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A consensus-based approach to improve the accuracy of machine learning models

EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 4257-4278页

作者： Karamdel, Hasti Ashtiani, Mehrdad Mehditabar, Mohammad Javad Bakhshi, Fatemeh Iran Univ Sci & Technol Sch Comp Engn Hengam StResalat Sq Tehran *** Iran

consensus algorithms simplify decision-making and conflict-management processes. On the other hand, machine learning is one of the best methods for data management and inference. The main objective of this paper is to introduce a method that can combine these two fields to enhance the accuracy of machine-learning models. In the proposed approach, the final results obtained from the training phase of the machine-learning models converge using two consensus layers. In the first layer, models pull their values closer to each other, and we will rank them based on their proximity to the actual final value. Thus, the models with more positive impacts on other models will gain higher coefficients to attract other models more in the next iterations, and models with negative impacts will lose their effects by reducing their coefficients. Three deduction approaches to obtain the final value have been considered in the second layer. The hard consensus method uses majority voting, the soft consensus method assigns the same weights to each model, and the consensus with the trust factor method sets weights according to the model's closeness to the actual value. This study combined four machine learning models for the regression task and evaluated our approach on three Kaggle datasets based on their R2 score and statistical test. Moreover, we compared our model with an ensemble method of stacking regression that our model outperformed. We have also demonstrated the models' effects on each other and how they approach the actual final value. Lastly, the results show that using consensus methods along with machine-learning models can potentially improve the accuracy of machine-learning models. Specifically, our hard consensus method could outperform other models in most cases, while soft consensus and trust factors can achieve better performance in fewer cases.

关键词： consensus reaching process consensus algorithms Group decision-making Machine learning

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Self-triggered consensus of multi-agent systems via model predictive control

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IFAC-PapersOnLine 2016年第4期49卷 19-24页

作者： Zhan, Jingyuan Li, Xiang Adaptive Networks and Control Lab Research Center of Smart Networks and Systems School of Information Science and Technology Fudan University Shanghai200433 China

This paper proposes a self-triggered consensus algorithm for multi-agent systems by using model predictive control (MPC), where the self-triggering rule and the control algorithm are optimized jointly. The proposed self-triggered MPC consensus algorithm drives the system to reach consensus asymptotically under mild assumptions, if the communication topology is connected. Numerical examples are finally presented to verify the effectiveness and advantages of the self-triggered MPC consensus algorithm. By comparing with the conventional time-triggered and event-triggered consensus algorithms, the self-triggered MPC consensus algorithm is shown to achieve equivalent performance with significant reduction of the numbers of controller updates and information transmission. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multi agent systems Model predictive control Predictive control systems Communication topologies consensus consensus algorithms Event triggered Event triggered controls Information transmission Self triggered controls Self triggering

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consensus Based Distributed Sparse Bayesian Learning by Fast Marginal Likelihood Maximization

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 2119-2123页

作者： Manss, Christoph Shutin, Dmitriy Leus, Geert German Aerosp Ctr Inst Commun & Nav D-82234 Wessling Germany Delft Univ Technol NL-2628 CD Delft Netherlands

For swarm systems, distributed processing is of paramount importance and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learning (SBL) methods rely on the automatic relevance determination (ARD), which involves a computationally complex reweighted l1-norm optimization, or they use loopy belief propagation, which is not guaranteed to converge. Hence, this paper looks into the fast marginal likelihood maximization (FMLM) method to develop a faster distributed SBL version. The proposed method has a low communication overhead, and can be distributed by simple consensus methods. The performed simulations indicate a better performance compared with the distributed ARD version, yet the same performance as the FMLM.

关键词： Signal processing algorithms Optimization Bayes methods Estimation Robot sensing systems Convergence Convex functions consensus algorithms distributed optimization sparse bayesian learning

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A multi-cast algorithm for robust average consensus over internet of things environments

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COMPUTER COMMUNICATIONS 2019年第May期140卷 15-22页

作者： Orostica, Boris Nunez, Felipe Pontificia Univ Catolica Chile Dept Elect Engn Av Vicuna Mackenna 4860 Santiago 7820436 Chile

The initial cloud-managed-network conceptualization of the IoT has mutated into a sparsely coupled, distributed system of interacting smart objects, or things. In this context, distributed control techniques have emerged as a tool for performing tasks locally without the support of a back-end infrastructure. consensus algorithms are the flagship of distributed control and find application in solving problems as varied as distributed task assignment, distributed estimation, and distributed optimization. Unfortunately, typical consensus algorithms deteriorate their performance when faced with realistic communication phenomena as asynchronous communications, packet losses and channel delays. In this work, an algorithm for achieving average consensus over an IoT environment is introduced and evaluated extensively in a large-scale IoT testbed. Evaluation results show that the proposed algorithm achieves average consensus in all evaluated scenarios despite heterogeneity in the network in terms of processing and networking capabilities, packet losses and delays.

