The identification of a dynamic, nonlinear model of human ankle stiffness is considered in a minimum mean squared error framework. The model consists of two parallel pathways, one representing the intrinsic dynamics, the other representing the reflex contribution to the stiffness. The model is shown to be linear in all of its parameters, except for those used to describe a single static nonline...

This paper deals with a method for the parameter identification of an improved dynamic model of a photovoltaic (PV) source, which encompasses a capacitor and an inductor. The method is based on piecewise-linear (PWL) circuit approximation and step load responses. Considering the PV I-V characteristic as a PWL function, if a transient occurs in a linear region, the PV source can be approximated ...

Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified str...

The Preisach hysteresis model has been adopted extensively in magnetic and smart materialbased systems. Fidelity of the model hinges on accurate identification of the Preisach density function. Existing work on the identification of the density function usually involves applying an input that provides sufficient excitation and measuring a large set of output data. In this paper, we propose a no...

Abaract--Equation error (or linear regression) models are known to inherently require the a priori choice for specific signal variables to be considered as regressand and/or regressor. This implies that a model set should be--a priori--restricted in some way in order to define an acceptable identification problem. In the case of approximate identification (i.e. the system to be modelled is not ...

This paper deals with the design and identification of the dynamic model of a compactor, an articulated frame steering mobile engine for use in road construction. A classical approach based on an Ordinary Least Squares identification method is used. A survey of these techniques is given and applied to estimate dynamic parameters of the compactor, and especially the contribution of the contact s...

This paper proposes a procedure for transfer function identification (specification) based on least-squares estimates of transfer function weights using the original or filtered series. The corner method is then used to identify a parsimonious rational form of the transfer function. The procedure is illustrated in a simulated example; it is shown how this straightforward approach outperforms ot...

This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the `1-regularization. The regularization constant regulates compl...