Add to ORKGIn this paper we consider the problem of modeling noninertial processes with stochastic dependence between the input variables. Such processes are called H-processes ("tubular" structure processes). A new class of parametric identification algorithms with the indicator function of multidimensional static objects is suggested to use. The results of some computational experiments are presented
This contribution reviews theory, algorithms, and validation results for system identification of co...
We propose a framework for modeling structured nonlinear systems using nonparametric Gaussian proces...
This thesis is concerned with parametric modelling techniques based on the higher order statistics (...
In this paper we consider the problem of modeling noninertial processes with stochastic dependence b...
The paper considers a problem of the new class processes modeling with “tubular” structure in the s...
The report considers the case when multidimensional memoryless objects have an unknown stochastic de...
The problem of the discrete continuous processes having "tubular" structure in space "input-output" ...
The task of nonparametric identification of dynamic objects with discrete-continuous nature of the p...
Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала
The problem of data processing and modeling for discrete and continuous processes widely used in va...
The paper addresses the problem of non-parametric estimation of the static characteristic in Wiener ...
AbstractThe problem of multialternative recognition of nonstationary random processes is considered....
We consider the identification of a Markov process {W_t, X^(*)_t} when only {W_t} is observed. In s...
In this paper we present a non-parametric approach to identification in networks. The main advantage...
In this paper the identification problem is considered for initial conditions in a non-minimal state...
This contribution reviews theory, algorithms, and validation results for system identification of co...
We propose a framework for modeling structured nonlinear systems using nonparametric Gaussian proces...
This thesis is concerned with parametric modelling techniques based on the higher order statistics (...
In this paper we consider the problem of modeling noninertial processes with stochastic dependence b...
The paper considers a problem of the new class processes modeling with “tubular” structure in the s...
The report considers the case when multidimensional memoryless objects have an unknown stochastic de...
The problem of the discrete continuous processes having "tubular" structure in space "input-output" ...
The task of nonparametric identification of dynamic objects with discrete-continuous nature of the p...
Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала
The problem of data processing and modeling for discrete and continuous processes widely used in va...
The paper addresses the problem of non-parametric estimation of the static characteristic in Wiener ...
AbstractThe problem of multialternative recognition of nonstationary random processes is considered....
We consider the identification of a Markov process {W_t, X^(*)_t} when only {W_t} is observed. In s...
In this paper we present a non-parametric approach to identification in networks. The main advantage...
In this paper the identification problem is considered for initial conditions in a non-minimal state...
This contribution reviews theory, algorithms, and validation results for system identification of co...
We propose a framework for modeling structured nonlinear systems using nonparametric Gaussian proces...
This thesis is concerned with parametric modelling techniques based on the higher order statistics (...