Towards Single-Input Single-Output Nonlinear System Identification and Signal Processing on Near-Term Quantum Computers

2019 ◽  
Author(s):  
Jiayin Chen ◽  
Hendra I. Nurdin ◽  
Naoki Yamamoto
Keyword(s):  
Signal Processing ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Quantum Computers ◽  
Single Input Single Output ◽  
Single Output ◽  
Single Input ◽  
Near Term

Assessment of a Robust Algorithm for the Detection and Diagnosis of Additive Faults

2006 ◽  
Author(s):  
Vasilis K. Dertimanis ◽  
Dimitris V. Koulocheris ◽  
Constantinos N. Spentzas
Keyword(s):  
System Identification ◽  
Input Output ◽  
Detection Problem ◽  
Identification Procedure ◽  
Single Input Single Output ◽  
Single Output ◽  
Robustness To Noise ◽  
Single Input ◽  
Detection And Diagnosis ◽  
Identification Techniques

This paper addresses the problem of additive faults (such as input/output sensor and actuator) in a dynamic system, from the view of system identification techniques. The relation between the residuals of the model–based fault diagnosis and the innovations of the system identification procedure is implemented and corresponding algorithms are extracted for the tracking of additive faults, while robustness to noise and disturbances is issued. The study is initiated using single input-single output models and extended to multiple inputs-multiple outputs structures. Furthermore, the detection problem of additive faults for systems with unobservable excitation is examined.

Nonlinear System Identification of a Refrigeration System

2016 ◽  
Vol 24 (04) ◽  
pp. 1650024 ◽  
Author(s):  
Tarcísio Soares Siqueira Dantas ◽  
Ivan Carlos Franco ◽  
Ana Maria Frattini Fileti ◽  
Flávio Vasconcelos da Silva
Keyword(s):  
System Identification ◽  
Analytical Modeling ◽  
Nonlinear System Identification ◽  
Least Square ◽  
Unknown Parameters ◽  
Average Output ◽  
Validation Data ◽  
Single Input Single Output ◽  
Point Tracking ◽  
Single Output

Applications of advanced control algorithms are important in the refrigeration field to achieve low-energy costs and accurate set-point tracking. However, the designing and tuning of control systems depend on dynamic mathematical models. Approaches like analytical modeling can be time-consuming because they usually lead to a large number of differential equations with unknown parameters. In this work, the application of system identification with the fast recursive orthogonal least square (FROLS) algorithm is proposed as an alternative to analytical modeling to develop a process dynamic model. The evaporating temperature (EVT), condensing temperature (CDT) and useful superheat (USH) are the outputs of interest for this system; covariance analysis of the candidate inputs shows that the model should be single-input–single-output (SISO). Good simulation results are obtained with two different validation data, with average output errors of 0.0343 (EVT model), 0.0079 (CDT model) and 0.1578 (USH model) for one of the datasets, showing that this algorithm is a valid alternative for modeling refrigeration systems.

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