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Articles 1 - 21 of 21
Full-Text Articles in Physical Sciences and Mathematics
Simulation And Research Of Manipulator Motion Strategy Based On Adaptive Dynamic Programming, Ming Li, Qun Xu, Yan Wang, Zhicheng Ji
Simulation And Research Of Manipulator Motion Strategy Based On Adaptive Dynamic Programming, Ming Li, Qun Xu, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: Aiming at the difficulty of manipulator to realize high-precision motion tracking in complex and harsh environment, a strategy method based on the combination of adaptive dynamic programming (ADP) and sliding mode admittance control is proposed. The unknown environment is modeled as a linear model and based on quasi, a sliding mode admittance controller is derived to resist disturbance interference. An optimal control method that combines ADP with sliding mode admittance controller is proposed, in which the definition of R-matrix in value function is optimized and improved to further improve the tracking accuracy. The neural network based on ADP is …
Identification Of Switching Operation Based On Lstm And Moe, Xiaoqing Zhang, Wanfang Xiao, Yingjie Guo, Bowen Liu, Xuesen Han, Jingwei Ma, Gao Gao, He Huang, Shihong Xia
Identification Of Switching Operation Based On Lstm And Moe, Xiaoqing Zhang, Wanfang Xiao, Yingjie Guo, Bowen Liu, Xuesen Han, Jingwei Ma, Gao Gao, He Huang, Shihong Xia
Journal of System Simulation
Abstract: Aiming at the individual differences of different personnel in the same operation and differences of the same person in the same operation at different times, a switching operation recognition model(MoE-LSTM) based on Mixture of experts model (MOE) and long short-term memory network(LSTM) is proposed. Based on MoE, LSTM is integrated to learn the feature distribution of different sources data. The acceleration data is collected to build the switching operation dataset and the action sequence is segmented and aligned based on sliding window. The action sequence is input to MoE-LSTM, and the temporal dependencies of different actions are independently learned …
Multi-Uavs 3d Path Planning Method Based On Random Strategy Search, Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu
Multi-Uavs 3d Path Planning Method Based On Random Strategy Search, Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu
Journal of System Simulation
Abstract: In view of the difficulty of the traditional path planning method without energy consumption constraints to meet the emergency rescue requirements in the complex mountain operation environment, a three-dimensional path planning algorithm for multi-UAVs is proposed based on LSTM-DPPO(long short-term memory-distributed proximal policy optimization) framework. The LSTM long and short-term memory neural network is used to extract the important characteristic state information sequence of the multiple unmanned aerial vehicles in their respective flight process. After repeated iteration and updating, an optimal network parameter model is obtained. Combined with the energy consumption, the optimal 3D detection path is generated. …
Neural Network Model Of Information Fusion For Coal Storage And Kinetic Energy Of Ball Mill, Bai Yan, He Fang
Neural Network Model Of Information Fusion For Coal Storage And Kinetic Energy Of Ball Mill, Bai Yan, He Fang
Journal of System Simulation
Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency …
Neural Network Inverse Control For The Output Voltage Of Energy Storage Inverter In Micro-Grid, Weiliang Liu, Yongjun Lin, Changliang Liu, Wenying Chen, Liangyu Ma
Neural Network Inverse Control For The Output Voltage Of Energy Storage Inverter In Micro-Grid, Weiliang Liu, Yongjun Lin, Changliang Liu, Wenying Chen, Liangyu Ma
Journal of System Simulation
Abstract: In order to improve the output voltage waveform quality of energy storage inverter in micro-grid, an inverse control method was proposed based on BP neural network. Mathematical model of the energy storage inverter was established, and the main factors affecting the output voltage were analyzed, and then the expansion inverse model of the system was established based on BP neural network. In order to overcome the local optimum disadvantage in BP training algorithm, gravity algorithm was adopted to optimize the network initial parameters. The neural network inverse model was put in series with its original model to form a …
Soft Sensor Of Particle Size Of Grinding Process Based On Improved Csapso Neural Networks, Zhou Ying, Huimin Zhao, Chen Yang, Wang Long
Soft Sensor Of Particle Size Of Grinding Process Based On Improved Csapso Neural Networks, Zhou Ying, Huimin Zhao, Chen Yang, Wang Long
Journal of System Simulation
