This study compares an existing method with a novel approach for state estimation of Max-Plus Linear systems with bounded uncertainties. Traditional stochastic filtering does not apply to this system class, despite co...
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This study compares an existing method with a novel approach for state estimation of Max-Plus Linear systems with bounded uncertainties. Traditional stochastic filtering does not apply to this system class, despite computable posterior probability density function (PDF) support. Existing literature suggests a limited scalability disjunctive approach using difference-bound matrices. To overcome this, we study an alternative method recently investigated in Mufid et al. (2022) using Satisfiability Modulo Theory (SMT) techniques, which are known to be NP-hard. We propose a concise method that utilizes a pseudo-polynomial time algorithm using max-plus algebra. We evaluate its efficiency against SMT techniques through numerical experiments involving sparse matrix multiplications for enhanced computational speed.
Maximum correntropy criteria (MCC) has been exhibited a robustness against impulse noise by applying various area of signal process. MCC has been shown to be a rather robust adaption principle for adaptive system trai...
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ISBN:
(纸本)9781509012862
Maximum correntropy criteria (MCC) has been exhibited a robustness against impulse noise by applying various area of signal process. MCC has been shown to be a rather robust adaption principle for adaptive system training in the presence of heavy-tailed non Gaussian noises. fixed point algorithms converge to the optimum solution more quickly for fixed signals. In this paper shows convergence of a Dynamic Harmonic Balance algorithms with sufficient condition and comparison between fixed point algorithm and Dynamic Harmonic Balance algorithm.
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