Towards the knowledge-based multi-agent system identification

Chernyshov, K.R.

doi:10.1109/iciea.2015.7334146

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Meanwhile, there are also known approaches, for instance, [11][12][13][14] that attempted to apply consistent measures of dependence in the stochastic system identification, but containing numerous delusions considered in details in papers [15][16][17][18][19] and others.…”

Section: Discussionmentioning

confidence: 99%

“…In comparison to [6], condition (18) is more strict that that of presented in [6], namely: n X X , , 0 1 , may be equal to infinity. Simultaneously, condition (18) is more close to the same axiom of Rényi for the case of bivariate dependence.…”

mentioning

confidence: 85%

“…In comparison to [6], condition (19) is more strict that that of presented in [6], namely: n X X , , 1 (under meeting condition (20)), where the is defined within Axiom 2016 International Siberian Conference on Control and Communications (SIBCON) C. Simultaneously, condition (19) is more close to the same axiom of Rényi [2] for the case of bivariate dependence. As can be seen, condition (19) is directly coordinated with condition (18).…”

mentioning

confidence: 92%

“…Simultaneously, condition (21) is completely equivalent to the same axiom of Rényi [2] for the case of bivariate dependence. Obviously, as well as condition (19), condition (21) is directly coordinated with condition (18).…”

mentioning

confidence: 97%

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Consistent measures of dependence in system identification: Some summary

Chernyshov

2016

2016 International Siberian Conference on Control and Communications (SIBCON)

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In the paper, consistent measures of dependence of random values (the consistency means vanishing if and only if the random values are stochastically independent) are considered as a basic mathematical tool to solve stochastic system identification problems. Constructing consistent measures of dependence in the view of the Rényi axioms is considered. Particular attention is paid to the system identifiability and proper selection of model variables.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 85%

mentioning

confidence: 92%

mentioning

confidence: 97%

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Consistent measures of dependence in system identification: Some summary

Chernyshov

2016

2016 International Siberian Conference on Control and Communications (SIBCON)

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“…To this goal, the ℓ ∞ -norms of the blocks are penalized instead of their ℓ 1 -norms. One motivation behind employing this type of estimator stems from topology extraction in consensus networks, especially in the multi-agent setting [24], [25]. In this problem, given a number of subsystems (agents) whose interactions are defined via an unknown sparse topology network, the objective is to estimate the state-space model governing the entire system based on a limited number of input-output sample trajectories.…”

Section: Introductionmentioning

confidence: 99%

Sample Complexity of Sparse System Identification Problem

Fattahi¹,

Sojoudi²

2018

Preprint

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In this paper, we study the system identification problem for sparse linear time-invariant systems. We propose a sparsity promoting block-regularized estimator to identify the dynamics of the system with only a limited number of input-state data samples. We characterize the properties of this estimator under high-dimensional scaling, where the growth rate of the system dimension is comparable to or even faster than that of the number of available sample trajectories. In particular, using contemporary results on high-dimensional statistics, we show that the proposed estimator results in a small element-wise error, provided that the number of sample trajectories is above a threshold. This threshold depends polynomially on the size of each block and the number of nonzero elements at different rows of input and state matrices, but only logarithmically on the system dimension. A by-product of this result is that the number of sample trajectories required for sparse system identification is significantly smaller than the dimension of the system. Furthermore, we show that, unlike the recently celebrated least-squares estimators for system identification problems, the method developed in this work is capable of exact recovery of the underlying sparsity structure of the system with the aforementioned number of data samples. Extensive case studies on synthetically generated systems, physical mass-spring networks, and multi-agent systems are offered to demonstrate the effectiveness of the proposed method.

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Entropy Criteria in the Econometric Model Identification

Chernyshov

Jharko

2016

IFAC-PapersOnLine

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Towards the knowledge-based multi-agent system identification

Cited by 7 publications

References 17 publications

Consistent measures of dependence in system identification: Some summary

Consistent measures of dependence in system identification: Some summary

Sample Complexity of Sparse System Identification Problem

Entropy Criteria in the Econometric Model Identification

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