检索结果-内蒙古大学图书馆

improved sparrow search algorithm with the Extreme Learning Machine and Its Application for Prediction

NEURAL PROCESSING LETTERS 2022年第5期54卷 4189-4209页

作者： Li, Jingjing Wu, Yonghong Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

The prediction accuracy and generalization ability of extreme learning machine (ELM) are reduced by randomly generated weight and threshold before training. To solve these problems, an improved sparrow algorithm (ISSA)-ELM hybrid prediction model is proposed in this paper. Firstly, SSA is improved by the opposition-based learning (OBL), cosine inertia weights and Levy flight strategy. Secondly, unimodal and multimodal benchmark functions are used to verify the performance of ISSA. The test results show that ISSA has better optimization accuracy, stability and statistical properties than SSA, whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), gravity search algorithm (GSA), grasshopper optimization algorithm (GOA) and dragonfly algorithm (DA). Finally, ISSA is applied to optimize the weights and thresholds of ELM, and four regression datasets are used to test ISSA-ELM. The simulation results show that ISSA-ELM has better prediction accuracy, generalization ability and stability than SSA-ELM and ELM.

关键词： improved sparrow search algorithm Extreme learning machine Opposition-based learning Levy flight

来源：评论

学校读者我要写书评

暂无评论

Natural gas consumption forecasting using a novel two-stage model based on improved sparrow search algorithm

引用

JOURNAL OF PIPELINE SCIENCE AND ENGINEERING 2025年第1期5卷 100220-100220页

作者： Qiao, Weibiao Ma, Qianli Yang, Yulou Xi, Haihong Huang, Nan Yang, Xinjun Zhang, Liang Yanshan Univ Sch Vehicle & Energy Qinhuangdao 066004 Hebei Peoples R China China Petr Engn & Construct Corp Southwest Co Chengdu 610041 Sichuan Peoples R China China Petrochem Corp Gas Co Beijing 100000 Peoples R China Heibei Univ Environm Engn Dept Basic Qinhuangdao 066004 Heibei Peoples R China PipeChina North Pipeline Co Informat Ctr Langfang 065000 Hebei Peoples R China

The foundation of natural gas intelligent scheduling is the accurate prediction of natural gas consumption (NGC). However, its volatility, brings difficulties and challenges in accurately predicting NGC. To address this problem, an improved model is developed combining improved sparrow search algorithm (ISSA), long short-term memory (LSTM), and wavelet transform (WT). First, the performance of ISSA is tested. Second, the NGC is divided into several high- and low-frequency components applying different layers of Coilfets', Fejer-Korovkins', Symletss', Haars', and Discretes' orders. In addition, the LSTM is applied to forecast the decomposed components in view of the one- and multi-step, and its hyper-parameters are optimized by ISSA. At last, the final prediction results are reconstructed. The research results indicate that: 1) Comparing to other machine algorithms (e.g., fuzzy neural network), the convergence speed and stability of ISSA are stronger in view of standard deviation and mean;2) The prediction performance of the developed model is better than that of other forecasting models;3) The forecasting performance of the single-step forecasting is superior to that of the two-, three-, and four- step;4) The computational load of the proposed prediction model is the highest compared to other models, and the prediction accuracy is still excellent on the extended time series.

关键词： Natural gas consumption improved sparrow search algorithm Long short-term memory Wavelet transform Forecasting

来源：评论

学校读者我要写书评

暂无评论

Hybrid improved sparrow search algorithm and sequential quadratic programming for solving the cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system

引用

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 6237-6253页

作者： Tian, Hao Wang, Keqing Yu, Bing Song, Changpo Jermsittiparsert, Kittisak Nanjing Univ Informat Sci & Technol Binjiang Coll Nanjing Jiangsu Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gener Beijing Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Humanities & Social Sci Da Nang Vietnam Henan Univ Econ & Law MBA Sch Henan Peoples R China

