Communication-Reducing Algorithm of Distributed Least Mean Square Algorithm with Neighbor-Partial Diffusion

With the development of distributed algorithms, many researchers are committed to the goal of maintaining the long-term stability of the network by reducing the communication cost. However, many algorithms that lessen communication costs often result in a significant decrease in estimation accuracy. In order to reduce the communication cost with less performance degradation, the distributed neighbor-partial diffusion least-mean-square algorithm (NPDLMS) is proposed in this paper. Besides, considering the data redundancy in the network, we offer the distributed data selection NPDLMS algorithm, which further improves the estimation accuracy and reduces the communication cost. In the performance analysis, the stability and the communication cost of the algorithms are given.

[1]  Zhaoyang Zhang,et al.  Diffusion Information Theoretic Learning for Distributed Estimation Over Network , 2013, IEEE Transactions on Signal Processing.

[2]  Ali H. Sayed,et al.  Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.

[3]  Shukai Duan,et al.  Diffusion least logarithmic absolute difference algorithm for distributed estimation , 2018, Signal Process..

[4]  Chunguang Li,et al.  Distributed Sparse Total Least-Squares Over Networks , 2015, IEEE Transactions on Signal Processing.

[5]  Alejandro Ribeiro,et al.  Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals , 2008, IEEE Transactions on Signal Processing.

[6]  Marc Moonen,et al.  Consensus-Based Distributed Total Least Squares Estimation in Ad Hoc Wireless Sensor Networks , 2011, IEEE Transactions on Signal Processing.

[7]  Yan Wang,et al.  A novel two-stage ellipsoid filtering-based system modeling algorithm for a Hammerstein nonlinear model with an unknown noise term , 2019 .

[8]  Ioannis D. Schizas,et al.  Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation , 2009, IEEE Transactions on Signal Processing.

[9]  Ali H. Sayed,et al.  Incremental Adaptive Strategies Over Distributed Networks , 2007, IEEE Transactions on Signal Processing.

[10]  Dimitri P. Bertsekas,et al.  Incremental Subgradient Methods for Nondifferentiable Optimization , 2001, SIAM J. Optim..

[11]  Xiaoxue Zhao,et al.  Mechanical-electromagnetic coupling elastic vibration instability of symmetrical three-phase external rotor induction motor , 2019, Nonlinear Dynamics.

[12]  Feng Ding,et al.  Multi-step-length gradient iterative algorithm for equation-error type models , 2018, Syst. Control. Lett..

[13]  Nanning Zheng,et al.  Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion , 2015, IEEE Signal Processing Letters.

[14]  Biao Huang,et al.  Variational Bayesian approach for ARX systems with missing observations and varying time-delays , 2018, Autom..

[15]  Hui Zhang,et al.  Active Steering Actuator Fault Detection for an Automatically-Steered Electric Ground Vehicle , 2017, IEEE Transactions on Vehicular Technology.

[16]  Hong Chen,et al.  A New Delay-Compensation Scheme for Networked Control Systems in Controller Area Networks , 2018, IEEE Transactions on Industrial Electronics.

[17]  Hongjing Liang,et al.  Event-Triggered Fault Detection and Isolation of Discrete-Time Systems Based on Geometric Technique , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[18]  Okyay Kaynak,et al.  Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond , 2018, IEEE Access.

[19]  Kostas Berberidis,et al.  Distributed Incremental-Based LMS for Node-Specific Adaptive Parameter Estimation , 2014, IEEE Transactions on Signal Processing.

[20]  Badong Chen,et al.  Kernel Kalman Filtering With Conditional Embedding and Maximum Correntropy Criterion , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.

[21]  Soummya Kar,et al.  Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise , 2007, IEEE Transactions on Signal Processing.

[22]  Paulo S. R. Diniz,et al.  On Data-Selective Adaptive Filtering , 2018, IEEE Transactions on Signal Processing.

[23]  Ali H. Sayed,et al.  Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks , 2011, IEEE Transactions on Signal Processing.

[24]  Dimitri P. Bertsekas,et al.  A New Class of Incremental Gradient Methods for Least Squares Problems , 1997, SIAM J. Optim..

[25]  Chunguang Li,et al.  Distributed Robust Optimization in Networked System , 2017, IEEE Transactions on Cybernetics.

[26]  Robert D. Nowak,et al.  Quantized incremental algorithms for distributed optimization , 2005, IEEE Journal on Selected Areas in Communications.

[27]  Nanning Zheng,et al.  Generalized Correntropy for Robust Adaptive Filtering , 2015, IEEE Transactions on Signal Processing.

[28]  Azam Khalili,et al.  Partial-Diffusion Least Mean-Square Estimation Over Networks Under Noisy Information Exchange , 2015, ICWMC 2015.

[29]  Shukai Duan,et al.  Diffusion generalized maximum correntropy criterion algorithm for distributed estimation over multitask network , 2018, Digit. Signal Process..

[30]  Ali H. Sayed,et al.  Diffusion Bias-Compensated RLS Estimation Over Adaptive Networks , 2011, IEEE Transactions on Signal Processing.

[31]  Kang Hao Cheong,et al.  Periodic habitat destruction and migration can paradoxically enable sustainable territorial expansion , 2019, Nonlinear Dynamics.

[32]  Marc Moonen,et al.  Low-Complexity Distributed Total Least Squares Estimation in Ad Hoc Sensor Networks , 2012, IEEE Transactions on Signal Processing.

[33]  Ali H. Sayed,et al.  Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data , 2011, IEEE Transactions on Signal Processing.

[34]  Shen Yin,et al.  Recursive Total Principle Component Regression Based Fault Detection and Its Application to Vehicular Cyber-Physical Systems , 2018, IEEE Transactions on Industrial Informatics.

[35]  Hongjing Liang,et al.  Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization , 2020, IEEE Transactions on Cybernetics.

[36]  Isao Yamada,et al.  Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis , 2010, IEEE Transactions on Signal Processing.

[37]  Sergios Theodoridis,et al.  Adaptive Robust Distributed Learning in Diffusion Sensor Networks , 2011, IEEE Transactions on Signal Processing.

[38]  Zhaoyang Zhang,et al.  Diffusion Sparse Least-Mean Squares Over Networks , 2012, IEEE Transactions on Signal Processing.

[39]  Xiaodan Shao,et al.  Broken-motifs diffusion LMS algorithm for reducing communication load , 2017, Signal Process..

[40]  Dong In Kim,et al.  Optimal Energy Management Policy of Mobile Energy Gateway , 2016, IEEE Transactions on Vehicular Technology.

[41]  Yih-Fang Huang,et al.  Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion , 2014, IEEE Transactions on Signal Processing.

[42]  Yih-Fang Huang,et al.  Distributed Least Mean-Square Estimation With Partial Diffusion , 2014, IEEE Transactions on Signal Processing.

[43]  Hui-Liang Shen,et al.  Distributed Learning of Predictive Structures From Multiple Tasks Over Networks , 2017, IEEE Transactions on Industrial Electronics.

[44]  Milos S. Stankovic,et al.  Decentralized Parameter Estimation by Consensus Based Stochastic Approximation , 2011, IEEE Trans. Autom. Control..

[45]  Yan Wang,et al.  A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data , 2017, J. Frankl. Inst..

[46]  Benoît Champagne,et al.  A diffusion LMS strategy for parameter estimation in noisy regressor applications , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).