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Consensus Learning for distributed Fuzzy Neural Network in Big Data Environment

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第1期5卷 29-41页

作者： Shi, Ye Lin, Chin-Teng Chang, Yu-Cheng Ding, Weiping Shi, Yuhui Yao, Xin Univ Technol Sydney Sch Comp Sci Ctr Artificial Intelligence CIBCI Lab Broadway NSW 2007 Australia Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Uncertainty and distributed nature inherently exist in big data environment. distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.

关键词： Fuzzy neural networks Big Data Uncertainty Computational modeling distributed algorithms Clustering algorithms Fuzzy logic Big data distributed fuzzy neural network (D-FNN) consensus learning distributed structure learning distributed parameter learning

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distributed Fixed-Time Optimization in Economic Dispatch Over Directed Networks

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第5期17卷 3011-3019页

作者： Dai, Hao Jia, Jinping Yan, Li Fang, Xinpeng Chen, Weisheng Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China Tianshui Normal Univ Sch Math & Stat Tianshui 741001 Peoples R China Xidian Univ Xian 710071 Peoples R China

A distributed algorithm is presented in this article under directed communication networks, which is used to solve the economic dispatch problem in fixed time in smart grid systems. A new globally fixed-time stability theory (Lemma 3) is first given in this article, which contains a new upper bound for the estimation of the settling time. Moreover, the fixed-time convergence for the proposed algorithm is rigorously proved with the aid of convex optimization theory, the new lemma, and Lyapunov stability theory under the strongly connected and weight-balanced network topology. Finally, numerical simulations show the effectiveness and advantages of the distributed fixed-time optimization algorithm.

关键词： Optimization Economics Smart grids Convex functions Informatics Communication networks distributed algorithms Convex optimization directed network distributed algorithm equality constraint fixed-time convergence

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distributed Dominating Sets in Interval Graphs 28th

Distributed Dominating Sets in Interval Graphs

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28th International Computing and Combinatorics Conference (COCOON)

作者： Gorain, Barun Mondal, Kaushik Pandit, Supantha Indian Inst Technol Bhilai Raipur Chattisgarh India Indian Inst Technol Ropar Rupnagar Punjab India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India

ISBN: (纸本)9783031221040;9783031221057

The Minimum Dominating Set (MMDS) and Minimum Connected Dominating set (MMCDS) problems are well-studied in the distributed communities due to their numerous applications across the field. These problems are also crucial in wireless ad hoc networks, mainly for the particular type of geometric graphs. We study these problems in geometric graphs such as interval and unit interval graphs. We exploit the underlying geometric structures of these graph classes and present either constant factors distributed algorithms in constant rounds or algorithms with matching lower bounds in the LOCAL communication model.

关键词： Minimum dominating set Minimum connected dominating set distributed algorithms Interval graphs Approximation algorithms

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Penalty Removal Search Algorithm for distributed Optimization of Nonconvex Functions in Economic Dispatch

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Ge, Yiyang Wang, Zhanshan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China

Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To address the above problem, a penalty removal search algorithm (PRSA) is proposed for ED nonconvex optimization. It is composed of two distributed optimization algorithms embedded in a reinforcement learning framework. In Phase I of PRSA, a distributed optimization algorithm with projection operators is designed. It uses fewer variables to locate the region where the optimal solution belongs by the cooperative Q-learning. In Phase II of PRSA, the sigmoid function serves as a penalty function to form the second distributed optimization algorithm. This is used to skip the searched solutions and allow the algorithm to continue searching for more feasible solutions. The PRSA solves the problem that the algorithm misses feasible solutions when the nonconvex coefficients increase. Finally, the effectiveness of the PRSA is verified by numerical examples.