In this paper, a hybrid photovoltaic/battery/diesel generator power generation system is designed and optimized to power a network of a remote village in China. The purpose of the system designed here is to lessen the fuel costs of the system dependent on the load demand and some constraints. Therefore, the idea comprises an optimization problem that is solved here based on a new optimization policy by an improved design of sparrow search algorithm (imSSA) and the Sequential Quadratic Programming (SQP). The results of the proposed method are analyzed in different seasons and also on weekdays and weekends to show their effect on the operational cost of the PV/Diesel BESS system. Final results indicate that in both winter and summer, the weekday costs are lesser than weekend fuel costs. Furthermore, the summer fuel cost is lesser than the winter fuel costs which is because of lesser demand in summer and the higher levels of summer radiation imply less usage of auxiliary origins. The results show that at the weekends cost more than the weekday. For instance, for the proposed mode, the winter weekends and the weekdays' costs are US$12.1 and US$10.2, respectively, or the summer weekends and the weekdays' costs are US$7.3 andUS$5.7, respectively.

关键词： Photovoltaic diesel generator battery energy storage system sequential quadratic programming improved sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

An enhanced fault diagnosis method for fuel cell system using a kernel extreme learning machine optimized with improved sparrow search algorithm

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartA期50卷 1184-1196页

作者： Quan, Rui Liang, Wenlong Wang, Junhui Li, Xuerong Chang, Yufang Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China Hubei Univ Technol Hubei Engn Res Ctr Safety Monitoring New Energy & Wuhan 430068 Peoples R China

Proton exchange membrane fuel cells (PEMFC) have a broad development prospect in the fields of vehicles, drones and ships due to their high efficiency and cleanliness. However, the problems of insufficient reliability and durability have severely restricted their indus-trialization process. To improve the safety, reliability and durability of fuel cell system, a fault diagnosis method that combined kernel principal component analysis (KPCA) with an improved sparrow search algorithm (ISSA) and an optimized kernel extreme learning machine (KELM) was proposed in this study. Firstly, KPCA is utilized to extract nonlinear features from fault indicators and obtain the fault feature vector of the fuel cell system. Then, by incorporating logistic mapping and Cauchy Gaussian mutation strategies to improve the sparrow search algorithm (SSA), ISSA was used to optimize the kernel parameters and regularization coefficient in KELM. The experimental results show that the KPCA-ISSA-KELM method for normal conditions, hydrogen leakage and membrane drying are 100%, 98.5% and 100%, respectively, with an overall accuracy of 99.5% and an operation time of 0.97s. The diagnostic accuracy of the proposed method is 10.4%, 5.7%, 4.8%, 4.2%, 3.0%, 1.8% higher than support vector machine (SVM), back propagation neural network (BPNN), KELM, genetic algorithm-based KELM (GA-KELM), particle swarm optimization based KELM (PSO-KELM) and SSA-KELM, respectively, and the operation time is only slightly higher than that of the SVM model and KELM model.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Fuel cell system Kernel principal component analysis improved sparrow search algorithm Kernel extreme learning machine Fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Departure flight delay prediction due to ground delay program using Multilayer Perceptron with improved sparrow search algorithm

引用

AERONAUTICAL JOURNAL 2024年第1322期128卷 706-724页

作者： Dong, X. Zhu, X. Zhang, J. Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat State Key Lab Air Traff Management Syst Nanjing Peoples R China

The ground delay program (GDP) is a commonly used tool in air traffic management. Developing a departure flight delay prediction model based on GDP can aid airlines and control authorities in better flight planning and adjusting air traffic control strategies. A model that combines the improved sparrow search algorithm (ISSA) and Multilayer Perceptron (MLP) has been proposed to minimise prediction errors. The ISSA uses tent chaotic mapping, dynamic adaptive weights, and Levy flight strategy to enhance the algorithm's accuracy for the sparrow search algorithm (SSA). The MLP model's hyperparameters are optimised using the ISSA to improve the model's prediction accuracy and generalisation performance. Experiments were performed using actual GDP-generated departure flight delay data and compared with other machine learning techniques and optimisation algorithms. The results of the experiments show that the mean absolute error (MAE) and root mean square error (RMSE) of the ISSA-MLP model are 16.8 and 24.2, respectively. These values are 5.61%, 6.3% and 1.8% higher in MAE and 4.4%, 5.1% and 2.5% higher in RMSE compared to SSA, particle swarm optimisation (PSO) and grey wolf optimisation (GWO). The ISSA-MLP model has been verified to have good predictive and practical value.

