Radial Basis Function Neural Network Prediction Model Research Articles

Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network

As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected.

GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature

In this paper, the scattering parameter (S-parameter) modeling method for heterojunction bipolar transistor (HBT) at different temperatures is investigated. The S-parameters of HBT at different temperatures are randomly divided into training and test sets, which are modeled by radial basis function (RBF) neural network and general regression neural network (GRNN), respectively. Then, the fitting results of the two models in predicting S-parameter are displayed. The experimental show that the fitting results of RBF neural network are good, but the fitting errors of some data are existed. Meanwhile most of the data predicted by GRNN can achieve ideal fitting. In addition, the absolute error curves of the two models show that the error curve of RBF neural network is more volatile, while the error curve of GRNN has a small fluctuation range, which can predict the S-parameters more stably. Finally, the mean square error (MSE) of RBF neural network prediction model and GRNN prediction model are 8.6178e-04 and 3.1041e-4, respectively. It is proved that GRNN has a better modeling effect for HBT S-parameter at different temperatures. Therefore, the proposed modeling method can accurately characterize the S-parameter of HBT at different temperatures.

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.

Research on Accurate Prediction of the Container Ship Resistance by RBFNN and Other Machine Learning Algorithms

Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.

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

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

Multi-Step Predictive Current Control for NPC Grid-Connected Inverter

Aiming at improving the quality of output current when the inverter is connected to the grid, this paper proposes a control strategy of neutral point clamped (NPC) grid-connected inverter based on radial basis function neural network (RBFNN) multi-step predictive control. Firstly, the predictive control model for output current of NPC inverter is constructed by $\alpha \beta $ coordinate transformation. Then the future values of output current are predicted for each voltage vectors, using the RBFNN prediction model. After the predicted values are obtained, a cost function $f$ is calculated for each voltage vectors and the optimal voltage vector which minimizes cost function is selected. The switch state corresponding to the optimal voltage vector is applied to the inverter in the next sampling period. Thereby the control of grid-connected output current can be achieved. The simulation results show that the RBFNN multi-step predictive control method improves the capability of current tracking and reduces the harmonic rate of output current. The proposed method can balance output current under the condition of grid voltage imbalance.

Prediction of Excavation and Settlement of Shallow-buried Tunnel Based on Radial Basis Function Neural Network of Human Brain

Artificial neural network technology is to simulate the neural network structure of the human brain so as to solve the nonlinear engineering problem information processing system by establishing artificial neurons and sensors. The ground surface settlement caused by the excavation of shallow-buried tunnels is a research hotspot in the field of tunnels and underground engineering. By analyzing the influence factors that cause ground surface settlement, this study selects seven factors such as the cohesion and internal friction angle of surrounding rock as input and takes the measured value as the output to establish a ground surface settlement prediction model based on radial basis function neural network (RBFNN). A genetic algorithm is introduced to eliminate the slow convergence speed and local optimum of RBFNN. RBFNN prediction model can predict the ground surface settlement caused by the excavation of shallow-buried tunnels. The relative error of prediction is controlled within ±9%, and the prediction results meet the actual needs of the project. This study can provide reference for the expansion and application of RBFNN of human brain cortex in the engineering field.

Slope Stability Analysis Based on the Radial Basis Function Neural Network of the Cerebral Cortex

The artificial neural network technology simulates the neural network structure of the cerebral cortex by establishing artificial neurons and sensors to solve nonlinear engineering problems, and establishes the prediction model for solving such problems. Rock slope stability is a hot research topic in the field of geotechnical engineering, especially for those slopes affected by the cyclic changes of water level. This paper establishes a slope stability prediction model based on the radial basis function neural network (RBFNN) of the cerebral cortex and introduces the genetic algorithm to eliminate the drawbacks of the cerebral cortex RBFNN, that is, slow convergence and local optimisation. The cerebral cortex RBFNN prediction model can predict the slope safety factor, with the relative error controlled between -5.16% and 6.02%. According to the cerebral cortex RBFNN prediction results, as the water level in the tailing pond changes cyclically, the rock mechanics parameters of the slope gradually weaken, which leads to the continuous decrease of the slope safety factor. The results can provide reference for the extension and application of the cerebral cortex RBFNN in the engineering field, and serve as guidance for the specific project safety maintenance.

