141 To generate identification data, we simulated this system using the feedback law u(t) = r(t) 0 (00:95q 02)y(t) = r(t) + 0:95y(t 0 2) (43) which places the closed-loop poles in 0.8618 and 0.6382. In the simulation we used independent, zero-mean, Gaussian white noise reference and noise signals fr(t)g and fe(t)g with variances 1 and 0.01, respectively. N = 200 data samples were used. In Table...

Identification of linear parameter-varying systems in an input-output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise. In the Box-Jenkins and output-error cases, it is shown that the currently available linear regression and instrumental variable methods from the literature are far from being optimal in terms of bias a...

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural...

The modeling process is to find a parametric model whose dynamic behavior close to that process. This model will be used to make predictions of the process output, or to simulate the process in a control system...etc. In this work we used RBF neural networks for modeling nonlinear systems. Generally the problem in neural networks is often to find a better structure. We propose in this work a me...

Abstract The objective of this research is to propose the appropriate forecasting models for analysis carbon dioxide emissions classified by major economic sectors in Thailand: power generation sector, transportation industrial and other sectors. twenty years monthly data from 2001 2020 was collected Energy Policy Planning Office, Ministry Energy. Box-Jenkins ARIMA approach used analysed order ...