检索结果-内蒙古大学图书馆

3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Yao, Wenyan Zhang, Tianbao Nanning Univ Nanning 530200 Guangxi Peoples R China Univ Sains Malaysia Sch Management George Town Malaysia Qinzhou Tobacco Monopoly Bur Qinzhou 535000 Guangxi Peoples R China

ISBN: (纸本)9798400718212

This study aims to address the lack of scientific and systematic decision systems in the field of Human Resources Management (HRM). By designing a HRM decision support system based on Multi-Agent systems and reinforcement learning algorithms, effective tools are provided to HR managers to assist them in making more scientific and systematic HRM decisions. The research analyzes the current issues in HRM practices and proposes comprehensive solutions. Through the optimization of Multi-Agent reinforcement learning algorithms, experiments validate the effectiveness of the system in supporting decision-making in HRM. The results demonstrate that the improved algorithms outperform traditional methods, confirming the efficacy of the system's design and optimization. This HRM decision support system, based on Multi-Agent systems and reinforcement learning algorithms, holds the potential to drive organizational development and enhance the efficiency of HRM. However, further research and practical application are needed to refine and optimize the system to adapt to the constantly evolving HRM environment.

关键词： Human Resources Management (HRM) Multi-Agent Systems reinforcement learning algorithm Decision Support System

来源：评论

学校读者我要写书评

暂无评论

Smart City Distributed Trade Economy Dynamic Scheduling of E-Commerce Platform Based on reinforcement learning algorithm

引用

JOURNAL OF TESTING AND EVALUATION 2024年第3期52卷 1804-1816页

作者： Tang, Yimeng Chongqing Inst Foreign Studies Sch Int Business & Management 18 Longshi Rd Chongqing 401120 Peoples R China

In order to improve the reliability and economy of decentralized trade economy dynamic scheduling on e-Commerce platforms and shorten the running time of decentralized trade economy dynamic scheduling on e-Commerce platforms, a decentralized trade economy dynamic scheduling method based on the reinforcement learning algorithm is proposed. In this paper, we analyze the basic theory of the reinforcement learning algorithm, study the Q -learning algorithm, build a neural network to fit the value model, and initialize the reinforcement learning algorithm. With Markov decision process as the framework model, the optimal state behavior value function is updated by using the modeless discounted reward reinforcement learning algorithm Q-learning as the value iteration method. Gibbs distribution is used to construct exploratory random strategies to select behaviors with probability. Using the reinforcement learning algorithm and the three-layer feedforward neural network as the approximator of the state behavior value function, this paper studies the generalization of the value function faced by the decentralized trade economy dynamic scheduling of e-Commerce platforms and realizes the decentralized trade economy dynamic scheduling of e-Commerce platforms. The experimental results show that the proposed method can effectively improve the reliability and economy of the decentralized trade economy dynamic scheduling of e-Commerce platforms.

关键词： reinforcement learning algorithm Q-learning algorithm e-Commerce platform decentralized trade economy dynamic scheduling

来源：评论

学校读者我要写书评

暂无评论

Research on Computer Aided learning System Based On reinforcement learning algorithm

引用

Procedia Computer Science 2024年 243卷 472-481页

作者： Haiyan Lu School of Education and Foreign Languages Wuhan Donghu University Wuhan 430212 Hubei China

In today's information-based education era, the computer-aided instruction system under the background of "Internet +" is ushering in unprecedented development opportunities. Based on VARK model, this paper discusses the design and implementation of computer-aided instruction system based on reinforcement learning algorithm. Through in-depth investigation of the application status of computer-aided instruction system and the characteristics of popular systems at home and abroad, combined with the principle and application of reinforcement learning algorithm, this study builds a system architecture with personalized education evaluation function. In the aspect of system design, with the help of neural network model and online test module, the study realizes intelligent analysis and evaluation of students' learning patterns, and improves the pertinence and effect of teaching. Through strict functional and performance tests, the stability and reliability of the system are verified, and the network and intelligent characteristics of the system are demonstrated in the learning process of students, which makes a positive contribution to the improvement of the level of education information and the optimization of teaching effect.