Neural network model predictive control optimisation for large wind turbines

Energy poverty limits the economy and social development throughout the world. Wind turbine reduces the energy costs and facilitates the development of renewable energy industry, which provides an effective solution to energy crisis and environment pollution and develops rapidly in recently years. In this paper, a radial basis function neural network (RBFNN) optimisation model predictive control (MPC) was proposed for large wind turbines. In accordance with the complexity and uncertainty of wind turbine operation, a linear model based on the blade element momentum theory was established and the influencing factors of the proposed model were evaluated. The MPC taking into full account three degrees of freedom control multivariate was enforced by RBFNN prediction model, which meets the requirements of specified operation region. Additionally, the RBFNN prediction model with the memory of complicated rules and changed trend was trained by a great deal of historical data. The RBFNN in combination with MPC solves global optimisation problems and improves the dynamic performance of system. Simulation results for three-bladed 5 MW onshore wind turbine verified the effectiveness of the proposed method and confirmed the fact that the fatigue loads were significantly reduced in the turbine tower.

RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task. Recently some machine learning approaches have been proposed to predict metamorphic relations. These approaches predicting single label metamorphic relation can alleviate this problem to some extent. However, many applications involve multi-group metamorphic relations, and these approaches are clearly inefficient. To address this problem, in this paper we propose a M ulti- L abel M etamorphic R elations prediction approach based on an improved radial basis function (RBF) neural network named RBF-MLMR. First, RBF-MLMR uses state-of-the-art soot analysis tool to generate control flow graph and corresponds labels from the source codes of programs. Second, the extracted nodes and the path properties constitute multi-label data sets for the control flow graph. Finally, a multi-label RBF neural network prediction model is established to predict whether the program satisfies multiple metamorphic relations. In order to improve the prediction results, affinity propagation and $k$ -means clustering algorithms are used to optimize the RBF neural network structure of RBF-MLMR. A set of dedicated experiments based on public programs is conducted to validate RBF-MLMR. The experimental results show that RBF-MLMR can achieve accuracy of around 80% for predicting two and three metamorphic relations.

RBF Neural Network Soft-Sensor Model of Electroslag Remelting Process Optimized by Artificial Fish Swarm Optimization Algorithm

For predicting the key technology index of electroslag remelting (ESR) process (the melting rate and cone purification coefficient of the consumable electrode), a radial basis function (RBF) neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA) is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of soft-sensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.

Prediction of pore-water pressure using radial basis function neural network

Knowledge of soil pore-water pressure variation due to climatic changes is fundamental for slope stability analysis and other problems associated with slope stability issues. This study is an application of Radial Basis Function Neural Network (RBFNN) modeling for prediction of soil pore-water pressure responses to rainfall. Time series data of rainfall and pore-water pressures were used to develop the RBFNN prediction model. The number of input neurons was decided by the analysis of auto-correlation between pore-water pressure data and cross-correlation between rainfall and pore-water pressure data. Establishing the number of hidden neurons by method of self learning network architecture determination and also by trial and error method was examined. A number of statistical measures were used for the evaluation of the network performance. Prediction results with a network architecture of 8–10–1 and a spread σ=3.0 produced the lowest error measures (MSE, RMSE, MAE), highest coefficient of efficiency (CE) and coefficient of determination (R2). The results suggest that RBFNN is suitable for mapping the non-linear, complex behavior of pore-water pressure responses to rainfall. Guidelines for choosing the number of input neurons and eliminating possibility of model over-fitting are also discussed.