Abstract
A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.
Highlights
Simulation, design, and process control tasks in engineering require the knowledge of the mathematical model of the controlled system
Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream
Artificial Intelligence (AI) and Machine Learning (ML) methods have arisen as a panacea for overcoming the model-building difficulties when the vast amount of monitored data is already available from the subject system
Summary
For real-time data analysis, regression and autocorrelation methods may compare and compete favorably both in efficiency and evaluation time for dynamic, predictive model identification. The aims at the development of a new dynamic system model are: 1) data compression without information loss; 2) processing speed increase in model identification; and 3) accuracy improvement for short-term forecasting. Such demands (1)-(3) have arisen, e.g., for forward predicting and controlling atmospheric conditions in the hazardous workplace environment for workers’ safety and health. The dynamic model will use the characteristic values of the groups of data as multivariate inputs kept in the time interval bins. Application examples will complete the study to show the operator model’s performance to complement or surpass those of other ML techniques including NN
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