scholarly journals Clustering Input Signals Based Identification Algorithms for Two-Input Single-Output Models with Autoregressive Moving Average Noises

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Khalid Abd El Mageed Hag ElAmin
Keyword(s):  
Input Signal ◽  
Clustering Algorithm ◽  
Environmental Changes ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Autoregressive Moving Average ◽  
Input Output ◽  
Single Output ◽  
Input Signals

This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were supplied to the model in an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence (MSC) measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective.

scholarly journals Data Filtering Based Recursive Least Squares Algorithm for Two-Input Single-Output Systems with Moving Average Noises

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xianling Lu ◽  
Wei Zhou ◽  
Wenlin Shi
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Noise Model ◽  
Autoregressive Moving Average ◽  
Data Filtering ◽  
Noise Transfer Function ◽  
Single Output ◽  
Recursive Least Squares Method

This paper studies identification problems of two-input single-output controlled autoregressive moving average systems by using an estimated noise transfer function to filter the input-output data. Through data filtering, we obtain two simple identification models, one containing the parameters of the system model and the other containing the parameters of the noise model. Furthermore, we deduce a data filtering based recursive least squares method for estimating the parameters of these two identification models, respectively, by replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of its covariance matrices become small. The simulation results indicate that the proposed algorithm is effective.

scholarly journals A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Nannan Yu ◽  
Lingling Wu ◽  
Dexuan Zou ◽  
Ying Chen ◽  
Hanbing Lu
Keyword(s):  
Evoked Potential ◽  
Moving Average ◽  
Extraction Process ◽  
Autoregressive Moving Average ◽  
Single Trial ◽  
Model Driven ◽  
Single Output ◽  
Multiple Input ◽  
Novel Method ◽  
Electroencephalographic Activity

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

scholarly journals A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Za'er Abo-Hammour ◽  
Othman Alsmadi ◽  
Shaher Momani ◽  
Omar Abu Arqub
Keyword(s):  
Genetic Algorithm ◽  
Dynamical Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Input Output ◽  
Data Sequence ◽  
Output Data ◽  
Linear Dynamical Systems ◽  
Genetic Algorithm Approach

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

scholarly journals Identification Method of SUAV in Diving Phase Based on Flight Tests

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tieying Jiang ◽  
Junjie Yin ◽  
Chengwei Yang ◽  
Liang Jiang
Keyword(s):  
Mathematical Model ◽  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Flight Test ◽  
Installation Space ◽  
Flying Height ◽  
Autoregressive Moving Average ◽  
Identification Method ◽  
Aerial Vehicle

A mathematical model of the dive phase is an important research content for improving the accuracy of terminal control in the small unmanned aerial vehicle. The acquisition of the diving model poses new challenges, such as the small installation space, ultra-low flying height of small suicide drones, short flight time, strong coupling, less observable measurement, and elastic deformation of the wings during the drone dive phase. Based on the autoregressive moving average method, a multi-input multioutput noise term topology mathematical model is proposed in this paper. Through an improved least squares identification method, the diving model in the flight test is analyzed and verified. The identification results of the diving model obtained by the proposed method are compared with the least squares method dive model. The results indicate that the mathematical model and identification method proposed in this paper can effectively obtain the parameters of the drone dive model.

scholarly journals Online NARMAX model for electron fluxes at GEO

2015 ◽  
Vol 33 (3) ◽  
pp. 405-411 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings
Keyword(s):  
Electron Flux ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Earth Orbit ◽  
Output Data ◽  
Satellite Systems ◽  
Single Output ◽  
Average Electron ◽  
Solar Wind Parameters ◽  
Nonlinear Autoregressive

Abstract. Multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 MeV and > 2 MeV electron fluxes at geostationary Earth orbit (GEO). The NARMAX algorithm is able to identify mathematical model for a wide class of nonlinear systems from input–output data. The models employ solar wind parameters as inputs to provide an estimate of the average electron flux for the following day, i.e. the 1-day forecast. The identified models are shown to provide a reliable forecast for both > 0.8 and > 2 MeV electron fluxes and are capable of providing real-time warnings of when the electron fluxes will be dangerously high for satellite systems. These models, named SNB3GEO > 0.8 and > 2 MeV electron flux models, have been implemented online at http://www.ssg.group.shef.ac.uk/USSW/UOSSW.html.

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