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Full-Text Articles in Systems Science
Distributed Optimization For Integrated Energy Based On Multi-Agent Reinforcement Learning, Caixia Tao, Naikun Chen, Fengyang Gao, Jiangang Zhang
Distributed Optimization For Integrated Energy Based On Multi-Agent Reinforcement Learning, Caixia Tao, Naikun Chen, Fengyang Gao, Jiangang Zhang
Journal of System Simulation
Abstract: To address the energy management and privacy preservation problems faced by the coordinated optimization of distributed integrated energy systems, a distributed coordinated optimization strategy based on the multi-agent proximal policy optimization algorithm was proposed. An energy management model was established under the MDP framework; the electrical and thermal heterogeneous energy characteristics were considered; a multi-region two-layer interaction mechanism was constructed. Under the framework of centralized training and decentralized execution, homomorphic encryption was utilized to avoid privacy leakage during the coordination process, while accurately quantifying individual contributions to mitigate the problem of variance explosion in multi-agent policy evaluation. In the …
Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang
Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang
Journal of System Simulation
Abstract: Current multi-agent reinforcement learning algorithms suffer from low efficiency in utilizing experience data and difficulties in setting appropriate learning rates. To address these issues, this paper proposed a BiGRU multi-agent PPO with priority sampling and dynamic learning rate. The algorithm incorporated a BiGRU network to enhance the policy network's ability to model temporal information. A priority partial sampling mechanism was introduced to improve the utilization efficiency of high-value experience data. Additionally, an improved Adam optimizer with dynamic learning rate adjustment was employed to address the challenge of learning rate configuration. Simulation experiment results demonstrate that the algorithm significantly …
Optimization Of Dynamic Weapon Target Assignment Considering Random Disturbances, Zhenzu Bai, Yizhi Hou, Zhangming He, Juhui Wei, Haiyin Zhou, Jiongqi Wang
Optimization Of Dynamic Weapon Target Assignment Considering Random Disturbances, Zhenzu Bai, Yizhi Hou, Zhangming He, Juhui Wei, Haiyin Zhou, Jiongqi Wang
Journal of System Simulation
Abstract: The impact of various random disturbances in the actual command and control environment of unmanned systems on problem modeling and solving of weapon target assignment was considered, and three types of uncertainty disturbance constraints were investigated. A multi-objective dynamic sensor weapon target assignment model was established. By considering the issues of model property changes caused by disturbances and insufficient robustness of the traditional single-operator solving algorithm, a multi-operator constrained multi-objective evolutionary framework based on the deep Q-network was proposed. The algorithm described the convergence, diversity, and feasibility of the population in both the objective and decision spaces. It established …
Optimization Of Service Caching And Computation Offloading In Digital Twin Cloud-Edge Networks, Jiayu Zheng, Zhuxue Mai, Zheyi Chen
Optimization Of Service Caching And Computation Offloading In Digital Twin Cloud-Edge Networks, Jiayu Zheng, Zhuxue Mai, Zheyi Chen
Journal of System Simulation
Abstract: In mobile edge computing (MEC), to satisfy diverse user demands by jointly optimizing service caching and computation offloading and address low-efficiency resource utilization caused by irrational resource allocation, this paper proposed a novel joint optimization of service caching and computation offloading with a convex-optimization-enabled deep reinforcement learning (JCO-CR) method. Additionally, a new model for digital twin cloud-edge networks (DTCEN) was constructed. The joint optimization of service caching and computation offloading was decoupled into two sub-problems, which were solved by an improved deep reinforcement learning method and convex optimization theory, respectively. Simulation experiments demonstrate that the proposed JCO-CR method …
Evolutionary Reinforcement Learning Based On Elite Instruction And Random Search, Jian Di, Xue Wan, Limei Jiang
Evolutionary Reinforcement Learning Based On Elite Instruction And Random Search, Jian Di, Xue Wan, Limei Jiang
Journal of System Simulation
Abstract: Evolutionary reinforcement learning currently suffers from low sample efficiency, a single coupling method, and poor convergence, which can affect its performance and scaling. To address this issue, an improved algorithm based on elite gradient instruction and double random search was proposed. The direction of the reinforcement strategy gradient update was corrected by introducing elite strategy gradient guidance carrying evolutionary information during reinforcement strategy training. Double stochastic search was used to replace the original evolutionary component, reducing the complexity of the algorithm while making the policy search meaningful and controllable in the parameter space. The introduction of complete replacement information …
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu
Journal of System Simulation
Abstract: To address the low planning efficiency, poor safety, and limited practicability of the RRT algorithm in global path planning within complex three-dimensional environments, which fail to meet the requirements of planning the safe flight path of UAVs, an improved SAC-RRT algorithm was proposed, which fused SAC deep reinforcement learning algorithm and RRT algorithm. A target point bias strategy and a dynamic step size based on the SAC decision-making network were designed to reduce the blindness of RRT. A random point correction process was designed to optimize the position of random points based on actions from the decision network and …
