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An improved interacting multiple model algorithm with adaptive transition probability matrix based on the situation
Two transition correction functions are proposed for the interaction multiple models (IMM) to
adaptively update the Markov transition probability matrix (TPM) according to the situation …
adaptively update the Markov transition probability matrix (TPM) according to the situation …
On the Gaussian Filtering for Nonlinear Dynamic Systems Using Variational Inference
This paper introduces a new variational Gaussian filtering approach for estimating the state
of a nonlinear dynamic system. We first assume that the predictive distribution of the state is …
of a nonlinear dynamic system. We first assume that the predictive distribution of the state is …
On the fixed-interval smoothing for jump Markov nonlinear systems
This paper considers the fixed-interval smoothing for jump Markov systems. An optimal
backward-time recursive equation for computing the joint posterior of the state vector and …
backward-time recursive equation for computing the joint posterior of the state vector and …
On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing
The quotient of two multivariate Gaussian densities can be written as an unnormalized
Gaussian density, which has been applied in some recently developed multiple-model fixed …
Gaussian density, which has been applied in some recently developed multiple-model fixed …
A two-stage transition correction function for adaptive markov matrix in imm algorithm
This paper proposes two probability correction functions to make adaptively the transition
probability matrix (TPM). In a traditional interacting multiple model (IMM) estimator, TPM is …
probability matrix (TPM). In a traditional interacting multiple model (IMM) estimator, TPM is …