Distributed Identification of Stable Large-Scale Isomorphic Nonlinear Networks Using Partial Observations Article Swipe
Chunhui Li
,
Chengpu Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.03216
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.03216
Distributed parameter identification for large-scale multi-agent networks encounters challenges due to nonlinear dynamics and partial observations. Simultaneously, ensuring the stability is crucial for the robust identification of dynamic networks, especially under data and model uncertainties. To handle these challenges, this paper proposes a particle consensus-based expectation maximization (EM) algorithm. The E-step proposes a distributed particle filtering approach, using local observations from agents to yield global consensus state estimates. The M-step constructs a likelihood function with an a priori contraction-stabilization constraint for the parameter estimation of isomorphic agents. Performance analysis and simulation results of the proposed method confirm its effectiveness in identifying parameters for stable nonlinear networks.
Related Topics
Concepts
Nonlinear system
Computer science
Identification (biology)
A priori and a posteriori
Constraint (computer-aided design)
Mathematical optimization
Scale (ratio)
Maximization
Stability (learning theory)
Particle filter
Expectation–maximization algorithm
Control theory (sociology)
Mathematics
Artificial intelligence
Maximum likelihood
Machine learning
Kalman filter
Control (management)
Statistics
Epistemology
Botany
Quantum mechanics
Biology
Geometry
Philosophy
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.03216
- https://arxiv.org/pdf/2401.03216
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390722842
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390722842Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2401.03216Digital Object Identifier
- Title
-
Distributed Identification of Stable Large-Scale Isomorphic Nonlinear Networks Using Partial ObservationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-06Full publication date if available
- Authors
-
Chunhui Li, Chengpu YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.03216Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.03216Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.03216Direct OA link when available
- Concepts
-
Nonlinear system, Computer science, Identification (biology), A priori and a posteriori, Constraint (computer-aided design), Mathematical optimization, Scale (ratio), Maximization, Stability (learning theory), Particle filter, Expectation–maximization algorithm, Control theory (sociology), Mathematics, Artificial intelligence, Maximum likelihood, Machine learning, Kalman filter, Control (management), Statistics, Epistemology, Botany, Quantum mechanics, Biology, Geometry, Philosophy, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approach, | 56 |
| abstract_inverted_index.consensus | 65 |
| abstract_inverted_index.filtering | 55 |
| abstract_inverted_index.networks, | 28 |
| abstract_inverted_index.networks. | 105 |
| abstract_inverted_index.nonlinear | 11, 104 |
| abstract_inverted_index.parameter | 1, 82 |
| abstract_inverted_index.stability | 19 |
| abstract_inverted_index.algorithm. | 48 |
| abstract_inverted_index.challenges | 8 |
| abstract_inverted_index.constraint | 79 |
| abstract_inverted_index.constructs | 70 |
| abstract_inverted_index.encounters | 7 |
| abstract_inverted_index.especially | 29 |
| abstract_inverted_index.estimates. | 67 |
| abstract_inverted_index.estimation | 83 |
| abstract_inverted_index.isomorphic | 85 |
| abstract_inverted_index.likelihood | 72 |
| abstract_inverted_index.parameters | 101 |
| abstract_inverted_index.simulation | 90 |
| abstract_inverted_index.Distributed | 0 |
| abstract_inverted_index.Performance | 87 |
| abstract_inverted_index.challenges, | 38 |
| abstract_inverted_index.distributed | 53 |
| abstract_inverted_index.expectation | 45 |
| abstract_inverted_index.identifying | 100 |
| abstract_inverted_index.large-scale | 4 |
| abstract_inverted_index.multi-agent | 5 |
| abstract_inverted_index.maximization | 46 |
| abstract_inverted_index.observations | 59 |
| abstract_inverted_index.effectiveness | 98 |
| abstract_inverted_index.observations. | 15 |
| abstract_inverted_index.identification | 2, 25 |
| abstract_inverted_index.uncertainties. | 34 |
| abstract_inverted_index.Simultaneously, | 16 |
| abstract_inverted_index.consensus-based | 44 |
| abstract_inverted_index.contraction-stabilization | 78 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |