A novel recursive learning identification scheme for Box–Jenkins model based on error data

Li, Linwei; Zhang, Huanlong; Ren, Xuemei; Zhang, Jie

doi:10.1016/j.apm.2020.08.076

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The error data in MSD can generally be divided into two categories: intrinsic error and systematic error [36]. There are four main types of system errors, namely table tennis switching, missing, repeated positioning and drift data [37].…”

Section: B Design Of Stop Point Recognition and Construction Of Dtprp...mentioning

confidence: 99%

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Cui,

Zhao

2024

IEEE Access

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To reduce traffic congestion, it is particularly important to use advanced technology to predict urban traffic flow. Therefore, a dynamic traffic pattern prediction model is proposed, which includes convolutional neural network, long and short term memory network and attention mechanism. The validity of the prediction model is verified by the loss function and the average absolute percentage error. In addition, the study also constructs a model for user travel pattern and parking point recognition based on deep learning and mobile signaling data. The performance of the recognition model is verified by the accuracy and other indicators. The research outcomes demonstrated that the max average absolute percentage error of the dynamic traffic mode prediction model was 7.8%, and the mini value was 2.9%. The average accuracy of the user travel pattern recognition model was 83.34%, and that of the parking point recognition model was 88.56%.The dynamic traffic model recognition and prediction model designed by the research institute has better results, and has practical guiding significance in smart city traffic management.

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Section: B Design Of Stop Point Recognition and Construction Of Dtprp...mentioning

confidence: 99%

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Cui,

Zhao

2024

IEEE Access

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“…It can be seen that Equations ( 18) and ( 17) are comparable and not much different. Matlab System Identification Toolbox also provides other methods such as Output-Error, ARX, Box-Jenkins etc [39], [40], [41], [42]. Figure 8, 9, and 10 show simulation results for the original model (Equation 17) and the model identified from System Identification Toolbox -SIT (Equation 18).…”

Section: B Dynamic Study Of Continuous Processmentioning

confidence: 99%

Dynamic Study and PI Control of Milk Cooling Process

Agustriyanto¹,

Mochni²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The background of this research is to understand the operation process, which is the main goal of developing the process model. This model is often used for operator training, process design, safety system analysis, or control system design. The dynamic model of the milk cooling process from 36˚C to 4˚C using chilled water available at 2˚C was performed. Chilled water was maintained at a constant temperature by using a refrigerant unit. The process being investigated was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang -Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2-hr cooling process, the gain and time constant values are 1.00 and 42.3548 mins, respectively. Heat balance was then extended to continuous processes so that its transfer function could also be obtained. This study simulated and investigated the behavior of batch and continuous processes. Process Identification via input-output data was also introduced for continuous process. The process model obtained from the system identification toolbox was very useful in control, such as for determining tuning parameters via the Ziegler-Nichol method for Proportional-Integral control. However, a small delay was required to be introduced to the system as the first order process without time Ziegler Nichol method cannot be implemented. Further research may include other system identification methods, such as ARX, ARMAX, Output-Error, Box Jenkins etc., or implementing advanced process control for milk cooling.

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A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise

Zhang

et al. 2022

Applied Mathematical Modelling

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A novel recursive learning identification scheme for Box–Jenkins model based on error data

Cited by 6 publications

References 38 publications

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Dynamic Study and PI Control of Milk Cooling Process

A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise

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