In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an ar...In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.展开更多
Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the...Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.展开更多
基金National High Technology Research and Development Program of China(No.2007AA04Z109)Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology at Huazhong University of Science and Technology,China(No.DMETKF2009006)
文摘In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.
文摘Throughput prediction is essential for congestion control and LTE network management. In this paper, the autoregressive integrated moving average （ARIMA） model and exponential smoothing model are used to predict the throughput in a single cell and whole region in an LTE network. The experimental results show that these two models perform differently in both scenarios. The ARIMA model is better than the exponential smoothing model for predicting throughput on weekdays in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput on weekends in a whole region. The exponential smoothing model is better than the ARIMA model for predicting throughput in a single cell. In these two LTE network scenarios, throughput prediction based on traffic time series leads to more efficient resource management and better QoS.
