different hazard factors through the method of literature data, expert review and extensive analysis. In the investigation, the various causes of the injury were analyzed in detail, and it was found that most sports injuries were caused by students’ lack of attention, technical errors, insufficient preparation activities, etc., and finally proposed countermeasures to solve the problem, so that the sports injury situation was obtained improve.

Optimal design of container heat dissipation safety based on five-block method and radial basis function

The ability of the ocean to provide a good thermal environment and not take up land space resources has led to subsea data centres due to their energy efficiency, low failure rates and green sustainability. As a result, there is a growing need for subsea data centres to be structurally sound. This paper investigates the optimal design of data centre capacity and cooling structures to improve their thermal safety performance to a certain extent.Firstly, the temperature quantification of the heat dissipation and heat generation processes is carried out using Newton’s cooling theorem and Fourier’s law of thermal conductivity to provide maximum and minimum constraints for optimization. Then, the " five-block method" is used to optimize the arrangement of internal servers with temperature constraints, considering the problem of packing same-sized items in closed spaces. Based on the quantification of the solid-liquid and solid-solid heat transfer processes and the local temperature analysis as well as the optimization of the boxing, the model yields a maximum of 384 servers for the data center.Secondly, the fin structure is then further optimized for heat dissipation. An approximate model of the fin structure and thermal performance is developed and solved by a radial basis function (RBF) neural network algorithm. Then, based on 100 sets of parameters for the serrated fin structure, the RBF approximation model is used to optimize the range of values for height, pitch and thickness. Based on the three ranges of parameters, Monte Carlo simulations are used to generate different combinations of parameters for different shapes, and to obtain the exact parameter that will increase the surface area by 13.8%. In the case of the first task, a solution is obtained to store 396 servers.

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm.

Urban landscape planning and design is not only closely related to people's living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent cases, low reuse rate of data, discrepancy between design scheme and actual situation, and serious shortage of relevant professionals. The artificial neural network can give corresponding ways to improve and solve these problems. Therefore, this paper proposes a research on garden planning and design based on multipopulation coevolution particle swarm radial basis function neural network algorithm. Based on multipopulation coevolution particle swarm radial basis function neural network algorithm, the error between the predicted evaluation value and the actual evaluation value in the simulation experiment is less than 5%, which shows good accuracy and generalization ability in performance. And in the plant configuration simulation experiment, it can effectively evaluate the urban planning and design and put forward the corresponding adjustment scheme according to the analysis results, which is more in line with the actual needs of urban planning.

Application Analysis of Radial Basis Function Neural Network Algorithm of Genetic Algorithm for Environmental Restoration and Treatment Effect Evaluation of Decommissioned Uranium Tailings Ponds.

A new analysis method for the environmental stability of uranium tailing ponds is established in this paper, and the stability intervals and environmental stability rates of indicators are defined in precise mathematical language and analyzed with examples. The results show that the overall environmental stability of this uranium tailings pond is still in a poor state after the first phase of decommissioning treatment, and special decommissioning treatment should be carried out for factors such as pH and radionuclides Po and Pb. Using the powerful nonlinear mapping function of the artificial neural network, a radial basis function neural network algorithm was constructed to predict the environmental stability of the uranium tailing pond. It provides a new feasible method for the comprehensive evaluation technology of uranium tailings ponds. Accuracy in DOA Estimation. The research work in this paper mainly analyzed the environmental stabilization process and stability of decommissioned uranium tailings ponds, proposed a new concept of environmental stability with ecological and environmental protection concepts and gave it a new connotation, established an environmental stability evaluation index system for decommissioned uranium tailings ponds through index screening by using rough set theory, comprehensively considered the influence of environmental factors such as external wastewater and exhaust gas, and realized the multifactor. The system of evaluation indexes for the stability of decommissioned uranium tailings ponds was established by combining multiple factors, and the long-term monitoring and modeling of the environmental stabilization process of decommissioned uranium tailings ponds was carried out by using mathematical methods. The results show that the RBFNN-GA algorithm can reduce the training error of the random radial basis function neural network, improve the generalization ability of the network, and make it capable of handling large data sets.