Optimization Dispatch Method For High-Proportion Renewable Energy Power Systems Based On Sc-Ppo, Zhongkai Xu, Chenyang Chu, Kai Xie, Ruizhuo Zhao, Wenjun Ke
Optimization Dispatch Method For High-Proportion Renewable Energy Power Systems Based On Sc-Ppo, Zhongkai Xu, Chenyang Chu, Kai Xie, Ruizhuo Zhao, Wenjun Ke
Journal of System Simulation
Abstract: The high proportion of renewable energy integration brings significant challenges of randomness, multi-objective coupling, and security constraints to power systems. Traditional model-driven methods have limitations in modeling accuracy and adaptability. To address these issues, this paper proposed a safety-constrained PPO algorithm (SC-PPO). The method included three improvements. A temporal convolutional network was utilized to construct a dynamic state encoder that integrated historical operation, real-time monitoring, and prediction data to form a causal state representation. A hierarchical reward structure was designed, and an adaptive weighting mechanism based on constraint satisfaction degree was introduced to coordinate multi-objective optimization. Physical constraint projection …
Solving The Vehicle Routing Problem Based On Deep Reinforcement Learning, Ming Jiang, Tao He
Solving The Vehicle Routing Problem Based On Deep Reinforcement Learning, Ming Jiang, Tao He
Journal of System Simulation
Abstract: The capacitated vehicle routing problem (CVRP) is a well-known combinatorial optimization challenge recognized as NP-hard due to its significant complexity. Building upon existing research, this paper introduces a novel end-to-end deep reinforcement learning approach based on a multi-pointer Transformer to tackle the CVRP. The proposed algorithm employs an invertible residual network in the encoder to encode input features, effectively reducing memory consumption. In the decoder, a multipointer network determines the probability distribution of solutions. To further enhance the performance of CVRP solutions, the algorithm leverages the symmetry in combinatorial optimization by implementing multi-trajectory parallel processing during both training …
Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang
Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang
Journal of System Simulation
Abstract: An improved A-DDQN algorithm is proposed to address the challenges of reward sparsity and the inability to distinguish sample importance in traditional DQN algorithms during robot path planning. Building on the original DQN, an enhancement is made by incorporating the Double-DQN approach, which updates the predictive Q-value network based on actions selected by the Q network, rather than directly using the predicted Q-values for action selection, thereby mitigating overestimation issues. Secondly, the concept of artificial potential field (APF) is introduced to design specific rewards for each step of the robot's movement, guiding the robot and addressing the problem of …
Research On Policy Representation In Deep Reinforcement Learning, Zhen Chen, Zhuoyi Wu, Lin Zhang
Research On Policy Representation In Deep Reinforcement Learning, Zhen Chen, Zhuoyi Wu, Lin Zhang
Journal of System Simulation
Abstract: Deep reinforcement learning (DRL) has achieved remarkable success in various domains. Nevertheless, existing policy networks in DRL still face significant challenges in areas such as generalizability, multi-task adaptability, and sample efficiency. Policy representation, as a crucial research direction for enhancing DRL capabilities, aims to improve an agent's adaptability to environmental changes and novel tasks by constructing more efficient and generalizable forms of policy expression. This paper provided a concise overview of key research advances in the field of policy representation. It introduced diverse policy architectures, ranging from traditional multi-layer perceptron (MLP) -based policies to those based on pointer networks, …
A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou
A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou
Journal of System Simulation
Abstract: Traditional quadrotor controllers, constrained by fixed model equation structures, encounter challenges in addressing control errors stemming from variations in parameters and environmental disturbances. This paper proposes a deep reinforcement learning solution for the quadrotor trajectory following control problem. We present the PPO-SAG algorithm incorporated into the PPO framework, utilizing adaptive mechanisms and PID expert knowledge to enhance training convergence and stability. Target functions incorporating distance constraint penalties and entropy policies are designed in alignment with the characteristics of the given problem. We also devise innovative disturbance-adaptive structures and trajectory feature selection mechanisms to augment control error information and extract …
Decision Modeling And Solution Based On Game Adversarial Complex Systems, Jiachen Jiang, Zhengxuan Jia, Zhao Xu, Tingyu Lin, Pengpeng Zhao, Yiming Ou
Decision Modeling And Solution Based On Game Adversarial Complex Systems, Jiachen Jiang, Zhengxuan Jia, Zhao Xu, Tingyu Lin, Pengpeng Zhao, Yiming Ou
Journal of System Simulation
Abstract: In view of the complex situation of the current game which will be large-scale, high-intensity, not omniscient, and strong confrontation, and in response to the lack of flexibility and long iteration cycles in traditional game decision-making, the model of the unmanned complex game system is built according to the background of the unmanned red and blue game. Based on deep reinforcement learning technology, intelligent decision-making algorithms are studied in the background of unmanned red and blue games. With the help of deep neural networks and Bellman's optimal principle, the search of the huge solution space is more efficient, and …
Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan
Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan
Journal of System Simulation
Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …
A Method For Key Node Identification In Operational Target System Based On War Gaming, Yongfu Zhang, Yang Liu, He Yuan
A Method For Key Node Identification In Operational Target System Based On War Gaming, Yongfu Zhang, Yang Liu, He Yuan
Journal of System Simulation
Abstract: The identification of key nodes in an operational target system is an important basis for combat command decision-making. Due to the lack of experimental verification of key node identification in the current operational target system in a campaign-level dynamic confrontation environment, a complex network model of operational target system with large-scale entities and complex interaction relationship was constructed by taking integrated air defense network as an example, with the help of the data derived from the large joint war gaming; the characteristics of wargame data were considered, and the value characteristics of combat targets and network structure characteristics were …
Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li
Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li
Journal of System Simulation
Abstract: Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the …
Co-Evolving Multi-Agent Transfer Reinforcement Learning Via Scenario Independent Representation, Ayesha Siddiqua
Co-Evolving Multi-Agent Transfer Reinforcement Learning Via Scenario Independent Representation, Ayesha Siddiqua
Graduate Theses/Dissertations
Multi-Agent Reinforcement Learning (MARL) addresses complex tasks involving cooperation and competition among agents, training them to develop optimal policies for collective goals. However, facilitating simultaneous learning for multiple agents is challenging because the complexity increases rapidly with the number of agents. Current methods encompass a variety of centralized, decentralized, semi-centralized, and hybrid approaches to balance the trade-offs between computational efficiency, scalability, and coordination in MARL. In this study, I employed a centralized training with semi-centralized execution (CTSCE) framework, utilize both local observations from agents and abstracted global observations to effectively train agents in cooperative environments. Additionally, earning complex, domain-specific tasks …
Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He
Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He
Journal of System Simulation
Abstract: Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing, a task scheduling method based on deep reinforcement learning is proposed. In multi-edge server scenario, a software-defined network-aided internet of vehicles task offloading system is built. On this basis, the task scheduling model of vehicle computation offloading is given. According to the characteristics of task scheduling, a scheduling method based on an improved pointer network is designed. Considering the complexity of task scheduling and computing resource allocation, the deep reinforcement learning algorithm is used to train the pointer network. The vehicle offloading …
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Journal of System Simulation
Abstract: Aiming at the autonomous decision-making difficulty of mobile picking robots in random and changeable complicated path environment during field operations, an autonomous obstacle avoidance path planning method based on deep reinforcement learning is propose. By setting the state space and action space and using the artificial potential field method to design the reward function, an obstacle penalty coefficient setting method based on collision cone collision avoidance detection is proposed to improve the autonomous collision avoidance ability. A virtual simulation system is constructed, in which the learning and training of the mobile picking robot is carried out and verified by …
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Journal of System Simulation
Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.
Intelligent Path Planning For Mobile Robots Based On Sac Algorithm, Laiyi Yang, Jing Bi, Haitao Yuan
Intelligent Path Planning For Mobile Robots Based On Sac Algorithm, Laiyi Yang, Jing Bi, Haitao Yuan
Journal of System Simulation
Abstract: Aiming at the high dimension, slow convergence and complex modelling of traditional path planning algorithms for mobile robots, a new intelligent path planning algorithm is proposed, which is based on deep reinforcement learning soft actor-critic (SAC) algorithm to save the poor performance of robot in complicated environments with static and dynamic obstacles. An improved reward function is designed to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority experience pool techniques. To evaluate the performance, a pygame-based simulation environment is constructed. Compared with proximal policy optimization(PPO) algorithm, experimental …
Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang
Joint Optimization Strategy Of Computing Offloading And Edge Caching For Intelligent Connected Vehicles, Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang
Journal of System Simulation
To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently …
Deep Reinforcement Learning-Based Control Strategy For Boost Converter, Yuxuan Dai, Chenggang Cui
Deep Reinforcement Learning-Based Control Strategy For Boost Converter, Yuxuan Dai, Chenggang Cui
Journal of System Simulation
Abstract: In view of the problems of model uncertainty and nonlinearity in bus voltage stability control of Boost converter, an intelligent control strategy based on model-free deep reinforcement learning(RL) is proposed. RL double DQN(DDQN) algorithm and deep deterministic policy gradient(DDPG) algorithm are used, and the Boost converter controller is designed. The state, action space, reward function, and neural network are also designed to improve the dynamic performance of the controller. The joint simulation of the Boost converter model and RL agent is realized by RL modelica(RLM …
