Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
| Autor: | Janetta Culita, Dan Stefanoiu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
stochastic cubic spline
0209 industrial biotechnology Polynomial Computer science hill climbing 02 engineering and technology lcsh:Chemical technology Biochemistry Least squares ARMAX identification models Article Analytical Chemistry 020901 industrial engineering & automation Real-time Control System 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Metaheuristic metaheuristics System identification 020206 networking & telecommunications Atomic and Molecular Physics and Optics Nonlinear system Spline (mathematics) Data point Autoregressive model Hill climbing Algorithm Smoothing |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 5038, p 5038 (2020) Sensors Volume 20 Issue 18 |
| ISSN: | 1424-8220 |
| Popis: | This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal-to-noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification. |
| Databáze: | OpenAIRE |
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