Swarm Optimization Radial Basis Function Research Articles

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

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

Analysis and prediction of the temporal and spatial evolution of carbon emissions in China's eight economic regions.

Facing increasingly severe environmental problems, as the largest developing country, achieving regional carbon emission reduction is the performance of China's fulfillment of the responsibility of a big government and the key to the smooth realization of the global carbon emission reduction goal. Since China's carbon emission data is updated slowly, in order to better formulate the corresponding emission reduction strategy, it is necessary to propose a more accurate carbon emission prediction model on the basis of fully analyzing the characteristics of carbon emissions at the provincial and regional levels. Given this, this paper first calculated the carbon emissions of eight economic regions in China from 2005 to 2019 according to relevant statistical data. Secondly, with the help of kernel density function, Theil index and decoupling index, the dynamic evolution characteristics of regional carbon emissions are discussed. Finally, an improved particle swarm optimization radial basis function (IPSO-RBF) neural network model is established to compare the simulation and prediction models of China's carbon emissions. The results show significant differences in carbon emissions in different regions, and the differences between high-value and low-value areas show an apparent expansion trend. The inter-regional carbon emission difference is the main factor in the overall carbon emission difference. The economic region in the middle Yellow River (ERMRYR) has the most considerable contribution to the national carbon emission difference, and the main contributors affecting the overall carbon emission difference in different regions are different. The number of regions with strong decoupling between carbon emissions and economic development is increasing in time series. The results of the carbon emission prediction model can be seen that IPSO-RBF neural network model optimizes the radial basis function (RBF) neural network, making the prediction result in a minor error and higher accuracy. Therefore, when exploring the path of carbon emission reduction in different regions in the future, the IPSO-RBF neural network model is more suitable for predicting carbon emissions and other relevant indicators, laying a foundation for putting forward more scientific and practical emission reduction plans.

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

Temperature compensation methods of spin-exchange relaxation-free co-magnetometer

According to the temperature characteristics of spin-free exchange relaxation (SERF) co-magnetometer, three temperature compensation methods are proposed in this paper, including particle swarm optimization radial basis function (PSO-RBF) neural network, Gaussian regression and least squares support vector basis. The effectiveness of the three compensation methods is verified by experiments and compared with the back-propagation (BP) neural network optimized by a genetic algorithm compensation method. In order to improve the effect of temperature compensation, this paper also conducts correlation and cluster analysis on the different positions temperature and output signals of the SERF co-magnetometer, and selects the data of temperature points that are closely related to signal bias changes for model training. Through experimental comparison with traditional linear compensation and BP neural network compensation methods, it is found that PSO-RBF neural network has advantages in training speed, compensation accuracy and robustness. Experiments show that PSO-RBF neural network temperature compensation algorithm improves the stability of the SERF co-magnetometer by more than 53 at room temperature or under artificially imposed temperature changes.

Detection method of dissolved oxygen saturation in ecological water in coastal cities

In order to monitor the dissolved oxygen saturation in the ecological water environment of coastal cities in real time, it provides a basis for the control of ecological water pollution in coastal cities. Therefore, the detection method of ecological water pollution saturation in coastal cities is studied. First, the process and principle of dissolved oxygen in the water body are solved, and the equilibrium equation of dissolved oxygen is obtained. The Mutual Information-Particle Swarm Optimization-Radial Basis Function (MI-PSO-RBF) saturation detection method is used to detect the dissolved oxygen saturation in the ecological water of coastal cities. This method preprocesses the sampling data of ecological water in coastal cities, uses mutual information theory and dissolved oxygen balance equation, and detects the saturation of ecological water pollution through particle swarm optimization RBF neural network. Experimental results show that this method can effectively detect the saturation of ecological water pollution in coastal cities. The detection accuracy of dissolved oxygen saturation in a research area in Shandong is as high as 99%, and the detection time is less than 40 ms.

Slope reliability analysis based on PSO-RBF neural network

In this paper, a reliability analysis method based on ABAQUS and particle swarm optimization radial basis function neural network is proposed. The strength reduction method based on ABAQUS is used to calculate the safety factor corresponding to the selected random variable. The data fitting function of the radial basis function neural network is used to establish the model and map the relationship between the safety factor and the random variable to construct the response surface function. A large number of random samples generated by Monte Carlo are substituted into the function to obtain the corresponding safety coefficient,in order to calculate the instability probability and reliability index of the slope.

Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network

Combining the good pattern recognition performance of neural network and the data processing performance of wavelet decomposition, this paper combines wavelet analysis with neural network and uses particle swarm optimization radial basis function neural network to diagnose the fault of electric vehicle battery. In this paper, for large data volume and redundant data, wavelet decomposition is used to process signal processing, including data noise reduction and feature vector extraction. In the fault diagnosis of electric vehicle battery system, a radial basis function RBF neural network based on particle swarm optimization (PSO) optimization is proposed. Finally, the reliability and feasibility of the proposed algorithm are proved by simulation.

Error Modeling And Compensation Of 3D Scanning Robot System Based On Pso-Rbfnn

abstract In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct input and output data pairs of neural network, and the specific process of error modeling using PSO-RBFNN is given. Finally, through the actual experiment we test and verify the correctness and effectiveness of the proposed error modeling and compensation method by measuring the distance between the centers of two standard balls. Experimental results show: the proposed error model and the compensation method can effectively compensate the measurement errors and improve the accuracy of the 3D scanning robot system.

NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS

This paper models and forecasts the Gold Miner Spread from 23 May 2006 to 30 June 2011. The Gold Miner Spread acts as a suitable performance indicator for the relationship between physical gold and US gold equity. The contribution of this investigation is twofold. First, the accuracy of each model is evaluated from a statistical perspective. Second, various forecasting methodologies are then applied to trade the spread. Trading models include an ARMA (12,12) model, a cointegration model, a multilayer perceptron neural network (NN), a particle swarm optimization radial basis function NN and a genetic programming algorithm (GPA). Results obtained from an out-of-sample trading simulation validate the in-sample back test as the GPA model produced the highest risk-adjusted returns. Correlation filters are also applied to enhance performance and, as a consequence, volatility is reduced by 5%, on average, while returns are improved between 2.54% and 8.11% across five of the six models.