Particle Swarm Optimization Least Research Articles

An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine

The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction.

Evaluation of Teaching Quality of English Courses in Comprehensive Universities under Multiple Indicators

With the continuous development of internationalization and the task of cultivating graduates with high comprehensive quality for the society, the importance of English teaching in comprehensive universities has been increasing. In this paper, based on the analysis of the evaluation subjects of university English teaching quality, 16 evaluation indexes are constructed from five aspects: teaching content, teaching method, teaching process, teaching literacy, and teaching effect, and the Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM) algorithm is used to comprehensively evaluate the teaching quality, and finally English teaching in universities is selected as the object of empirical research. The research results show that PSO-LSSVM algorithm has high applicability in evaluating ECCU and can provide over reference for universities to improve teaching quality and develop reform programs. Due to the lack of training data, although the evaluation index system in this paper is scientific and feasible, it still needs to be further adjusted and optimized according to the specific practice situation.

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station. The accuracy of this system depends on the speed of updating the correction data. Yet, this system often faces the problem of the reference GPS receiver’s signal cut off and internal or non- internal delays which increase the latency time and decrease the updating speed. The prediction models are used to compensate these delays. In this paper, the Particle Swarm Optimization Least Square Wavelet Support Vector Machine (PSO-LSWSVM) is used for predicting the DGPS correction. An experimental setup is designed in order to carry out the operations of reference and user stations. Low-cost receivers were used in both stations. The accuracy of PSO-LSWSVM was evaluated through various simulations and experimental. The practical tests showed that the accuracy of the designed RTDGPS is 0.42 m with PSO-LSWSVM and is equal to 0.73 m without it. In comparing with other method that recently is introduced; it has a higher positioning accuracy.

Combining Grey Relational Analysis and a Bayesian Model Averaging Method to Derive Monthly Optimal Operating Rules for a Hydropower Reservoir

Various regression models are currently applied to derive functional forms of operating rules for hydropower reservoirs. It is necessary to analyze and evaluate the model selecting uncertainty involved in reservoir operating rules for efficient hydropower generation. Moreover, selecting the optimal input variables from a large number of candidates to characterize an output variable can lead to a more accurate operation simulation. Therefore, this paper combined the Grey Relational Analysis (GRA) method and the Bayesian Model Averaging (BMA) method to select input variables and derive the monthly optimal operating rules for a hydropower reservoir. The monthly input variables were first filtered according to the relationship between the preselected output and input variables based on the reservoir optimal deterministic trajectory using GRA. Three models, Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM), Adaptive Neural Fuzzy Inference System (ANFIS), and Multiple Linear Regression Analysis (MLRA) model, were further implemented to derive individual monthly operating rules. BMA was applied to determine the final monthly operating rules by analyzing the uncertainty of selecting individual models with different weights. A case study of Xinanjiang Reservoir in China shows that the combination of the two methods can achieve high-efficiency hydropower generation and optimal utilization of water resources.

Sensitivity analysis of structural health risk in operational tunnels

Abstract During the operation of metro tunnels, structural performance could inevitably degrade due to the combined effects of the stochastic and disadvantageous environment. In order to reduce the randomness and uncertainty underlying the structural safety risk analysis in operational tunnels, this paper develops a novel hybrid approach to perform global sensitivity analysis. The deterministic and stochastic finite element (FE) model is used to develop the approximate relationship between input and output parameters with a high level of accuracy. Based on the simulated data from an FE model, a meta-model is constructed by a built Particle Swarm Optimization-Least Square Support Vector Machine (PSO-LSSVM) model. In this research, 10,000 groups of data are generated by the built PSO-LSSVM model, which provides data support for the global sensitivity analysis through Extended Fourier Amplitude Sensitivity Test (EFAST). The input variables with a high global sensitivity are identified as crucial variables which should be well controlled and managed during tunnel operation. A Hankou-Fanhu (H-F) tunnel section in the Wuhan metro system is utilized as a case study to verify the applicability of the proposed approach. Global sensitivity analysis enables the reduction of the epistemic uncertainty in tunnel structural safety management, providing insight into a better understanding of (1) the input-output causal relationships of the structural safety risk in operational tunnels, (2) the reduction of the epistemic uncertainty in project safety management of operational tunnels.

Adaptive PSO-LS-wavelet H∞ control for two-wheeled self-balancing scooter

The current study is concerned with adaptive Particle Swarm Optimization Least Square Wavelet H∞ for a two-wheel self-balancing scooter that provides a platform in order to balance itself and transport the driver in accordance to its natural lean. In order to keep the rider close to the upright position over smooth and non-smooth surfaces, providing a stable control system is the main challenge for the aforementioned vehicle. For this purpose, H∞ is combined with adaptive algorithm, Least Square Support Vector Machine (LS-SVM) and Particle Swarm Optimization (PSO) to construct the adaptive control. The most important feature of the proposed control strategy is its inherent robustness and ability to handle the nonlinear behavior of the system. Simulations results indicated that the introduced motion control architecture is capable of providing appropriate control actions to achieve both position control and trajectory tracking satisfactorily.

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

Temperature Compensation for a Six-Axis Force/Torque Sensor Based on the Particle Swarm Optimization Least Square Support Vector Machine for Space Manipulator

The output of a six-axis force/torque sensor (F/T sensor) not only varies with force or torque but also is affected by ambient temperature. In order to check the effects of temperature drift on the measurement precision of the sensor, this paper carried out experiments to obtain the quantitative results without loading the force and torque. In detail, three methods, including the least square method, radial basis function neural network, and least square support vector machine (LSSVM), were used to achieve the temperature compensation, showing that the LSSVM has the obvious advantage. However, for obtaining the optimization parameters of the LSSVM model, the particle swarm optimization (PSO) was adopted. Experimental results imply that the F/T sensor compensated by the PSO LSSVM has higher measurement precision and temperature stability.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Particle Swarm Optimization Least Square Support Machine Based Missing Data Imputation Algorithm in Wireless Sensor Network for Nuclear Power Plant’s Environmental Radiation Monitor

In wireless sensor network for nuclear power plant’s peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particle swarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node’s previous monitoring data and neighbor node’s current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.