Abstract
This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different algorithms. In this paper, we aim to perform the model identification and time delay estimation simultaneously from a limited number of observations. For an MISO-FIR system with many inputs and unknown input time delays, the corresponding identification model contains a large number of parameters, requiring a great number of observations for identification and leading to a heavy computational burden. Inspired by the compressed sensing (CS) recovery theory, a threshold orthogonal matching pursuit algorithm (TH-OMP) is presented to simultaneously identify the parameters, the orders and the time delays of the MISO-FIR systems. The proposed algorithm requires only a small number of sampled data compared to the conventional identification methods, such as the least squares method. The effectiveness of the proposed algorithm is verified by simulation results.
Highlights
IntroductionTime delay is an unavoidable behavior in their dynamical models
In many industry processes, time delay is an unavoidable behavior in their dynamical models.For example, in a chemical industry, analyzers need a certain amount of time to process an analysis, which is used as a part of the control loop [1]
For a multiple input single output finite impulse response (MISO-FIR) system with many inputs and unknown input time delays, the parameterized model contains a large number of parameters, requiring a great number of observations and a heavy computational burden for identification
Summary
Time delay is an unavoidable behavior in their dynamical models. System identification is an effective way to build mathematical models of dynamical systems from observed input-output data Conventional identification methods, such as the least squares methods [2], the stochastic gradient methods [3] and the maximum likelihood estimation methods [4], usually require a large number of sampled data and that the model structures, including time delays, are a priori known [5,6]. For a multiple input single output finite impulse response (MISO-FIR) system with many inputs and unknown input time delays, the parameterized model contains a large number of parameters, requiring a great number of observations and a heavy computational burden for identification. We will consider identifying the parameters, orders and time delays of the MISO-FIR systems from a small number of noisy measured data.
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