scholarly journals Diffusion adaptive filtering algorithm based on the Fair cost function

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sihai Guan ◽  
Qing Cheng ◽  
Yong Zhao ◽  
Bharat Biswal
Keyword(s):  
Cost Function ◽  
Adaptive Filtering ◽  
Adaptive Estimation ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
Least Mean Square ◽  
Filtering Algorithm ◽  
Impulsive Interference ◽  
The Stability ◽  
M Estimate

AbstractTo better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.

scholarly journals Diffusion Adaptive Filtering Algorithm Based on the Fair Cost Function

2021 ◽  
Author(s):  
Sihai Guan ◽  
Qing Cheng ◽  
Yong Zhao ◽  
Bharat Biswal
Keyword(s):  
Cost Function ◽  
Adaptive Filtering ◽  
Adaptive Estimation ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
Network Nodes ◽  
Filtering Algorithm ◽  
Impulsive Interference ◽  
Mean Estimation ◽  
The Stability

Abstract To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the RDLMS, DNLMM, DGCLD, and DPLMS algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.

scholarly journals Robust Fast Adaptive Fault Estimation for T-S Fuzzy Markovian Jumping Systems with Mode-Dependent Time-Varying State Delays

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Chao Sun ◽  
Shengjuan Huang ◽  
Libing Wu ◽  
Suhuan Yi
Keyword(s):  
Adaptive Estimation ◽  
Estimation Error ◽  
External Disturbance ◽  
Estimation Algorithm ◽  
Fault Estimation ◽  
Time Varying ◽  
Markovian Jumping ◽  
Markovian Jumping Systems ◽  
Stochastically Stable ◽  
Estimation Scheme

This paper studies the problem of actuator fault estimation for a class of T-S fuzzy Markovian jumping systems, which is subject to mode-dependent interval time-varying delays and norm-bounded external disturbance. Based on the given fast adaptive estimation algorithm and by employing a novel Lyapunov–Krasovskii function candidate, a robust fault estimation scheme is proposed to estimate faults whose derivative is bounded. With this improved method, the proposed fault estimator minimizes the effect of disturbance on the estimation error and reduces the conservatism of systems stability results simultaneously. To be specific, an improved mode-dependent criterion for the existence of the fault estimation observer is established to guarantee the error dynamic system to be stochastically stable with a prescribed H ∞ performance and reduce the conservatism of designing procedure. Finally, three numerical examples are given to show the effectiveness of the proposed method.

scholarly journals Distributed Estimation in Periodically Switching Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jie Niu ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Estimation Error ◽  
Shortest Paths ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Time Period ◽  
Time Varying Networks ◽  
Weight Design ◽  
Error System ◽  
The Stability

This paper studies the distributed estimation problem of sensor networks, in which each node is periodically sensing and broadcasting in order. A consensus estimation algorithm is applied, and a weight design approach is proposed. The weights are designed based on an adjusting parameter and the nodes’ lengths of their shortest paths to the target node. By introducing a (T+2)-partite graph of the time-varying networks over a time period [0,T] and studying the relationships between the product of the time-sequence estimation error system matrices and the sequences of edges in the (T+2)-partite graph, a sufficient condition in terms of the observer gain and the adjusting parameter for the stability of the estimation error system is proposed. A simulation example is given to illustrate the results.

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