关键词： Linear programming Economics distributed algorithms Cost function Q-learning Germanium Costs Valves Fuels Convex functions Collaborative Q-learning (CQL) algorithm distributed optimization economic dispatch (ED) nonconvex optimization penalty removal search algorithm (PRSA)

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distributed Optimization for Integrated Frequency Regulation and Economic Dispatch in Microgrids

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IEEE TRANSACTIONS ON SMART GRID 2021年第6期12卷 4595-4606页

作者： Xu, Yiqiao Dong, Zhen Li, Zhongguo Liu, Yixing Ding, Zhengtao Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

In hierarchically controlled microgrids, economic dispatch and corresponding real-time control action are inherently performed at different timescales. Owing to the uncertainty in renewables, running economic dispatch in advance may not adequately guarantee the anticipated outcomes. In this paper, distributed optimization algorithms are developed to manage economic dispatch and frequency regulation in microgrids concurrently, leaving the inter-layer coordination across timescales unnecessary. We first formulate a distributed economic dispatch problem that minimizes the overall opportunity cost yielded by renewable energy sources participating in frequency regulation. Then, by considering the dynamical characteristics of inverter-based microgrids, we reconstruct the optimization problem from the perspective of feedback control design utilizing local frequency feedback to perceive and compensate supply-demand imbalance in an online fashion, despite resistance losses. As a result, the proposed algorithms can settle the optimal dispatch and restore the frequency to its nominal value. Under reasonable operational assumptions, the closed-loop stability and asymptotic convergence to the optimal solution are rigorously established. Simulation results demonstrate the effectiveness and tractability of the proposed algorithms.

关键词： Microgrids Inverters Frequency control Economics Optimization Time-frequency analysis Eigenvalues and eigenfunctions Optimization distributed algorithms frequency control power generation dispatch microgrids

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Online Learning Over Dynamic Graphs via distributed Proximal Gradient Algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第11期66卷 5065-5079页

作者： Dixit, Rishabh Bedi, Amrit Singh Rajawat, Ketan Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ 08901 USA Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

We consider the problem of tracking the minimum of a time-varying convex optimization problem over a dynamic graph. Motivated by target tracking and parameter estimation problems in intermittently connected robotic and sensor networks, the goal is to design a distributed algorithm capable of handling nondifferentiable regularization penalties. The proposed proximal online gradient descent algorithm is built to run in a fully decentralized manner and utilizes consensus updates over possibly disconnected graphs. The performance of the proposed algorithm is analyzed by developing bounds on its dynamic regret in terms of the cumulative path length of the timevarying optimum. It is shown that as compared to the centralized case, the dynamic regret incurred by the proposed algorithm over T time slots is worse by a factor of log(T) only, despite the disconnected and time-varying network topology. The empirical performance of the proposed algorithm is tested on the distributed dynamic sparse recovery problem, where it is shown to incur a dynamic regret that is close to that of the centralized algorithm.

关键词： Heuristic algorithms Signal processing algorithms Convex functions distributed algorithms Network topology Optimization Robot sensing systems distributed optimization dynamic regret online convex optimization sparse signal recovery

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distributed Balancing Under Flow Constraints Over Arbitrary Communication Topologies

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第12期66卷 5637-5650页

作者： Hadjicostis, Christoforos N. Dominguez-Garcia, Alejandro D. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

In this article, we consider a flow network that is described by a digraph, each edge of which can admit a flow within a certain interval, with non-negative end points that correspond to lower and upper flow limits. We propose and analyze a distributed iterative algorithm for solving the so-called feasible circulation problem, which consists of computing flows that are within the given intervals at each edge and balance the total inflow and the total outflow at each node. Unlike previously proposed distributed algorithms that required bidirectional communication between pairs of nodes that share an edge in the flow network, the algorithm we propose can operate over any communication network, assuming the corresponding digraph that describes it is strongly connected. The proposed algorithm allows the nodes to asymptotically compute (with a geometric rate that depends on the specifics of the given flow network and communication topology) a solution to the feasible circulation problem, as long as such a solution exists. An important special case of the setting studied in this article is the case where the digraph of the flow network matches the digraph of the communication network.

