Identification of symmetric noncausal processes

Lu, Qiugang; Loewen, Philip D.; Gopaluni, R. Bhushan; Forbes, Michael G.; Backström, Johan U.; Dumont, Guy A.; Davies, M. S.

doi:10.1016/j.automatica.2019.01.035

Cited by 1 publication

(3 citation statements)

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“…Theorem 3.1: Consider the system (1) with measured output (5), and {u(t)} given by (9). Then, for any integers M nc , M c such that M nc + M c ≥ 0, we have ρN a.s.…”

Section: A Non-causal Least-squares Estimatormentioning

confidence: 99%

“…These systems have been used for generating the bandlimited equivalent impulse responses in Figures 1 and 2. The sampling periods are h 1 = 0.3[s] and h 2 = 1[s] respectively, and the inputs to G * 1 (p) and G * 2 (p) are given by (9), where {e(kh)} is white noise of unit variance. The noiseless continuous-time output is simulated by oversampling the input by a factor of 100, and assuming a firstorder hold behavior.…”

Section: Simulationsmentioning

confidence: 99%

“…The non-causal discrete-time equivalent system description leads to direct ways to estimate the non-causal impulse response, and therefore the continuous-time frequency response, based on sampled band-limited data. Non-causal system identification has been recently studied in [9], in which a Maximum Likelihood estimator is proposed for symmetric non-causal systems with applications to cross direction modeling of paper machines. In particular, noncausal FIR models have been used for identifying systems in closed-loop in [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations

González

Rojas

Hjalmarsson

2021

2021 60th IEEE Conference on Decision and Control (CDC)

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In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods. One common input behavior assumption is that the spectrum of the input is band-limited. The sinc interpolation property of these input signals yields equivalent discrete-time representations that are non-causal. This observation, often overlooked in the literature, is exploited in this work to study non-parametric frequency response estimators of linear continuous-time systems. We study the properties of non-causal least-square estimators for continuous-time system identification, and propose a kernelbased non-causal regularized least-squares approach for estimating the band-limited equivalent impulse response. The proposed methods are tested via extensive numerical simulations.

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“…Theorem 3.1: Consider the system (1) with measured output (5), and {u(t)} given by (9). Then, for any integers M nc , M c such that M nc + M c ≥ 0, we have ρN a.s.…”

Section: A Non-causal Least-squares Estimatormentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%