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- W9752124 abstract "This paper presents identification analysis problem respect to first-order stochastic invariant time with unknown parameter evolution, expressed it in finite difference within limit velocity time. Requiring in this case the dynamical interaction between two filters (estimation and identification structures), giving both a good enough convergence rate respect to the real observable signal system. The identification filter gain K as second probability moment error, resulted as difference between the signal model system and its identification results, achieving greater convergence without lost the natural time interval interaction, minimizing the error trajectories respect to proposed model considering in it, dynamical estimation respect to unknown parameters. Emphasizing the dynamics filter interaction, using the stochastic gradient filter method as estimator into first order identification filter. The identification filter model based in the black box scheme didn't use the transition matrix because didn't know the dynamical parameter condition; although in ideal description require it. Therefore, in the real sense is better to use dynamical estimator inside it as to see in the concepts developed below. This dynamical interaction could be used in tracking paths, navigation systems and gravimetric measures, description, reconstruction and information systems, requiring its operations to describe internal parameters and states, respectively. The natural stochastic system evolution shines without lost its internal properties." @default.
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- W9752124 date "2009-02-21" @default.
- W9752124 modified "2023-09-23" @default.
- W9752124 title "Identification first order stochastic system with etimation parameters: recursive description" @default.
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