Proposal of a novel GPU-accelerated lifetime optimization method for onshore wind turbine dampers under real wind distribution

In recent years, the criticality of fore-aft vibrations induced by winds on wind turbine towers has increased; these vibrations are generally evaluated using equivalent fatigue load (EFL). This is the first study to adopt wind speed histories from large eddy simulations as input for dynamic analysis of wind turbines, and to evaluate EFL against real wind distribution. Tuned mass damper (TMD) and rotational inertial double tuned mass damper (RIDTMD) were employed to control these vibrations. Parametric analysis of the damper parameters was conducted. An innovative global optimization tool was developed based on a radial basis function neural network and genetic algorithm. Moreover, for the first time, GPU acceleration technologies were adopted to enable the optimizations of dampers through massive cases. Numerical results show that damper optimizations under real wind distributions are essential, and that optimized dampers reduce 44% EFL. The performance of RIDTMD is better than TMD but has a narrower system control bandwidth. The optimized dampers are significantly affected by wind speed; however, they are least affected by wind direction. The developed GPU-based codes can run 2001 times faster than the CPU-based ones, and the optimization tool can further reduce 85% computational time, which is open to other researchers.

Neural tracking control of surface vehicles with lumped uncertainties

In this article, an adaptive radial basis function neural network scheme for trajectory tracking control of surface vehicles is proposed. Under complex uncertainties, the proposed controller is designed by combining radial basis function neural network and finite-time control algorithm. Using the novel controller, the stability of accurate trajectory tracking can be ensured and the robustness of control system can be improved. Theoretical proof is proposed by Lyapunov function that the radial basis function neural network controller can make surface vehicle to accurately track desire trajectory steadily. Simulation studies conducted on a prototype CyberShip II demonstrate remarkable performance of proposed control scheme.

Quantitative analysis of soybean protein content by terahertz spectroscopy and chemometrics

The protein content in soybeans is one of the important indicators to measure the quality of soybeans. This paper studied the feasibility of terahertz (THz) spectroscopy and chemometrics for quantitative detection of protein content in soybeans. Firstly, the THz absorption spectra of samples were processed by eight pre-processing. Secondly, through the calibration set, partial least squares regression (PLS), principal component analysis-radial basis function neural network (PCA-RBFNN) and artificial bee colony algorithm support vector regression (ABC-SVR) were used to establish quantitative detection models of soybean protein. Samples from the prediction set were used to verify the models. Finally, the related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP) and relative standard deviation (RSD) of ABC-SVR model were respectively 0.9659, 1.3085% and 3.5334%. The experimental results show that after proper pre-processing, THz spectroscopy and chemometrics can be used to quickly detect the protein content in soybeans.

Optimization of wind turbine TMD under real wind distribution countering wake effects using GPU acceleration and machine learning technologies

Excessive fore-aft vibrations of wind turbine tower are the major reason for tower collapsing, which can be evaluated using the equivalent fatigue load (EFL). Wake effects are generated by the former wind turbines on the latter ones, and can greatly increase EFL and reduce lifetime of the latter ones, but they were seldom considered. Consequently, this study calculated EFL countering the wake effects under real wind distributions. The effects of wind turbine spacing on EFL indicate that the wake effects between wind turbines with spacing of approximately 2 times of rotor radius should be considered. Subsequently, tuned mass damper (TMD) was placed in the nacelle to passively control the fore-aft vibrations. An optimization tool for TMDs was developed based on the radial basis function neural network and genetic algorithm, which was compared with the theoretical equations and parametric analysis. GPU acceleration technology was utilized. Numerical results show that, under real wind distributions, the TMDs obtain at least 40.1% EFL reduction, which is 8.9% higher than the theoretically optimized one. Importantly, the GPU-based codes can run 2001 times faster than the CPU-based ones, and the optimization tool can reduce 44.9% computational time further. The codes are made available for other researchers.