Traditional RBF Neural Network Research Articles

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.

Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4

Water inrush is one of the most frequent and catastrophic hazards in tunnel engineering, and poses serious threats to the safety of engineering and personnel. This paper presents a case study of a water inrush and ground collapse in the Qingdao Metro Line 4, which caused a cave-in with the diameter and depth of about 30 m and 6 m, respectively. Based on the field data and numerical modelling, the causes of the disaster were analyzed. A numerical model was used to analyze the changes of surface settlement, vault settlement and water pressure during the tunnel excavation. The results of the study indicate that the cause of this disaster was the failure of the tunnel vault surrounding rock caused by the weakening of the tunnel surrounding rock and water pressure, which in turn triggered the water inrush in the tunnel and caused a large volume of surface collapse. As the tunnel was excavated from the slightly weathered area to the strongly weathered area, the vault settlement increased, and the influence zone expanded towards the surface due to the continuous decrease in the strength of the surrounding rock. In particular, a negative pore water pressure zone was formed in a certain area around the tunnel during the water inrush. The negative pressure zone caused the surrounding groundwater to converge here, leading to an increase in the amount of water inflow, which also increased the scope and scale of the impact of this disaster. A risk assessment method for water inrush in tunnels is proposed. According to the geological and engineering characteristics of Qingdao area, the evaluation index system of tunnel water inrush risk was established. An RBF neural network was improved by gray correlation analysis and a PAM clustering algorithm to establish the tunnel water inrush risk assessment model. Comparing the evaluation data with the actual data, the prediction data of a traditional RBF neural network and a BP neural network, the accuracy and reliability of the model were verified. This study has value in reducing the occurrence of water inrush in a composite formation tunnel.

Neural network based multi-dimensional and nonlinear landscape design

In order to improve the effect of landscape design, based on the traditional multi-dimensional nonlinear landscape design and RBF neural network, this paper proposes and designs a multi-dimensional nonlinear landscape design method based on neural network. Firstly, the camera parameters are set, the landscape images are collected by UAV, and the collected landscape images are segmented. Landscape image features are extracted according to different classification criteria, and the feature information is used as training samples to train the neural network. Finally, the landscape design parameters are fitted and the results of the landscape design model are output. The experimental results show that the proposed method has better classification accuracy than the other two traditional landscape image classification algorithms. In different experiments, the landscape image classification accuracy of this method is kept above 85%, while the other two methods are lower. In addition, the regression analysis value and test value of this method also perform well. Finally, given a noisy image, it is found that the text method can effectively remove the noise in the landscape design image, making the image present a clearer landscape layout.

Research on Hydraulic Servo Valve Control Based on Fuzzy RBF

Aiming at the problems of low control precision and slow response speed of traditional control of actuators in hydraulic systems, the mathematical model of the valve-controlled cylinder is established as the research object, and the control strategy of fuzzy RBF neural network is proposed to control hydraulic servo valves On the basis of traditional fuzzy control, fuzzy control and RBF neural network control are integrated. According to the mathematical model of the system, a tracking controller is designed to realize precise control and real-time feedback of the valve-controlled cylinder system. The simulation results show that compared with traditional PID and fuzzy PID systems, the fuzzy RBF neural network strategy has high control precision, fast response speed, better robustness, and stability.

Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network.

Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al2O3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified.

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

In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks. Based on RBF, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund. RBF neural network is used to build the risk warning model of social security fund operation. Then, put forward the corresponding risk treatment scheme to the warning signal. Finally, the RBF neural network is used for comprehensive risk warning. In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal. This paper uses an improved ant colony algorithm to optimize the parameters of the RBF neural network, which overcomes the shortcomings of the traditional RBF neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the RBF neural network. It has the characteristics of good output stability and fast convergence speed. On this basis, the prediction model based on the improved ANT colony-RBF neural network is established, and the MATLAB software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers. The results show that the network has small calculation error, fast convergence, and good generalization ability.