关键词： ground delay programs departure flight delay improved sparrow search algorithm machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimisation of vehicle body parameters based on improved sparrow search algorithm

引用

INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2024年第1-2期69卷 39-55页

作者： Guo, Xiuzhen Wei, Yannan Zhang, Xianzhen Hunan Ind Polytech Dept Mech Engn Changsha 410000 Peoples R China Henan Univ Urban Construct Dept Civil & Transportat Engn Pingdingshan 467000 Peoples R China Shandong Polytech Coll Dept Automot Engn Jining 272000 Peoples R China

In order to solve the problems of low structural strength, high total manufacturing cost and long optimisation time of vehicle body parameters optimisation methods, a multi-objective optimisation method of vehicle body parameters based on improved sparrow search algorithm is proposed. The multi-objective optimisation model of vehicle body parameters is constructed by analysing the multi-objectives and related constraints such as minimising body mass, maximising structural stiffness and minimising vibration and noise, and combining with Gaussian process regression. Through tent mapping strategy, reverse learning strategy, Levy flight strategy, inertia weight factor and random walk strategy, the improved sparrow search algorithm is used to solve the model and the multi-objective optimisation of vehicle body parameters is realised. The experimental results show that the average strength of the vehicle body structure is 323.4 MPa, the total cost of vehicle body manufacturing is 107,113 yuan, and the optimisation time range is 30 similar to 53 min.

关键词： improved sparrow search algorithm vehicle body parameters multi-objective optimisation tent mapping strategy reverse learning strategy Levy flight strategy inertia weight factor

来源：评论

学校读者我要写书评

暂无评论

Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm

引用

MEASUREMENT & CONTROL 2024年第7期57卷 1014-1021页

作者： Zhou, Yaosheng Han, Guirong Wei, Ziang Huang, Zixin Chen, Xubing Wu, Jianjun Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China Hubei Inst Fine Arts Sch Ind Design Wuhan 430020 Hubei Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China Wuhan Text Univ Hubei Key Lab Digital Text Equipment Wuhan Peoples R China Huangshi Jiufeng Intelligent Electromech Co LTD Wuhan Peoples R China

In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.

关键词： Welding robot optimal energy consumption trajectory planning B-spline improved sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Reservoir Porosity Construction Based on BiTCN-BiLSTM-AM Optimized by improved sparrow search algorithm

引用

PROCESSES 2024年第9期12卷 1907页

作者： Qiao, Lei Gao, Haijun Cui, You Yang, Yang Liang, Shixin Xiao, Kun Hebei Petr Univ Technol Hebei Instrument & Meter Engn Technol Res Ctr Chengde 067000 Peoples R China East China Univ Technol State Key Lab Nucl Resources & Environm Nanchang 330013 Peoples R China

To evaluate reservoir porosity accurately, a method based on the bidirectional temporal convolutional network (BiTCN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM) optimized by the improved sparrow search algorithm (ISSA) is proposed. Firstly, the sparrow search algorithm improved by a phased control step size strategy and dynamic random Cauchy mutation is introduced. Secondly, the superiority of the ISSA is confirmed by the test functions of Congress on Evolutionary Computation in 2022 (CEC-2022). Furthermore, the experimental findings are assessed using the Wilcoxon test, which provides additional evidence of the ISSA's superiority against the competing algorithms. Finally, the BiTCN-BiLSTM-AM is optimized by the ISSA, and the ISSA-BiTCN-BiLSTM-AM was applied to reservoir porosity construction in the Midlands basin. The results showed that the RMSE and MAE of the proposed model were 0.4293 and 0.5696, respectively, which verified the effectiveness and success rate of reservoir parameter construction by addressing the shortcomings in the capabilities shown by conventional interpretation procedures.