关键词： Network topology Topology distributed algorithms Communication networks Optimization Iterative methods Bidirectional control Digraph directed communication topology distributed balancing feasible circulation flow networks

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distributed Continuous-Time Optimization of Second-Order Multiagent Systems With Nonconvex Input Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第10期51卷 6404-6413页

作者： Mo, Lipo Yu, Yongguang Zhao, Lin Cao, Xianbing Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China Beijing Jiaotong Univ Dept Math Beijing 100044 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

This article discusses the distributed continuous-time optimization problem (DCTOP) of second-order multiagent systems (SOMASs). It is assumed that the inputs are required to be in some nonconvex sets, the team objective function (TOF) is a combination of general differentiable convex functions, and each agent can only obtain the information of one local objective function. Based on the neighbors' information, a new distributed continuous-time optimization algorithm (DCTOA) is first proposed for each agent, where its gradient gains are nonuniform. By introducing a scaling factor and a model transformation, the corresponding system is changed into a time-varying nonlinear system which does not contain constraint operator in form. Then, it is proven that all agents' states could reach an agreement and the TOF could be minimized by constructing some new Lyapunov functions. Finally, the effectiveness of the algorithm is shown by simulation results.

关键词： Optimization Multi-agent systems Linear programming distributed algorithms Force Convergence Lyapunov methods Agreement continuous-time algorithm distributed optimization nonconvex constraints nonuniform gains second-order systems

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Randomized Block Proximal Methods for distributed Stochastic Big-Data Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第9期66卷 4000-4014页

作者： Farina, Francesco Notarstefano, Giuseppe GlaxoSmithKline Artificial Intelligence & Machine Learning Grp London TW8 9GS England Univ Bologna Dept Elect Elect & Informat Engn G Marconi I-40136 Bologna Italy

In this article, we introduce a class of novel distributed algorithms for solving stochastic big-data convex optimization problems over directed graphs. In the addressed set-up, the dimension of the decision variable can be extremely high and the objective function can be nonsmooth. The general algorithm consists of two main steps: a consensus step and an update on a single block of the optimization variable, which is then broadcast to neighbors. Three special instances of the proposed method, involving particular problem structures, are then presented. In the general case, the convergence of a dynamic consensus algorithm over random row stochastic matrices is shown. Then, the convergence of the proposed algorithm to the optimal cost is proven in expected value. Exact convergence is achieved when using diminishing (local) stepsizes, whereas approximate convergence is attained when constant stepsizes are employed. The convergence rate is shown to be sublinear and an explicit rate is provided in the case of constant stepsizes. Finally, the algorithm is tested on a distributed classification problem, first on synthetic data and, then, on a real, high-dimensional, text dataset.

关键词： Optimization Convergence Linear programming Stochastic processes distributed algorithms Approximation algorithms Heuristic algorithms Big data applications distributed algorithms optimization methods Stochastic systems

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distributed Time-Varying Output Formation Tracking Control for General Linear Multi-Agent Systems With Multiple Leaders and Relative Output-Feedback

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IEEE ACCESS 2021年 9卷 59586-59596页

作者： Wang, Rui Univ Toronto Dept Elect & Comp Engn Syst Control Grp Toronto ON M5S 3G4 Canada

In this paper, a unified distributed swarm intelligence algorithm is developed to study time-varying output formation (TVOF) for a general linear multi-agent system (MAS) with a directed network. New adaptive output-feedback formation protocols are proposed to achieve TVOF stabilization for leaderless directed networks and TVOF tracking for leader-follower networks. For the leaderless case, only agents' outputs are required to achieve the desired time-varying formation. An adaptive observer-type formation protocol is constructed via relative outputs of neighboring agents. No global information of the directed network is used to determine the protocol. A distributed algorithm is developed to solve the TVOF stabilization problem after the observability decomposition. For the leader-follower case, only partial agents have knowledge of the leaders' information. An adaptive formation tracking protocol is constructed using dynamic relative output-feedback for neighboring followers. Based on the distributed algorithm, it is proved that the TVOF tracking problem with multiple leaders can be solved in a fully distributed manner.

关键词： Protocols distributed algorithms Multi-agent systems Matrix decomposition Eigenvalues and eigenfunctions 5G mobile communication Vehicle dynamics Output formation tracking adaptive observer-type protocol dynamic relative output-feedback time-varying formation multi-agent system

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