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Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional.(More)
We illustrate the power of combining semi-physical and neural network modeling in an application example. It is argued that some of the problems related to the use of neural networks, such as high dimensionality of the parameter space and problems with local minima, can be alleviated using this approach. 1. INTRODUCTION System identiication as described by,(More)
The use of periodic excitation signals in identiication experiments is advocated. With periodic excitation it is possible to separate the driving signals and the disturbances , which for instance implies that the noise properties can be independently estimated. In the paper a non-parametric noise model, estimated directly from the measured data, is used in(More)
  • Urban Forssell, G Le Rtek N Ik A U T O M At Ic Co N, T R O L, To Susanne, Tea
  • 1997
System identiication deals with the construction of mathematical models of dy-namical systems using measured data. Closed-loop identiication is what results when performing the identiication experiment under output feedback, that is, in closed loop. In this thesis we study a number of closed-loop identiication methods , both classical and more recently(More)