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang
Journal of System Simulation
Abstract: Deep reinforcement learning is an agent modeling method with both deep learning feature extraction ability and reinforcement learning sequence decision-making ability, which can make up for the depleted non-stationary adaptation, complex feature selection and insufficient state-space representation ability of traditional opponent modeling. The deep reinforcement learning-based opponent modeling methods are divided into two categories, explicit modeling and implicit modeling, and the corresponding theories, models, algorithms and applicable scenarios are sorted out according to the categories. The applications of deep reinforcement learning-based opponent modeling techniques on different fields are introduced. The key problems and future development are summarized to provide …
Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang
Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang
Journal of System Simulation
Abstract: The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the …
A Devs-Based Formal Description Method For Complex Product Behavior Models, Qingquan Lin, Jiaran Yang, Heming Zhang
A Devs-Based Formal Description Method For Complex Product Behavior Models, Qingquan Lin, Jiaran Yang, Heming Zhang
Journal of System Simulation
Abstract: For the online optimization of pedestrian flow control in subway station, an algorithm frame for pedestrian flow control in subway station based on machine learning is designed. The pedestrian flow control process of a subway station during morning rush hour is selected,and the agent-based model is built to simulate the control process. The training data is collected through the multiple runs of the model, which is used as the input of deep reinforcement learning network, and the mature net is obtained through adequate training to provide the optimizing scheduling policy. Linking the actual data with the mature net …
Research On Optimization Of Airport Cargo Business Based On Deep Reinforcement Learning, Hongwei Wang, Peng Yang
Research On Optimization Of Airport Cargo Business Based On Deep Reinforcement Learning, Hongwei Wang, Peng Yang
Journal of System Simulation
Abstract: An intelligent agent technology architecture is adopted to the simulation model development of airport cargo business. Aiming at the optimization of airport cargo resources, a decision support system framework combining deep reinforcement learning (DRL) and airport cargo business simulation model is proposed. The simulated results are applied as the training data of the DRL network, and the DRL is used to optimize operation parameter of the simulation model. The mature system can be run online, which can provide optimized operation order in real time. In order to verify the effectiveness of the architecture, model development and experiments are conducted …
Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng
Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng
Journal of System Simulation
Abstract: To solve the difficulty in job scheduling in the complex and transient multi-user, multi-queue, and multi-data-center cloud computing environment, this paper proposed a job scheduling method based on deep reinforcement learning. A system model of cloud job scheduling and its mathematical model were built, and an optimization goal consisting of transmission time, waiting time, and execution time was obtained. A job scheduling algorithm based on deep reinforcement learning was designed, and its state space, action space, and reward function were given. A simulated cloud job scheduler was designed and developed, and simulated scheduling experiments were conducted on it. The …
A Data-Driven Modeling Method For Game Adversity Agent, Zeng Bi, Fang Xiao, Deshuai Kong, Xiangxiang Song, Zhengxuan Jia, Tingyu Lin
A Data-Driven Modeling Method For Game Adversity Agent, Zeng Bi, Fang Xiao, Deshuai Kong, Xiangxiang Song, Zhengxuan Jia, Tingyu Lin
Journal of System Simulation
Abstract: Aiming at the problems of collaborative modeling of formation behavior and intelligent generation of decision-making in complex confrontation scenarios, based on the serious game to simulate the confrontation scenarios of complex maritime equipment against the air, this paper proposes a data-driven modeling method for game agent and uses a distributed modeling technology of parallel adversarial scenarios and opportunistic decision making technology of smart targets to achieve agent modeling. It provides support for the further exploration of multi-objective collaborative modeling in complex confrontation scenarios. The simulation results show that deep reinforcement learning algorithms can provide a basis for the modeling …
Research On Generation Technology Of Computer Generated Force In Lvc Training System, Gao Ang, Zhiming Dong, Guohui Zhang, Liang Tao, Qisheng Guo
Research On Generation Technology Of Computer Generated Force In Lvc Training System, Gao Ang, Zhiming Dong, Guohui Zhang, Liang Tao, Qisheng Guo
Journal of System Simulation
Abstract: LVC training system of the combat equipment under the condition of confrontation is an effective means of training, aiming at the problem that in LVC training system, computer generated forces are difficult to meet the demand of training problems. The concept of LVC training and LVC training system is clarified, according to the relationship between model and system structure, the corresponding modeling technology requirements of three different hierarchical models, namely logical range entity configuration, command entity and combat entity, are expounded. According to the specific requirements, four computer-generated force generation methods are proposed, namely, logical target range virtual and …