检索结果-内蒙古大学图书馆

An SOA-RBFNN approach for the system modelling of optimal energy management in grid-connected smart grid system

ARTIFICIAL INTELLIGENCE REVIEW 2023年第5期56卷 4171-4196页

作者： Kuppusamy, Karthikumar Vairakannu, Senthil Kumar Marimuthu, Karuppiah Natarajan, Udhayaraj Sekar, Krishnakumar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai Tamil Nadu India Anna Univ Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India

A hybrid method for energy management on grid-connected MG system is proposed under this manuscript. Grid-connected MG system takes photovoltaic (PV), wind turbine (WT), battery. The proposed system is an integration of seagull optimization algorithm (SOA) and the radial basic functional neural network (RBFNN), thus it is named SOA-RBFNN. Here, in the grid-connected microgrid configuration, the necessary load demand is always monitored with RBFNN methodology. SOA optimizes the perfect match of the MG taking into account the predictable load requirement. The fuel cost, together with the power variation per hour of the electric grid, the operation and maintenance cost of microgrid system linked with grid, is described. The proposed model runs on the MATLAB/Simulink workstation and efficiency is investigated using existing techniques as AGO-RNN and MBFA-ANN. Statistical analysis, elapsed time, modeling metrics, and determination of optimal sample size for adjustment and validation of proposed and existing technique are evaluated. The efficiency values on the 100, 200, 500, and 1000 trails are 99.7673%, 99.7609%, 99.9099%, and 99.9373%.

关键词： Energy management system Grid connected SG system Predicted load demand seagull optimization algorithm Radial basis functional neural network

来源：评论

学校读者我要写书评

暂无评论

Tuning PID Controllers Based on Hybrid Arithmetic optimization algorithm and Artificial Gorilla Troop optimization for Micro-Robotics Systems

引用

IEEE ACCESS 2023年 11卷 27138-27154页

作者： Ghith, Ehab Saif Tolba, Farid Abdel Aziz Ain shams Univ Fac Engn Mechatron Engn Dept Cairo 11566 Egypt

Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. In the present paper, a novel hybrid algorithm based on Arithmetic optimization algorithm (AOA) and Artificial Gorilla troop's optimization (GTO), (HAOAGTO) is compared with different four algorithms Arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), seagull optimization algorithm (SOA), Parasitism-predation algorithm (PPA). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. We verified that the obtained results by application of the proposed hybrid algorithm-based AOA and GTO (HAOAGTO) is better than those obtained by Arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), seagull optimization algorithm (SOA), Parasitism-predation algorithm (PPA). it is implemented to obtain the optimal parameters of the PID for reduction the ISTES.

关键词： optimization Mathematical models Classification algorithms Metaheuristics Tuning Force PID controller arithmetic optimization algorithm artificial gorilla troop's optimization seagull optimization algorithm parasitism-predation algorithm hybrid between AOA and GTO (HAOAGTO) minimally invasive surgery

来源：评论

学校读者我要写书评

暂无评论

Research on Threshold Segmentation Method of Two-Dimensional Otsu Image Based on Improved Sparrow Search algorithm

引用

IEEE ACCESS 2023年 11卷 70459-70469页

作者： Du, Yun Yuan, Hanliu Jia, Kejin Li, Feifei Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Peoples R China

Aiming at the issues of complex calculation and low accuracy of two-dimensional (2D) Otsu segmentation images, an image threshold segmentation means of 2D Otsu ground on a modified sparrow search algorithm is proposed. Firstly, in the initialization stage, the tent chaos mapping is added to enhance the multiformity of the population, and the population elite strategy is introduced to enhance the quality of the initial solution. Secondly, in the local search stage, the elite reverse learning strategy is applied to renewal the sparrow location to solve the issue of getting into local optimality. Eventually, the modified sparrow search algorithm is fused with 2D Otsu and the image threshold is segmented to enhance the accuracy of image segmentation. Compared with the traditional 2D Otsu algorithm, 2D Otsu genetic algorithm (GA-Otsu), 2D Otsu seagull optimization algorithm (SOA-Otsu), 2D Otsu particle swarm algorithm (PSO-Otsu) and 2D Otsu sparrow search algorithm (SSA-Otsu), the mean square error (MSE) value is reduced by 40.84%, 2.68%, 1.57%, 0.77% and 1.04%, respectively, and the peak signal-to-noise ratio (PSNR) value is increased by 24.48%, 1.24%, 0.83%, 0.40% and 0.45%, respectively. Moreover, the optimal threshold of the proposed algorithm is better than the other five algorithms. It is verified that the algorithm in this paper has faster convergence speed and higher accuracy, and effectively improves the quality of image segmentation.