Abstract: Aiming at the problems that the particle size can’t be measured online and the offline analysis by lab sample existing in large-time delay, by combining the characteristics of the one stage grinding circuit, the soft sensor model of particle size was proposed by the combination of improved chaotic self-adaptive particle swarm optimization and BP neural network algorithm. Taking advantages of chaotic theory ergodicity and PSO global optimal searching ability, the algorithm above couldadjust the weights of BP network adaptively and avoid falling into the local optimum. As a result of MATLAB simulation, the measurement accuracy of the improved CSAPSO-BP …
Uav Takeoff Decision Based On Neural Network Model Of Takeoff Capability, Yongtao Peng, Yueping Wang, Xiaoting Wang
Uav Takeoff Decision Based On Neural Network Model Of Takeoff Capability, Yongtao Peng, Yueping Wang, Xiaoting Wang
Journal of System Simulation
Abstract: To enhance the safety in case of engine flameout failure, a new type of UAV takeoff decision based on neural network capacity model was proposed. Two capacity parameters of takeoff safety in case of engine flameout failure were defined, one is the maximum velocity for a safe takeoff and the other is the minimum velocity for a safe shut down. A calculation method based on iterative simulations for those parameters under multiple flight conditions was introduced. Double layer neural networks were used to model the relationship between flight conditions and the capacity parameters, to realize the compressive storage and …
Boiler Combustion Optimization Based On Bayesian Neural Network And Genetic Algorithm, Haiquan Fang, Huifeng Xue, Li Ning, Fei Xi
Boiler Combustion Optimization Based On Bayesian Neural Network And Genetic Algorithm, Haiquan Fang, Huifeng Xue, Li Ning, Fei Xi
Journal of System Simulation
Abstract: Neural network and genetic algorithm have been extensively used in boiler combustion optimization problems. But the traditional Back Propagation neural network's generalization ability is poor. The Bayesian regularization can improve the neural network's generalization ability. A boiler combustion multi-objective optimization method combining Bayesian regularization BP neural network and genetic algorithm （Bayes NN-GA）was researched. A number of field test data from a boiler was used to simulate the Bayesian neural network model. The results show that the thermal efficiency and NOx emissions predicted by the Bayesian neural network model show good agreement with the measured, and the optimal results show …
Application Of Pso-Bp Algorithm In Hydraulic System Fault Diagnosis, Handong Zhang, Liusong Tao
Application Of Pso-Bp Algorithm In Hydraulic System Fault Diagnosis, Handong Zhang, Liusong Tao
Journal of System Simulation
Abstract: It is of great significance to monitor, forecast and diagnose hydraulic systems’ fault timely and accurately. First, this paper describes the basic fault model theoretical knowledge of BP neural neystem failure neural network modeling has created and simulated. PSO-BP neural network has been raised, this paper has established PSO optimize model of the BP neural system fault diagnosis. BP network has been created and simulated in Plunger pump hydraulic system failure. The correct results indicate that this mixed PSO-BP algorithm is better than the improved BP algorithm, and can meet the requirements of Hydraulic system fault diagnosis.
Two Power Sliding Mode Neural Network Compensation Control For Space Robot After Target Capturing, Cheng Jing, Chen Li
Two Power Sliding Mode Neural Network Compensation Control For Space Robot After Target Capturing, Cheng Jing, Chen Li
Journal of System Simulation
Abstract: The impact analyses of space robot capturing a target and stability control problem in the post-impact process were discussed. The dynamic models of space robot system and target were derived by multi-body theory. The impact effect of rigidcouplingmodel was analyzed by applying geometric relationship and principle of momentum conservation. Atwo power sliding mode neural network control scheme was proposed for the combined system after acquiring with uncertain system parameters and external disturbance. The convergence speed of the control system was guaranteed by applyingtwo power sliding mode reaching raw, and the uncertain part was compensated by using neural …
Adaptive Control For Hydraulic Servo Position System With Bounded Input, Jianfei Shi, Shujuan Yi
Adaptive Control For Hydraulic Servo Position System With Bounded Input, Jianfei Shi, Shujuan Yi
Journal of System Simulation
Abstract: An adaptive state feedback controller based on neural network fitting was proposed for hydraulic servo position systems containing parameter uncertainties, external disturbance and bounded input problem. Taking the saturation characteristic into account sufficiently, the adaptive state feedback trajectory tracking controller was designed with an adaptive law to real-timely adjust the disturbance parameters and the bounded hyperbolic tangent functions to promise the bounded of the control law. Moreover, the complete stability and performance analysis were presented using Lyapunov theory. Simulation results show the effectiveness of the designed controller for the trajectory tracking in the present of actuators saturation.
Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren
Multi-Objective Optimization Design Of Aerodynamic Layout For Twin Swept-Wing Aircraft, Yuchang Lei, Dengcheng Zhang, Yanhua Zhang, Guangxu Su, Luo Hao, Zhan Ren
Journal of System Simulation
Abstract: Multi-objective optimization of aerodynamic layout is a key technology in the design of vehicles. The overall configuration of the shape parameters is optimized with a double swept-shaped wave shape as the basic configuration. We use NSGA-Ⅱ multi-objective genetic algorithm, take the aircraft double sweep angle as the design variable, consider the maximum takeoff weight, range, volume ratio and other performance indicators, use Elman neural network to establish the relationship between shape parameters and performance parameters, and establish constraints based on mission planning requirements. The Pareto optimal solution set is obtained by using optimized design and the individuals with …
Robot Arm Control Method Based On Deep Reinforcement Learning, Heyu Li, Zhilong Zhao, Gu Lei, Liqin Guo, Zeng Bi, Tingyu Lin
Robot Arm Control Method Based On Deep Reinforcement Learning, Heyu Li, Zhilong Zhao, Gu Lei, Liqin Guo, Zeng Bi, Tingyu Lin
Journal of System Simulation
Abstract: Deep reinforcement learning continues to explore in the environment and adjusts the neural network parameters by the reward function. The actual production line can not be used as the trial and error environment for the algorithm, so there is not enough data. For that, this paper constructs a virtual robot arm simulation environment, including the robot arm and the object. The Deep Deterministic Policy Gradient (DDPG),in which the state variables and reward function are set,is trained by deep reinforcement learning algorithm in the simulation environment to realize the target of controlling the robot arm to move the gripper below …
Identification And Prediction Of Room Temperature Delay Neural Network Model For Vav Air Conditioning, Xiuming Li, Jili Zhang, Tianyi Zhao, Tingting Chen
Identification And Prediction Of Room Temperature Delay Neural Network Model For Vav Air Conditioning, Xiuming Li, Jili Zhang, Tianyi Zhao, Tingting Chen
Journal of System Simulation
Abstract: Aiming at the problem of mathematical description for dynamic response characteristic of indoor temperature time-delay system, the fundamental principle of neural network model identification is introduced in regulation process of variable air volume (VAV) air conditioning system. Considering the model structure of Elman neural network, this paper presents an optimal selection algorithm for layer delay coefficient in order to determine delay time between indoor temperature and regulation parameters; and a multiple-step prediction model of indoor temperature time-delay system based on Elman neural network is built. The effectiveness of the proposed method is validated through the simulation experiment.
New Clock-Driven Algorithm Based On Separation Of Synaptic Conductance Computation, Zhijie Wang, Peng Xia, Han Fang, Xiaochun Gu
New Clock-Driven Algorithm Based On Separation Of Synaptic Conductance Computation, Zhijie Wang, Peng Xia, Han Fang, Xiaochun Gu
Journal of System Simulation
Abstract: In order to reduce the computing time when simulating the biologic neural network, an efficient clock-driven algorithm based on the separation of synaptic conductance computation is presented. It is found that the calculation of the synaptic state variables can be separated into two independent parts: one called conductance coefficient related with the pre-synaptic neuron, and the other called synaptic current. By introducing the data structure of the virtual synapse cluster to storing sequences of synaptic conductance coefficient, the former part can be calculated independently according to the spiking states of pre-synaptic neuron at each time step. When calculating the …
Artificial Fish Swarm And Feedback Linearization Of Flue Gas Denitration Control Based On Neural Network, Yuguang Niu, Pan Yan, Wenyuan Huang
Artificial Fish Swarm And Feedback Linearization Of Flue Gas Denitration Control Based On Neural Network, Yuguang Niu, Pan Yan, Wenyuan Huang
Journal of System Simulation
Abstract: According to the present situation of SCR flue gas dentration control system in thermal power plant, an optimum proposal that control valve and concentration transmitter are added in the inlet of the SCR reactor is presented, and the corresponding control strategy is given. At the entrance of the SCR reactor, the receding horizon algorithm combined with the single neuron adaptive algorithm and the artificial fish swarm algorithm (RSNAAFS) is used to control branch valves to pretreat NOX in the exhaust flue gas. At the outlet of the SCR reactor, the neural network based on feedback linearization algorithm (NNFL) …