关键词： reservoir porosity construction bidirectional temporal convolutional network bidirectional long short-term memory network attention mechanism improved sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by improved sparrow search algorithm

引用

IEEE ACCESS 2024年 12卷 17045-17057页

作者： Wang, Manqi Cao, Hui Ai, Zeren Zhang, Qi Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

The key to reducing operation and maintenance costs and improving reliability is to evaluate the condition and fault detection methods of ship ballast water systems. To reduce the impact of support vector machine (SVM) parameter uncertainty and improve the accuracy of ship ballast water system fault diagnosis models, this paper proposes a multi-strategy collaborative improved sparrow search algorithm (ISSA) to optimize the parameters of SVM for fault diagnosis. First, ISSA initializes the population using tent chaotic maps to improve spatial distribution uniformity and stabilize the population quality. Second, the discoverer in the algorithm adopts an adaptive weighting strategy to avoid falling into local optima and accelerate the convergence speed. Finally, the follower position introduces mutation strategies and chaotic perturbations to enhance local evolution capabilities and improve the utilization of search areas. The experimental results show that compared with sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimization algorithm (GWO), ISSA can obtain better optimal solutions at the fastest convergence rate on the benchmark test function. On the dataset of the ship ballast water system, the ISSA-SVM fault diagnosis model proposed in this paper has the best classification performance, with an average accuracy of 97.44%. Compared with the other two models, ISSA-SVM is 7.55% and 0.83% higher than SVM's 89.89% and SSA-SVM's 96.61%.

关键词： Fault diagnosis improved sparrow search algorithm marine engineering support vector machine ship ballast water system

来源：评论

学校读者我要写书评

暂无评论

A new approach for seepage parameter inversion of earth-rockfill dams based on an improved sparrow search algorithm

引用

COMPUTERS AND GEOTECHNICS 2024年 167卷

作者： Zhou, Yang Li, Chuyin Pang, Rui Li, Yichuan Xu, Yongsheng Chen, Jiansheng Dalian Univ Technol Sch Infrastructure Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China China Special Equipment Inspect & Res Inst Beijing 100013 Peoples R China Power China Zhongnan Engn Corp Ltd Changsha 410014 Hunan Peoples R China 2 Linggong Rd High Tech Zone Dalian 116024 Peoples R China Dalian Univ Technol DUT Sch Infrastructure Engn Dalian Peoples R China Dalian Univ Technol DUT State Key Lab Coastal & Offshore Engn Dalian Peoples R China

This paper introduces a novel seepage parameter inversion method for earth-rockfill dams. The method utilizes pore water pressure data and employs a radial basis function (RBF) neural network as a surrogate model, which is optimized with the improved chaos sparrow search algorithm (ICSSA) using a hybrid strategy. The improved surrogate model (ICSSA-RBF) establishes a nonlinear relationship between the seepage parameter and pore water pressure. Unlike the original algorithm, the algorithm proposed in this paper can avoid three principal problems: the optimization search falling into a local optimal value, the population diversity decreasing during the iteration process, and the RBF neural network being prone to overfitting. The ICSSA, which is proficient in recognizing an objective function's significant values, is also chosen for identifying the parameters. To validate the effectiveness of the proposed approach, four classical test functions, a numerical model, and an actual engineering project are considered for the comparative analysis. The study outcomes reveal that ICSSA-RBF exhibits an exceptional level of prediction accuracy, with a mean relative error of less than 5 parts per thousand. The findings also affirm the potential of the proposed approach in parameter identification and its superiority in facilitating the evaluation of seepage parameters in earth-rockfill dams.

关键词： Inverse analysis Pore water pressure Earth-rockfill dams improved sparrow search algorithm Seepage parameter

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：