关键词： Tent chaos mapping elite reverse learning strategy sparrow search algorithm (SSA) two-dimensional Otsu seagull optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

DA-RNN-Based Bus Arrival Time Prediction Model

引用

INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2024年第3期22卷 660-674页

作者： Li, Zhixiao Cangzhou Normal Univ Dept Math & Stat Cangzhou 061000 Peoples R China

Accurate prediction of bus arrival time is crucial for constructing smart cities and intelligent transportation systems. Objectivity and clarity must be maintained throughout to ensure efficient operation. Therefore, it is essential to achieve precise bus arrival time prediction. A recurrent neural network prediction model employing a dual-stage attention mechanism is proposed. The model was constructed based on bidirectional long and short-term memory networks, and arrival time predictions incorporate both dynamic and static factors of bus travel. The model utilized an advanced seagull optimization algorithm to optimize the model parameters, enhanced model iteration and population richness by incorporating the sine-cosine operator and adaptive parameters, and ultimately validated model performance through simulation experiments. The experimental results showed that the prediction error of the benchmark model is 324s and that of the normal peak is 87s. Considering the dynamic and static factors, the prediction error of the model was 6s similar to 8s. The minimum values of mean absolute percentage error, root mean square error and mean absolute error of the model were 0.07, 11.28 and 9.22, respectively. The experimental results demonstrated that the minimum error of the model exhibits the highest prediction accuracy, substantiating the model's potential for accurate prediction. Furthermore, the model's performance is effectively safeguarded from the impact of peak time. In addition, the model is feasible in practical application.

关键词： Recurrent neural network Dual-stage attention mechanism seagull optimization algorithm Public transportation Arrival time Prediction

来源：评论

学校读者我要写书评

暂无评论

Adaptive PID control of overhead cranes based on ISOA-NN

引用

INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第8期12卷 2903-2917页

作者： Tang, Weiqiang Ma, Rui Wang, Wei Xu, Tianpeng Gao, Haiyan Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China

Positioning and anti-swing are the key issues of overhead crane control systems. An adaptive PID control strategy based on improved seagull optimization algorithm and neural network is proposed for overhead cranes under variable working conditions and disturbances. An adaptive PID controller based on a neural network is firstly designed to adjust parameters of the controller online. Then, an improved seagull optimization algorithm with an adaptive inertia coefficient is introduced to optimize the initial weight of the neural network. The optimization process of controller parameters and initial weights of neural network are given in the form of two algorithms. The results show that, compared with the adaptive PID control methods based on fuzzy rules and neural network technique with random initial weights, the proposed adaptive PID control strategy can make the crane system more adaptable to payload changes and more robust against disturbances. Additionally, compared with the particle swarm optimization algorithm, the improved seagull optimization algorithm makes the crane system more stable and has better positioning and anti-swing performance.

关键词： Adaptive PID control Neural networks Overhead cranes Parameter optimization seagull optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Path optimization research for in-vehicle navigation systems 7

Path optimization research for in-vehicle navigation systems

引用

7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Wu, Gang Ma, Jnubo Shenyang Univ Shenyang Peoples R China

ISBN: (纸本)9781510674479

In order to improve the use of roads and solve the huge pressure brought by the influx of a large number of private cars on the existing traffic and transportation network due to the development of the economy and the improvement of people's living standards1, thus leading to more and more large and medium-sized city roads facing traffic congestion and increasingly serious status quo, to achieve the purpose of reducing traffic congestion, this paper uses an intelligent algorithm - this paper uses an intelligent algorithm - the seagull optimisation algorithm - to optimise the driving route of in-car navigation, so as to achieve the shortest possible vehicle driving time and driving path, reduce traffic pressure and improve the current situation of traffic congestion.

关键词： in-car navigation path optimization seagull optimization algorithm transportation

来源：评论

学校读者我要写书评

暂无评论

MCSOA: A Novel High-Reliable Wireless Sensor Networks Routing optimization Method for Power Internet of Things 9

MCSOA: A Novel High-Reliable Wireless Sensor Networks Routin...

引用

9th International Conference on Automation, Control and Robotics Engineering (CACRE)

作者： Wei, Jingyi Luo, Tao Jia, Dingyi Zhu, Yuanjiao Ma, Pengwei Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

In recent years, as the Power Internet of Things continues to advance and improve, the routing requires efficient and reliable optimization methods to ensure stable operation. However, sensor nodes in Wireless Sensor Networks for Power Internet of Things have limited energy and lifespan, making the search for an efficient routing optimization method a critical issue. This paper innovatively proposes a novel high-reliable Wireless Sensor Networks routing optimization method for Power Internet of Things-the Multi-Objective Chaotic seagull optimization algorithm (MCSOA). MCSOA integrates chaotic optimization strategies with the seagull optimization algorithm, enable to escape local optima and improving its global search capability. Additionally, it incorporates multi-objective functions to address various key factors in PSWSN routing, including minimizing transmission loss, balancing load distribution, and enhancing system reliability. To evaluate the effectiveness of the optimization method, extensive new experiments were conducted, accurately reflecting real-world power grid operation scenarios. The experimental findings indicate that MCSOA significantly outperforms traditional algorithms like Genetic algorithm and Particle Swarm optimization in terms of efficiency. Specifically, MCSOA cuts down energy use by a minimum of 16.1% and prolongs the lifespan of the PSWSN by at least 15.6%.

