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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu
Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu
Journal of System Simulation
Abstract: Aiming at the problem that the existing learning algorithms of Gaussian processes mixture (GPM) model, such as Markov Chain Monte Carlo (MCMC), variation or leave one out, have high computational complexity, a hidden variables posterior hard-cut iterative training algorithm is proposed, which simplifies the training process of the model. The GPM model based on the proposed algorithm is applied to chaotic time series prediction. The effects of embedding dimension, time delay, learning sample number, and testing sample numbers on predictive ability are discussed. It is demonstrated by the experimental results that the prediction of the GPM model is more …
Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang
Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang
Journal of System Simulation
Abstract: Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of …
Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo
Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo
Journal of System Simulation
Abstract: In this paper, we study the nonlinear time series prediction method for action capture. A prediction method based on the capture data is studied and implemented by analyzing human motion data to solve the data loss and correction problem caused by sensor failure. Based on this research purpose, the simulation experiment assumes that a sensor in the sequence of actions fails, then uses eight kinds of machine learning methods, and evaluates them with six indexes. The prediction results of different methods are compared and the predicted motions are visualized. Through the experiments, data prediction accuracy by random forest, decision …
Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi
Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi
Journal of System Simulation
Abstract: To the characteristics of multiple properties, hard to remove and high cost of time and manpower of aircraft electrical faults maintenance in aircraft maintenance of civil aviation, construction of intelligent aircraft electrical faults diagnosis system using RMBP neural network is proposed. RMBP algorithm is used to study sample data in the intelligent faults diagnosis system as it can overcome the faults of long time of convergence and easy to go into local minima of common BP algorithm, and is suitable for training large-scale neural network,. Experience data are collected, samples are made, samples training and experiment are carried out. …
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Journal of System Simulation
Abstract: The central heating system has complex structure, along with the characteristics of hysteresis, strong coupling and nonlinear. Contraposing the problem that the process is difficult to be identified and controlled by the mechanism modeling, an optimal control method of heat source total heat production based on machine learning is proposed. The heat source model of central heating system is established by BP neural network and long short-term memory neural network. Under the premise of meeting the demand of heating quality, with the total energy consumption as the optimization objective, the optimal control sequence of water supply temperature and water …