Distributed Online System Identification for LTI Systems Using Reverse Experience Replay

• URL: http://arxiv.org/abs/2207.01062v1
• Date: Sun, 3 Jul 2022 15:03:38 GMT
• Title: Distributed Online System Identification for LTI Systems Using Reverse Experience Replay
• Authors: Ting-Jui Chang and Shahin Shahrampour
• Abstract summary: We study distributed online system identification of linear time-invariant (LTI) systems over a multi-agent network. We propose DSGD-RER, a distributed variant of the SGD-RER algorithm, and theoretically characterize the improvement of the estimation error with respect to the network size.
• Score: 14.924672048447334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identification of linear time-invariant (LTI) systems plays an important role in control and reinforcement learning. Both asymptotic and finite-time offline system identification are well-studied in the literature. For online system identification, the idea of stochastic-gradient descent with reverse experience replay (SGD-RER) was recently proposed, where the data sequence is stored in several buffers and the stochastic-gradient descent (SGD) update performs backward in each buffer to break the time dependency between data points. Inspired by this work, we study distributed online system identification of LTI systems over a multi-agent network. We consider agents as identical LTI systems, and the network goal is to jointly estimate the system parameters by leveraging the communication between agents. We propose DSGD-RER, a distributed variant of the SGD-RER algorithm, and theoretically characterize the improvement of the estimation error with respect to the network size. Our numerical experiments certify the reduction of estimation error as the network size grows.

Related papers

• ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling [57.91760520589592]
  Scaling network depth has been a central driver behind the success of modern foundation models.<n>This paper revisits the default mechanism for deepening neural networks, namely residual connections.<n>We introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data.
  arXiv  Detail & Related papers  (2026-02-09T18:54:18Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Online Multi-modal Root Cause Analysis [61.94987309148539]
  Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
  arXiv  Detail & Related papers  (2024-10-13T21:47:36Z)
• CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning [52.72888626663642]
  Copy-move forgery detection aims at detecting duplicated regions in a suspected forged image. Deep learning based copy-move forgery detection methods are in the ascendant. We propose a Transformer-style copy-move forgery network named as CMFDFormer. We also provide a novel PCSD continual learning framework to help CMFDFormer handle new tasks.
  arXiv  Detail & Related papers  (2023-11-22T09:27:46Z)
• Online Self-Supervised Deep Learning for Intrusion Detection Systems [1.2952596966415793]
  This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection.
  arXiv  Detail & Related papers  (2023-06-22T16:46:35Z)
• Faulty Branch Identification in Passive Optical Networks using Machine Learning [0.0]
  Passive optical networks (PONs) have become a promising broadband access network solution. PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Machine learning (ML) based approaches have shown great potential for managing optical faults in PON systems.
  arXiv  Detail & Related papers  (2023-04-03T20:59:16Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Regret Analysis of Distributed Online LQR Control for Unknown LTI Systems [8.832969171530056]
  We study the distributed online linear quadratic regulator (LQR) problem for linear time-invariant (LTI) systems with unknown dynamics. We propose a distributed variant of the online LQR algorithm where each agent computes its system estimate during an exploration stage. We prove that our proposed algorithm scales $tildeO(T2/3)$, implying the consensus of the network over time.
  arXiv  Detail & Related papers  (2021-05-15T23:02:58Z)
• State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks [8.673621107750652]
  Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper formulates a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE) Two deep neural networks (DNNs) are trained to operate in a sequential manner for implementing TI and DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs)
  arXiv  Detail & Related papers  (2021-04-15T02:46:50Z)
• Non-Episodic Learning for Online LQR of Unknown Linear Gaussian System [0.0]
  We propose an online non-episodic algorithm that gains knowledge about the system from a single trajectory. We characterize the almost sure convergence rates of identification and control, and reveal an optimal trade-off between exploration and exploitation.
  arXiv  Detail & Related papers  (2021-03-24T15:51:28Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Resilient Identification of Distribution Network Topology [0.0]
  This paper develops a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Having a low computational burden, this approach is fast-track and can be applied in real-time applications.
  arXiv  Detail & Related papers  (2020-11-16T14:23:56Z)
• Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth [51.40441097625201]
  We propose an end-to-end trainable deep learning architecture for sparse signal recovery problems. The proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase.
  arXiv  Detail & Related papers  (2020-10-29T06:32:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.