RETRACTED ARTICLE: Mountain rainfall estimation and online English teaching evaluation based on RBF neural network

In order to solve the problem of the main parameters setting of traditional RBF neural network learning algorithm and the lack of local information based on space, this paper proposes a particle swarm hybrid optimization RBF neural network prediction algorithm based on genetic operation, which solves the early problems of PSO on the basis of the cross and mutation fusion of PSO and GA advantages. At the same time, we analyze the limitations of using the mountain rainfall simulation process and taking the mountain rainfall estimation as the model input, and put forward the idea of using the radar estimation technology from the perspective of spatial rainfall, and carrying out the mountain rainfall evaluation and simulation according to the spatial rainfall. The application of flood forecasting focuses on the research of mountain rainfall estimation simulation method based on radar precipitation estimation technology, and it is also the combination of mountain rainfall simulation process. Finally, this paper evaluates and analyzes online English teaching, and the new network environment has become the carrier of College English learning. In the new network environment, students will form the awareness of reading, listening, and speaking English, and conduct conscious online English learning guidance and enhance their language ability to find their own understanding and perception of English. In this paper, we reveal the problems and shortcomings of the current college English education evaluation in China, put forward various new intelligence evaluation theories, and try to explore the practical functions of several education evaluation systems in education, so as to better use the means of education evaluation to improve the efficiency of College English teaching. According to the research of the RBF neural network, it is applied to rainfall estimation and teaching evaluation, which can effectively improve rainfall estimation and teaching evaluation.

Improved RBF neural network algorithm in financial time series prediction

The financial market is often unpredictable and extremely susceptible to political, economic and other factors. How to achieve accurate predictions of financial time series is very important for scientific research and financial enterprise management. Based on this, this article takes the application of the improved RBF neural network(NN) algorithm in financial time series forecasting as the research object, and explores how to use the improved RBF NN algorithm to predict the stock market price, with a view to reducing investment risks and increasing returns for the majority of stock investors to provide help. This article uses the stock market prices of three listed companies in May 2019 as the data samples for this survey, including 72 training sample data and 21 test sample data. These three stocks were predicted by using the improved RBF NN algorithm Experiments, the experimental results show that the prediction errors of the improved RBF NN algorithm for the three stocks are 2.14%, 0.69% and 1.47%, while the traditional RBF NN algorithm’s prediction errors for the stocks are 5.74%, 2.38% and 11.37%. This shows that the improved algorithm is significantly more accurate and more effective than traditional algorithms. Therefore, the application of the improved RBF NN algorithm in financial time series prediction will be more extensive in the future.

Robot learning system based on dynamic movement primitives and neural network

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.

Research and Analysis of Nonlinear Model Identification Control Algorithm Based on Improved Neural RBF For Short Term Heat Load Forecasting of Heat Supply Network

Aiming at the mismatch between heat supply and demand of heating system, a nonlinear model identification control algorithm based on improved neural network for short-term heat load prediction of heat supply network is proposed by using the characteristics that heat load and temperature of heating system will not change dramatically in a short period of time By using MATLAB simulation, short-term heat load rolling prediction is realized. From the experimental results, this algorithm is better than the traditional RBF neural network in the prediction accuracy, and can accurately predict the trend of heat load.

Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO).

This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.

Study on power consumption load forecast based on K-means clustering and FCM–BP model

With the development of smart power distribution networks, the demand of smart grid user-side management is increasingly urgent. To improve the accuracy of short-term electric load forecasting for individual users, this study proposes a short-term power load forecasting model based on K-means and FCM–BP. Firstly, by analyzing the users’ electricity consumption features, K-means is applied to group users into two clusters. Secondly, for users with strong correlation at adjacent moments, local similar data is filtered out with the help of improved Fuzzy C-Mean clustering (FCM), integrating the load value of the adjacent moments into new input features. For users with weak correlation at adjacent moments, the local similar daily data are utilized as features. Finally, the feather vectors are used as input data for BP Neural Network, which is utilized to forecast the short-term load. Experimental results show that the clustering method proposed concurs with the characteristics of users’ electricity consumption behavior. With the same forecasting method, the accuracy of clustering-based load forecasting is higher than that of non-clustering load forecasting. Compared with traditional BP, RBF and GRNN Neural Network, this model has higher forecasting accuracy.

Research on Inverse Kinematics Algorithm of 6-DOF Industrial Robot Based on RBF-PID

To solve the question of multi value and precision in the process of designing the inverse kinematics controller of 6-DOF Industrial robot, a radial basis function-proportional integral differentiation(RBF-PID) adaptive controller method has been proposed to train the inverse kinematics model of industrial robot. Compared with the traditional RBF neural network, the advantage of RBF-PID adaptive controller was that it had high search efficiency and was not easy to fall into local optimal solution. The simulation results showed that this method could not only overcome the influence of the initial value selection of the traditional RBF neural network learning algorithm on the network performance, but also had a good solution accuracy for the inverse kinematics of the 6-DOF Industrial robot. Compared with the traditional RBF neural network algorithm, the RBF-PID adaptive controller method had improved the performance of overshoot, adjustment time and anti-interference, and solved the problem of solving the multivalued inverse kinematics algorithm.

