This study provides a recursive parametric identification scheme for liquid-saturated steam heat exchanger system. The uses block-structured Wiener and Hammerstein models as model structure least squares estimation the parameter method. estimated block-oriented higher accuracy of than linear provided in literature. From simulation results, it is observed that can provide 88% goodness-of-FIT, wh...

In this paper we consider the problem of whether a nonlinear system has dynamic noise and then estimate the level of dynamic noise to add to any model we build. The method we propose relies on a nonlinear model and an improved least squares method recently proposed on the assumption that observational noise is not large. We do not need any a priori knowledge for systems to be considered and we ...

Very often, in the context of system identification, the error signal which is by definition the difference between the system and model filter outputs is assumed to be zero-mean, white, and Gaussian. In this case, the least squares estimator is equivalent to the maximum likelihood estimator and hence, it is asymptotically efficient. While this supposition is very convenient and extremely usefu...

In system identification, the true system is often known to be stable. However, due to finite sample constraints, modeling errors, plant disturbances and measurement noise, the identified model may be unstable. We present a constrained optimization method to ensure asymptotic stability of the identified model in the context of subspace identification methods. In subspace identification, we firs...

This paper proposes a non iterative algorithm for the identification of Hammerstein model, using the sampled output data obtained from the step response, giving a continuoustime model for the linear part and a point-wise estimation of the nonlinear one. Key in the derivation of the results is the algebraic derivative method in the frequency domain yielding exact formula in terms of multiple int...

This work focuses on friction and table mass identification using signal processing techniques. This report describes the system, data collection process and analysis of the data. Keywords—Friction Curve Identification, Table Mass Identification, Least Squares, Sampling Frequency, Butterworth Filter

Increased railway patronage worldwide is putting pressure on rolling stock and infrastructure to operate at higher capacity and with improved punctuality. Condition monitoring is seen as a contributing factor in enabling this and is highlighted here in the context of rolling stock being procured with high capacity data buses, multiple sensors and centralised control. This therefore leaves scope...

In vector autoregressive modeling, the order selected with the Akaike Information Criterion tends to be too high. This effect is called overfit. Finite sample effects are an important cause of overfit. By incorporating finite sample effects, an order selection criterion for vector AR models can be found with an optimal trade-off of underfit and overfit. The finite sample formulae in this paper ...

We propose and develop SG-ELM, a stable online learning algorithm based on stochastic gradients and Extreme Learning Machines (ELM). We propose SG-ELM particularly for systems that are required to be stable during learning; i.e., the estimated model parameters remain bounded during learning. We use a Lyapunov approach to prove both asymptotic stability of estimation error and boundedness in the...

We study the total least squares (TLS) problem that generalizes least squares regression by allowing measurement errors in both dependent and independent variables. TLS is widely used in applied fields including computer vision, system identification and econometrics. The special case when all dependent and independent variables have the same level of uncorrelated Gaussian noise, known as ordin...