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Multi-objective linear-programming-based four-judgment algorithm for linear bounded noise system modeling
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DOI：10.1016/j.jfranklin.2020.02.041.png)
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Abstract
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A multi-objective linear-programming-based four judgment modeling algorithm is proposed for an unknown but bounded noise system. Because there is no prior knowledge about the bounded noise term, during each recursive step, the noise signal is warped in a strip and the hyperplanes can be obtained by samples of input and output signals. The feasible parameter set of a linear discrete-time system with bounded noise, viewed as a convex polytope, is transformed into a polyhedral cone with increasing parameter dimension. One of the vertices of the polyhedral cone is the origin, and the polyhedral vertices can be calculated when the polyhedral cone edge vectors are determined. Moreover, by adopting the multi-objective linear programming idea, a four-judgment modeling algorithm is proposed for linear discrete-time systems. The given simulations illustrate the feasibility and effectiveness of the given algorithm. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
STATE ESTIMATION
PARAMETER-ESTIMATION
SET
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