The Sequential Fusion Estimation Algorithms Based on Gauss-Newton Method Over Multi-Agent Networked Systems

Wu, Mou; Zhong, Liangji; Tan, Liansheng; Xiong, Naixue

doi:10.1109/access.2020.3003984

Cited by 7 publications

(6 citation statements)

References 40 publications

(63 reference statements)

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“…where α represents the learning rate and is set to 0.35, as in [39][40][41]. Owing to the learning effect, lim t⟶∞ p i,j,t � 0 and p i,j,t < p * i,j,t ≤ 1 for all t * < t < ∞ and 0 < p i,j,t , in accordance with Equation (6). Note that 0 ≤ p i,j,t � p * i,j,t ≤ 1 for all t * and t if there is no learning effect.…”

Section: Learning Effectmentioning

confidence: 99%

“…e foregoing observation is useful, and, accordingly, we must focus on the nodes without the same neighbors. For example, nodes 1, 3, and 4 all have the same neighbors, that is, 6, 7}, and we can search for the state vectors and calculate the probability for node 1 because nodes 3 and 4 are identical.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Almost all individuals and industries constantly rely on computer and network technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. us, the types of computer viruses are becoming more diversified, and the intensity of the attacks is increasing.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Spread Probability of Learning‐Effect Computer Virus

Yeh

Lin

Huang³

2021

Complexity

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With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.

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Section: Learning Effectmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

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Predicting Spread Probability of Learning‐Effect Computer Virus

Yeh

Lin

Huang³

2021

Complexity

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“…For example in computer vision based on a distributed camera sensor network [27], many problems such as image alignment, face alignment, or camera calibration can be posed as solving such nonlinear optimization problem, where sensor nodes cooperate to minimize the overall residual between the source and target intensity images. In some decentralized network optimization tasks, for example power system state estimation in smart grids [28], [29] and fusion estimation in multi-agent systems [30], they are typically formulated as a NLLS problem across network.…”

Section: B Extension To Distributed Optimization Problemsmentioning

confidence: 99%

A Consensus-Based Diffusion Levenberg-Marquardt Method for Collaborative Localization With Extension to Distributed Optimization

Zhong

et al. 2020

IEEE Access

Self Cite

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Non-linear least squares problems arise from data fitting have received recently a lot of attention, particularly for the estimates of the model parameters over networked systems. Although the diffusion Gauss-Newton method offers many advantages for solving the non-linear least squares problem in wireless sensor network to estimate target position parameter, there are some key challenges when applying it to practice, including singularity of Gauss-Newton Hessian, selection to constant step sizes and steady state oscillation. These remaining issues lead to obvious performance degradation such as high computational cost, vulnerability to step size change and resulting instability on estimation.In this paper, to eliminate the singularity, we develop a diffusion Levenberg-Marquardt method such that the problem of constant step size is also addressed together. Then, to reach agreement on estimated vector, a consensus implementation is further proposed, thus eliminating the oscillation during steady state. Consequently, the proposed consensus-based diffusion Levenberg-Marquardt method provides a general solution for the non-linear least squares problems with an objective that takes the form of a sum of squared residual terms. By applying to collaborative localization and distributed optimization arise in large scale machine learning, simulation results confirm the effectiveness and wide applicability of proposed method in terms of convergence rate, accuracy and consistency of estimates.

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“…. , i r , and the probability of state transition in step k is [21]. (4) e state transition matrix is built.…”

Section: Markov Prediction Modelmentioning

confidence: 99%

Forecast and Analysis of Coal Traffic in Daqin Railway Based on the SARIMA-Markov Model

Zhang

Liu

2020

Journal of Advanced Transportation

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With the continuous advancement of China’s supply-side structural reform, the country’s energy consumption structure has undergone considerable changes, including an overall reduction in fossil energy use and a rapid increase in clean energy application. In the context of China’s coal overcapacity, port and rail capacities are difficult to change in the short term. This study forecasts the monthly coal traffic of Daqin Railway on the basis of the seasonal autoregressive integrated moving-average Markov model and then uses the monthly coal transport data of this railway from September 2009 to November 2019 as samples for model training and verification. Coal traffic from December 2019 to September 2020 is accurately predicted. This study also analyzes the effects of China’s industrial structure adjustment, clean energy utilization, and low-carbon usage on the coal transport volume of Daqin Railway. In addition, the characteristics of seasonal fluctuation and the development trend of Daqin Railway’s coal traffic are explored. This study provides a reference for adjusting the train operation chart of Daqin Railway’s coal transport and developing a special coal train operation plan. It can determine the time of coal transport peak warning, improve the efficiency of coal transport management, and eventually realize a reasonable allocation of resources for Daqin Railway.

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The Sequential Fusion Estimation Algorithms Based on Gauss-Newton Method Over Multi-Agent Networked Systems

Cited by 7 publications

References 40 publications

Predicting Spread Probability of Learning‐Effect Computer Virus

Predicting Spread Probability of Learning‐Effect Computer Virus

A Consensus-Based Diffusion Levenberg-Marquardt Method for Collaborative Localization With Extension to Distributed Optimization

Forecast and Analysis of Coal Traffic in Daqin Railway Based on the SARIMA-Markov Model

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