关键词： Computer-Aided Instruction reinforcement learning algorithm VARK Model Intelligent Teaching System

来源：评论

学校读者我要写书评

暂无评论

Construction of Automatic Scheduling and Visualization System for Power Grid Space Operation Based on reinforcement learning algorithm 23

Construction of Automatic Scheduling and Visualization Syste...

引用

Proceedings of the 2023 International Conference on Big Data Mining and Information Processing

作者： Xiaokang Zhu Ning Wang Biao Zou Songtao Zhu Teng Fang Yubo Gao State Grid Electric Power Space Technology Company Limited China

ISBN: (纸本)9798400709166

With the complexity and increasing demand of the power grid, more efficient scheduling methods are needed. reinforcement learning, as an artificial intelligence technology, provides adaptive decision-making solutions that can optimize resource allocation and job scheduling based on the actual state and task requirements of the power grid. The experimental results indicate that the system has achieved significant performance improvement in different power grid operation scenarios. In terms of resource utilization, due to the higher utilization rate of the algorithm in this paper compared to the other two algorithms, the scheduling success rate is correspondingly higher than the other two algorithms. The success rate of the algorithm in this paper can reach 90.24%, which is 22.18% and 22.72% higher than GA (Genetic algorithm) and DL (Deep learning). Finally, in the cost-effectiveness ratio experiment, it is evident that the cost-effectiveness ratio of the algorithm proposed in this paper is much higher than that of the other two algorithms. This demonstrates the potential advantages of reinforcement learning algorithms in power grid spatial job scheduling. Future research can further explore more complex power grid operation and maintenance scenarios and algorithm optimization to continuously improve the efficiency and reliability of power grid operation and maintenance. The construction of an automatic scheduling and visualization system for power grid spatial operations provides strong support for the sustainable development of the power grid industry and the integration of renewable energy.

关键词： Automatic scheduling of power grid operations Optimization of power grid management reinforcement learning algorithm System visualization

来源：评论

学校读者我要写书评

暂无评论

Combination of reinforcement learning and bee algorithm for controlling two-link arm with six muscle: simplified human arm model in the horizontal plane

引用

PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2020年第1期43卷 135-142页

作者： Rahatabad, Fereidoun Nowshiravan Rangraz, Parisa Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran

The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.

关键词： reinforcement learning algorithm Human arm modeling Artificial bee colony algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Smart Scheduling Strategy for Islanded Microgrid Based on reinforcement learning algorithm

Smart Scheduling Strategy for Islanded Microgrid Based on Re...

引用

上海市研究生“新能源与智能电网”学术论坛

作者： Lingxiao Gan Tao Yu Jing Li Electric Power College South China University of TechnologyGuangzhou 510640China

This paper investigates a hierarchical Automatic Generation Control (AGC) strategy for an islanded microgrid, including wind power, solar photovoltaic, micro turbines, small hydropower and energy storage *** upper AGC is for central *** bottom AGC is to optimize the allocation factors, expecting to meet the requirement of energy-saving generation dispatching (ESGD).Three different bottom controllers are *** of them are designed based on reinforcement learning (RL) *** order to evaluate their control performance, another proportion-based (PROP) controller which has been put into practical application is also *** dynamic models of distributed generations and loads are built to simulate the *** responses to wind turbine hipping and to large load disturbances are *** results indicate that the proposed strategy based on RL algorithm can not only achieve reliability and stability of microgrid in islanded mode, but also reduce fossil energy *** approach is a possible candidate for future microgrid control approaches.

关键词： Distributed generation islanded microgrid hierarchical AGC reinforcement learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Dynamic Coordination of Energy and Hops in WSNs Using reinforcement learning Routing algorithm

Dynamic Coordination of Energy and Hops in WSNs Using Reinfo...