Research on oil and gas pipeline defect recognition based on IPSO for RBF neural network

To judge location of oil and gas pipeline defects and improve recognition accuracy for morphological characteristics of the oil and gas pipeline defects. This paper proposes a recognition model of oil and gas pipeline defects based on RBF neural network model with optimal parameters selected by improved Particle Swarm Optimization (IPSO-RBFNN). In view of long search time and being easy to trap local minimum of traditional PSO, improved PSO by initializing entropy-based population and using the dynamically adjusted inertia weight and improving learning factors were used. The global optimizer IPSO was employed to optimize the center, width and weight of RBF neural network. A two-dimensional model of pipeline defects by ANSYS was established and different defects were made. IPSO-RBFNN was tested and compared to other algorithms to identify size of pipeline defects. Simulation results show that proposed model of IPSO-RBFNN could provide higher recognition accuracy than the traditional PSO-RBF neural network (PSO-RBFNN) and RBF neural network (RBFNN). Meanwhile, a theoretical basis for the selection and application of pipeline safety assessment and recognition methods is provided.

Improvement of RBF Neural Network by AdaBoost Algorithm Combined with PSO

The traditional RBF neural network has the problem of slow training speed and low efficiency, this paper puts forward the algorithm of improvement of RBF neural network by AdaBoost algorithm combined with PSO, to expand the application range of the RBF neural network. Firstly, it preprocesses the sample data in training set, and initialize the weights of test data; Secondly, it optimizes and chooses different implied layer functions and network learning parameters by using the improved PSO algorithm, to produce different types of RBF weak predictor, and repeatedly train the sample data by using Matlab tools; Finally, it constructs multiple generated RBF weak predictors to strong predictors by using AdaBoost iterative algorithm. It chooses data sets of UCI database to do the simulation experiment, and the simulation results show that the proposed algorithm further reduces the mean absolute error, compared with the traditional RBF neural network prediction, the experiment has improved the prediction precision of the network, to provide a reference for RBF neural network prediction.

Intrusion Detection Scheme based on IPSO-RBF

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network intrusion detection model of IPSO-RBF, made convergence and disturbance variation analysis on improving optimized particle swarm optimization, finally made a experimental simulation to this detection model, the result showed: compared with traditional models, the neural network intrusion detection of improved particle swarm optimization was faster and has a higher accuracy rate as well as efficiency.

The Estimate for Approximation Error of Neural Network with Two Weights

The neural network with two weights is constructed and its approximation ability to any continuous functions is proved. For this neural network, the activation function is not confined to the odd functions. We prove that it can limitlessly approach any continuous function from limited close subset of R m to R n and any continuous function, which has limit at infinite place, from limitless close subset of R m to R n. This extends the nonlinear approximation ability of traditional BP neural network and RBF neural network.

International Early Warning Nonlinear Model for Securities Systemic Risk Based on RBFNN Using UDM and BVM

In the process of the international early warning for securities systemic risk, the uniform design method (UDM) and boundary value method(BVM) are used to design the uniform, large-scale and representative samples to train the RBF neural network (RBFNN). The experiments show, the difference between the real output value of RBFNN early warning model and the expected early warning fussy evaluation value of Experts is very small. The designed early warning method overcomes the difficulties of lack of large-scale and high quality training samples,the lack causing the traditional RBFNN's poor generalization ability. Meantime the methods realizes the non-linear approaching ability, avoids the non-repeatability and instability of traditional experts early warning.

Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting

A wavelet decomposition based load forecast approach is proposed for 24-h and 168-h ahead short-term load forecasting. The proposed approach is applied to and compared with representative load forecasting methods such as: time series in traditional approaches and RBF neural network and neuro-fuzzy forecaster in nontraditional approaches. The other forecasters, such as multiple linear regression (MLR), time series, feed forward neural network (FFNN), radial basis function neural network (RBFNN), clustering, and fuzzy inference neural network (FINN), reported in the literature are also compared with the present approach. The process of the proposed wavelet decomposition approach is that it first decomposes the historical load and weather variables into an approximate part associated with low frequencies and several detail parts associated with high frequencies components through the wavelet transform. The historical data are smoothened by deleting the high frequency components and fed as input to the proposed models for the prediction. A comparison of wavelet and non-wavelet based approaches shows the superiority of proposed wavelet based approach over non-wavelet methods for the same set of data of the same utility.

Design of RBF Neural Networks Based on Adjustable Radius

In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better than oRBFNN.