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...

1 Abstract This paper presents and discusses the simulation of time stochastic ARMA systems and the development of an oo line identiication based on a prediction error method combined with exponential forgetting. The simulation involves diierent stages, starting with the simulation of the Gaussian white noise as an input to the original system and, the simulation with a combined signal and Gaus...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

This paper addresses the problem of identifying echelon canonical forms for a vector autoregressive moving average model with exogenous variables using finite algorithms. For given values of the Kronecker indices a method for estimating the structural parameters of a model using ordinary least squares calculations is presented. These procedures give rise, rather naturally, to a technique for th...

The vision-guided robot is intensively embedded in modern industry, but it still a challenge to track moving objects real time accurately. In this paper, hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) proposed for dynamic visual tracking of the manipulator. extracts line features on image plane based laser-camera system ...