引用

International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Jianyong Li Huang Wei Department of Computer and Information Science Southwest University

In wireless sensor network,the existing reinforcement learning routing algorithm usually optimize single goal and the process of route establishment is *** also has problem of data forwarding control *** this paper,we present a dynamic adaptive routing algorithm with feedback learning ability to balance the energy of wireless sensor network,to reduce the routing hops,and to reduce the establishment *** algorithm will use the local routing information and the method of feedback to learn neighbors' state;routing reward values will be obtained by weighted calculation according to the energy information and the hop counts information;the optimal routing strategy will be obtained by updating the Q-value of routing table.

关键词： Wireless sensor network Routing algorithm reinforcement learning algorithm Energy consumption

来源：评论

学校读者我要写书评

暂无评论

Neural network-based adaptive reinforcement learning for optimized backstepping tracking control of nonlinear systems with input delay

引用

APPLIED INTELLIGENCE 2025年第2期55卷 1-16页

作者： Zhu, Boyan Karimi, Hamid Reza Zhang, Liang Zhao, Xudong Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Italy DaLian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China

In this paper, the problem of adaptive optimized tracking control design is addressed for a class of nonlinear systems in strict-feedback form. The system under consideration contains input delay and has unmeasurable and restricted states within predefined compact sets. First, neural networks (NNs) are employed to approximate the unknown nonlinear dynamics, and an adaptive neural network (NN) state observer is constructed to compensate for the absence of state information. Additionally, by utilizing an auxiliary system compensation method alongside the backstepping technique, the impact of input delay is eliminated, and the generation of intermediate variables is prevented. Second, tan-type barrier optimal cost functions are established for each subsystem within the backstepping method to prevent the state variables from exceeding preselected sets. Moreover, by establishing both actor and critic NNs to execute a reinforcement learning algorithm, the optimal controller and optimal performance index function are evaluated, while relaxing the persistence of excitation condition. According to the Lyapunov stability theorem, it is demonstrated that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the output signal accurately tracks a reference trajectory with the desired precision. Finally, a practical simulation example is provided to verify the effectiveness of the proposed control strategy, demonstrating its potential for real-world implementation.

关键词： reinforcement learning algorithm Auxiliary system State observer Input delay Optimized backstepping technique

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting algorithm Using a Similar Day Selection Method Based on reinforcement learning

引用

ENERGIES 2020年第10期13卷 2640-2640页

作者： Park, Rae-Jun Song, Kyung-Bin Kwon, Bo-Sung Soongsil Univ Dept Elect Engn Seoul 06978 South Korea

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert's experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

关键词： short-term load forecasting deep Q-network (DQN) backpropagation neural network (BPNN) long short-term memory (LSTM) reinforcement learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Electric Vehicle Optimal Charging algorithm using reinforcement learning 10

Electric Vehicle Optimal Charging Algorithm using Reinforcem...

引用

10th International Conference on Electrical Engineering, Control and Robotics (EECR)

作者： Kumar, Alok Kelkar, Atul Clemson Univ Dept Mech Engn Clemson SC 29634 USA

ISBN: (纸本)9798350370010;9798350370003

The number of Electric vehicles is increasing significantly with the increase in environmental concerns and technological advancements, among many reasons, which will affect the power grid load management system. To manage the electric grid successfully, it is required to charge the electric vehicle optimally, especially during peak load hours. It will help enhance energy efficiency, lower costs, and promote grid integration. In this paper, the optimal charging algorithm is developed using a finite Markov Decision Process based reinforcement learning approach. Here, the aim is to formalize a model based on the Markov property for sequential decision-making problems. We have considered a practical scenario of a single charging station with three charging points to implement the algorithm. We aim to find the best charging rate for each time step to satisfy the feeder transformer constraints while fulfilling the energy requirement of consumers' EVs. The finite MDP approach is developed based on policy evaluation, policy iteration, and value function iteration.

关键词： Electric vehicle Optimal charging reinforcement learning 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：