Research On Interaction Model Of Hand Tracking Based On Cognitive Theory, Shaobai Zhang, Zhang Teng
Research On Interaction Model Of Hand Tracking Based On Cognitive Theory, Shaobai Zhang, Zhang Teng
Journal of System Simulation
Abstract: This paper aims to solve the problems in a vector integration to endpoint (VITE) model of human reaching and grasping under perturbations of object size, distance and orientation. We discuss how to reduce the numbers of disturbances of three main kinds of components: hand/wrist transport, grip aperture and hand orientation. Based on the achievements of cognitive psychology, and a tracking and cognitive model for operational 3D gestures, this paper proposes a new divide-and-conquer model that is used for indicating current grasping status and to trigger three main kinds of methods of when to start or stop working. The model …
Error Estimation For Material Simulation Data Based On Hybrid Learning Algorithm, Wang Juan, Xiaoyu Yang, Zongguo Wang, Ren Jie, Xushan Zhao
Error Estimation For Material Simulation Data Based On Hybrid Learning Algorithm, Wang Juan, Xiaoyu Yang, Zongguo Wang, Ren Jie, Xushan Zhao
Journal of System Simulation
Abstract: In order to obtain high quality material simulation data from Density Functional Theory material calculation software package, a modeling method based on BP neural network was proposed to build model estimating the error of material simulation data. A novel hybrid algorithm combining simple particle swarm optimization algorithm that excludes speed item with BP algorithm, also referred to tsPSO-BP, was proposed to optimize the connection weights of the BP neural network. The hybrid learning algorithm not only makes use of strong global searching ability of the PSO, but also strong local searching ability of the BP algorithm. The BP …
Prediction Of Aircraft Cabin Energy Consumption Based On Improved Cooperative Pso Neural Network, Xiuyan Wang, Yanmin Liu, Gewen Zhang, Zongshuai Li, Jiaquan Lin
Prediction Of Aircraft Cabin Energy Consumption Based On Improved Cooperative Pso Neural Network, Xiuyan Wang, Yanmin Liu, Gewen Zhang, Zongshuai Li, Jiaquan Lin
Journal of System Simulation
Abstract: To correctly evaluate the energy needs of the aircraft cabin and to predict the energy consumption of the aircraft cabin with higher accuracy, an energy consumption prediction method based on improved particle swarm optimization (PSO) neural network algorithm parameters is proposed. The method combines the cooperative particle swarm optimization algorithm with chaotic particle swarm optimization algorithm. On the basis of cooperative particle swarm optimization algorithm chaos theory is introduced. Continuous search ability by using chaos optimization method to overcome the collaborative optimization algorithm is easy to fall into the local extremum problem. The parameters of the neural network can …
Optimization Via Simulation Based On Neural Network, Shihui Wu, Xiaodong Liu, Shao Yue, Zhang Fa, Minxiang Yang
Optimization Via Simulation Based On Neural Network, Shihui Wu, Xiaodong Liu, Shao Yue, Zhang Fa, Minxiang Yang
Journal of System Simulation
Abstract: To improve the efficiency of optimization via simulation (OvS), an OvS method based on neural network is proposed. Taking advantage of the approximation ability of neural network to nonlinear input-output relationship, neural network's outputs are used as substitutes for simulation results to reduce the required simulation runs. Samples are generated by simulation according to the three proposed samples selection methods. Owning to its advantages on learning speed, network stability and parameters selection, generalized regression neural network (GRNN) is adopted to train the samples. The trained GRNN forms a regression surface that represents the relationship between simulation inputs and outputs, …
Multi-Response Parameters Optimization Based On Pca And Neural Network, Jianli Yu, Hongqi Huang, Manxiang Miao
Multi-Response Parameters Optimization Based On Pca And Neural Network, Jianli Yu, Hongqi Huang, Manxiang Miao
Journal of System Simulation
Abstract: A multi-response parameters optimization method based on principal component analysis (PCA) and neural network is proposed. It is used to optimize temperature and time parameters in complex thermal polymerization process. By using the method of weighted PCA, two response indexes, capacity value and loss tangent value, are converted into a single quality performance index. The main effect value is used to identify the search range. The radical basis function (RBF) neural network model is established to search and identify the optimal process parameters. Results show that response indexes are improved and the optimization effect is obvious. Therefore, this study …