1,721,086 research outputs found
Set membership identification of nonlinear systems
No full textWe investigate the problem of finding upper and lower bounds of a real valued function of several variables, on the base of a set of noise corrupted values of the function evaluated at a given set of variables and on some assumptions on function regularity and on noise bounds. Several set membership linear and nonlinear identification problems can be recast into the above problem. Two solutions are proposed. The first one is quite straightforward and leads to the definition of bounds that are the tightest ones but, in high dimensional spaces, computationally expensive. The second solution, relying on approximation properties of neural networks, leads to the evaluation of somewhat more conservative bounds, whose computational complexity is significantly lower than for the optimal bounds. A numerical example, related to the identification and prediction of a Lorenz chaotic system is presented to show the effectiveness of the proposed approach
Structured Set Membership identification of nonlinear systems with application to vehicles with controlled suspension
No full textComputation of local radius of information in SM-IBC-identification of nonlinear systems
No full textAbstractSystem identification consists in finding a model of an unknown system starting from a finite set of noise-corrupted data. A fundamental problem in this context is to asses the accuracy of the identified model. In this paper, the problem is investigated for the case of nonlinear systems within the Set Membership—Information Based Complexity framework of [M. Milanese, C. Novara, Set membership identification of nonlinear systems, Automatica 40(6) (2004) 957–975]. In that paper, a (locally) optimal algorithm has been derived, giving (locally) optimal models in nonlinear regression form. The corresponding (local) radius of information, providing the worst-case identification error, can be consequently used to measure the quality of the identified model. In the present paper, two algorithms are proposed for the computation of the local radius of information: The first provides the exact value but requires a computational complexity exponential in the dimension of the regressor space. The second is approximate but involves a polynomial (quadratic) complexity
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