{
  "id": "2401.03216",
  "version": "v1",
  "published": "2024-01-06T13:36:16.000Z",
  "updated": "2024-01-06T13:36:16.000Z",
  "title": "Distributed Identification of Stable Large-Scale Isomorphic Nonlinear Networks Using Partial Observations",
  "authors": [
    "Chunhui Li",
    "Chengpu Yu"
  ],
  "categories": [
    "math.DS"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-01-06T13:36:16.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stable large-scale isomorphic nonlinear networks",
      "partial observations",
      "multi-agent networks encounters challenges",
      "distributed identification",
      "yield global consensus state estimates"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}