Direct Identification Method of Second Order Plus Time Delay Model Parameters

2001 ◽  
Vol 79 (7) ◽  
pp. 754-764 ◽  
Author(s):  
Chang Kyoo Yoo ◽  
Hee Jin Kwak ◽  
In-Beum Lee
1999 ◽  
Vol 32 (3) ◽  
pp. 288-294 ◽  
Author(s):  
Kyung Joo Chung ◽  
Hee Jin Kwak ◽  
Su Whan Sung ◽  
In-Beum Lee ◽  
Jin Yong Park

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xianqiang Yang ◽  
Hamid Reza Karimi

This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth; to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.


2013 ◽  
Vol 416-417 ◽  
pp. 822-833
Author(s):  
Qi Bing Jin ◽  
Si Nian Li ◽  
Qie Liu ◽  
Qi Wang

In this paper, a simple yet robust closed-loop identification method based on step response is presented. By approximating the process response firstly using Laguerre series expansions, a high-order process transfer function can be obtained. Then, a linear two-step reduction technique is used to reduce the high-order process to a second-order plus time delay model based on the frequency response data. This method is robust to measurement noise and it also does not need any numerical technique or iterative optimization. Simulation examples show the effectiveness of the proposed method for different process models. Comparison of identification performance between different methods is also illustrated in this work.


Transport ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 249-259 ◽  
Author(s):  
Sankaran Marisamynathan ◽  
Perumal Vedagiri

Enhancing pedestrian safety and improving the design standards of pedestrian facilities at signalized intersection requires a clear understanding of pedestrian delay model and pedestrian crossing behaviours under mixed traffic condition. The existing delay models do not consider the behavioural constrains of pedestrians. This research has been undertaken with the aim of developing a suitable pedestrian delay model for signalized intersection crosswalks, based on considering actual pedestrian crossing behaviours. The required model parameters were extracted from the video-graphic survey conducted for the selected four signalized intersections in Mumbai (India). Crossing behaviours of pedestrians were examined through field data in terms of pedestrian arrival pattern, crossing speed, compliance behaviour and pedestrian–vehicular interactions. Based on pedestrian crossing behaviour analysis results, two new pedestrian delay estimation models were developed and the models were validated by comparing with field and existing model values. The performance level of the proposed models is showing more precise and reliable solutions. The first pedestrian delay model is developed on the basis of compliance behaviour, has two components, such as waiting time delay and crossing time delay. This model can be used to evaluate pedestrian Level Of Service (LOS) and signal timing optimization. The second developed pedestrian delay model is based on noncompliance behaviour, has three components, such as waiting time delay, crossing time delay, and pedestrian–vehicular interaction delay. This model can also be used to evaluate the quality of pedestrian flow, estimating accurate pedestrian delay and LOS for local conditions, which is representative of the prevailing pedestrian condition.


1996 ◽  
Vol 29 (6) ◽  
pp. 990-999 ◽  
Author(s):  
Su Whan Sung ◽  
Jungmin O ◽  
In-Beum Lee ◽  
Jietae Lee ◽  
Seok-Ho Yi

2017 ◽  
Vol 40 (12) ◽  
pp. 3498-3506 ◽  
Author(s):  
Xianqiang Yang ◽  
Weili Xiong ◽  
Zeyuan Wang ◽  
Xin Liu

The joint parameter and time-delay estimation problems for a class of nonlinear multirate time-delay system with uncertain output delays are addressed in this paper. The practical process typically has time-delay properties and the process data are often multirate, sampled with output data inevitably corrupted by uncertain delays. The linear parameter varying (LPV) finite impulse response (FIR) multirate time-delay model is initially built to describe the considered system. The problems of over-parameterization and the existence of both continuous model parameters and discrete time-delays have made the conventional maximum likelihood difficult to solve the considered problems. In order to handle these problems, the joint parameter and time-delay estimation for the LPV FIR multirate time-delay model are formulated in the expectation-maximization scheme, and the algorithm to estimate the model parameters and time-delays is derived, simultaneously based on multirate process data. The efficacy of the proposed method is verified through a numerical simulation and a practical chemical plant.


2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.


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