关键词： Wireless Sensor Networks Power Internet of Things Routing optimization seagull optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Parameter optimization of PID Controller Based on Improved Sine-SOA algorithm 19

Parameter Optimization of PID Controller Based on Improved S...

引用

19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： You, Mankun Wu, Yanjuan Wang, Yunliang Xie, Xiyang Xu, Chen Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Binshui Xidao 391 Tianjin Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Aiming at the problem that the traditional PID controller was not ideal, the parameters could not be adjusted to the best state, and the control system could not achieve good control effect, an improved seagull optimization algorithm (SOA) based on improved Sine chaotic mapping was proposed to optimize the parameters of PID controller. Sine mapping strategy was adopted to make the initial seagull population evenly distributed in the search space, to improve the shortcomings of the seagull optimization algorithm, such as low solution accuracy, slow convergence speed and easy to fall into premature convergence, and improve the convergence speed and convergence accuracy of the algorithm. Eight standard test functions were tested, and the improved gull optimization algorithm was compared with the unimproved gull algorithm, particle swarm optimization algorithm (PSO), beetle antennae search algorithm (BAS), particle swarm optimization -beetle antennae search algorithm (PSO-BAS) and the seeker optimization algorithm (TSOA), to verify that the improved gull optimization algorithm has better optimization effect. The improved algorithm is applied to a second-order system and double closed-loop DC motor speed regulation system to optimize the parameters of PID controller. The results show that the algorithm has high precision, simple principle, better convergence precision and faster convergence speed.

关键词： Sine chaotic mapping seagull optimization algorithm PID control Motor speed control

来源：评论

学校读者我要写书评

暂无评论

Load Frequency Control of Multi-Area Power System with Integration of SMES and Plug-In Electric Vehicles 4

Load Frequency Control of Multi-Area Power System with Integ...

引用

4th IEEE Global Power, Energy and Communication Conference (IEEE GPECOM)

作者： Kalian, Ch Naga Sai Bajaj, Mohit Kamel, Salah Jurado, Francisco Vasireddy Venkatadri Inst Technol Elect & Elect Engn Guntur 522508 Andhra Pradesh India Natl Inst Technol Delhi Dept Elect & Elect Engn Delhi India Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Univ Jaen Dept Elect Engn Jaen Spain

ISBN: (纸本)9781665469258

In this paper, fractional order PID (FOPID) optimized with seagull optimization algorithm (SOA) is designed as a secondary regulator for load frequency control (LFC) of the interconnected power system (IPS). However, the efficacy of FOPID is showcased with other integral order type classical controllers. IPS deliberated in this work is a widely used model of dual area hydro-thermal (DAHT) upon laying 10% perturbation of step load on area-1 (10%SLP) for investigation. Further, plug-in-electric vehicles (PEVs) are integrated into area-1 of DAHT for analysis purposes. DAHT responses are enhanced with the integration of PEVs. Later, the control areas of DAHT are incorporated with superconducting magnetic energy storage (SMES) devices to get further enhancement in system stability. Finally, simulation results reveal the superiority of implementing PEVs and SMES coordinated strategies for improvement in frequency regulation.

关键词： FOPID controller seagull optimization algorithm DAHT system PEVs SMES

来源：评论

学校读者我要写书评

暂无评论

Optimizing Workforce Demand Forecasting in Enterprises: A Novel Approach with SF-SOA-based Extreme Learning Machine 3

Optimizing Workforce Demand Forecasting in Enterprises: A No...

引用

3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Liu, Qing Claro M. Recto Academy of Advanced Studies Lyceum of the Philippines University Manila Manila Philippines

ISBN: (纸本)9798350380989

The Extreme Learning Machine (ELM) stands out in machine learning as a powerful tool for approximating complex nonlinear mappings. However, challenges arise from the inherent randomness in weight initialization, impacting consistency and optimality. This work introduces a novel optimization approach, the Spiral Flight seagull optimization algorithm-based ELM (SF-SOA-based ELM), leveraging the seagull optimization algorithm (SOA) for global and local search capabilities. SF-SOA optimizes weights and thresholds, addressing traditional SOA limitations with three key strategies: chaotic sequences, optimization of spiral flight paths and local search based on convergence conditions. Applied to practical workforce demand forecasting in enterprises, SF-SOA demonstrates significant improvements in convergence accuracy, speed, and error reduction. The refined SF-SOAbased ELM showcases enhanced performance, highlighting its potential to overcome challenges in random weight initialization within traditional networks. © 2024 IEEE.

关键词： Demand Forecasting Extreme Learning Machine Nonlinear Mappings seagull optimization algorithm

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：