关键词： consensus algorithms Internet of things Multi-agent control Distributed control

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Energy Efficient consensus Over Complex Networks

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2015年第2期9卷 292-303页

作者： Asensio-Marco, Cesar Beferull-Lozano, Baltasar Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway Univ Agder CIEM N-4879 Grimstad Norway Univ Valencia Grp Informat & Commun Syst Valencia 46010 Spain

The need to extract large amounts of information from the environment to have precise situation awareness and then react appropriately to certain events has led to the emergence of complex and heterogeneous sensor networks. In this context, where the sensor nodes are usually powered by batteries, the design of new methods to make inference processes efficient in terms of energy consumption is necessary. One of these processes, which is present in many distributed tasks performed by these complex networks, is the consensus process. This is the basis for certain tracking algorithms in monitoring and control applications. To improve the energy efficiency of this process, in this paper we propose a new methodology to optimize the network topology. More specifically, the topologies we obtain are Pareto-optimal solutions in terms of energy consumption and network lifetime metrics. This methodology is first approached from a general point of view, including most network properties at a time. Then, since in the practice not all networks present the same characteristics, we identify three real settings in which the optimization must be tackled differently. This leads to three particularizations of the problem, where the appearance of well-known graph models: small world, scale free and random geometric graphs is related with certain environment and nodes characteristics. Finally, extensive numerical results are presented to show the validity and efficiency of the proposed methodology.

关键词： Complex networks consensus algorithms network topology optimization situational awareness

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Workforce-efficient consensus in crowdsourced transcription of biocollections information

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年 56卷 526-536页

作者： Matsunaga, Andrea Mast, Austin Fortes, Jose A. B. Univ Florida Adv Comp & Informat Syst Lab ECE Gainesville FL 32611 USA Florida State Univ Dept Biol Sci Tallahassee FL 32306 USA

Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical biocollections data, and a number of crowdsourcing platforms have been developed to facilitate interaction with the public and to design simple "Human Intelligence Tasks". However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data, (c) implements a consensus-based controller to assign a dynamic number of workers per task and per field of a task, (d) proposes the use of clustering to further eliminate redundant work and (e) proposes enhancements of future crowdsourcing task assignments in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show multifold increase in the ability to reach consensus when compared to majority voting using exact string matching. In addition, the workforce controller is able to decrease the crowdsourcing cost per task and per task field by 37% and 50%, respectively, when compared to a strategy that uses a fixed number of workers. The accuracy of clustering is also good and it has the potential to increase the quality of tasks that can be clustered. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Crowdsourcing consensus algorithms Digitization Biocollections infrastructure Biodiversity

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consensus-Based Stochastic Control for Model-Free Cell Balancing

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2021年第3期8卷 1139-1150页

作者： Bistritz, Ilai Bambos, Nicholas Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Balancing the state of charge (SOC) of the cells improves both the capacity and the health of the battery. State of the art cell balancing algorithms assume highly accurate SOC estimation which can only be achieved using model-based control solutions. Unfortunately, electrochemical models of batteries are complicated and are still not reliable enough. We propose a simple consensus-based cell balancing algorithm that does not assume any knowledge of the electrochemical model of the battery. Instead, our algorithm only uses SOC estimates from online measurements to make sequential balancing decisions. Using martingale techniques, we prove that our algorithm equalizes the SOC of all cells with probability 1 even with very noisy SOC estimates under a general non-i.i.d. noise model. We then analyze the convergence time of our algorithm as a function of the problem parameters. We also prove performance guarantees for the more challenging case where balancing is done while the battery is charging or discharging. Finally, we present simulations that support our theoretical claims and show the reliability of our algorithm. Our model-free cell balancing is easy to implement and therefore is a major step towards making active cell balancing practical.

关键词： Battery control cell balancing consensus algorithms martingales stochastic control

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A consensus Algorithm Based on Nearest Second-order Neighbors' Information

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IFAC-PapersOnLine 2015年第11 11期48卷 545-550页

作者： Chen, C. Zhang, B. Lu, Q. Wu, Q. Wang, J. Liu, S. Institute of Electrical Engineering and Automation Hangzhou Dianzi University HangzhouZhejiang310018 China

For the problem of communication in consensus algorithm based on secondorder neighbors' information, a consensus algorithm using the nearest second-order neighbors' information, in which only partial second-order neighbors' information is used, is proposed and applied to continuous multiple motion bodies with double-integrator kinematics. We conducted the stable analysis to compare the proposed algorithm with the consensus algorithms based on the first-order neighbors' information, and based on the second-order neighbors' information on the convergence, respectively. The three consensus algorithms were used to multiple robot formation with leader-followers. The simulations have demonstrated the feasibility and validity of the proposed algorithm. © 2015

关键词： Industrial robots Kinematics consensus algorithms Double integration Leader follower Multiple robot Second orders

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An Asynchronous consensus-Based Algorithm for Estimation From Noisy Relative Measurements

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2014年第3期1卷 283-295页

作者： Carron, Andrea Todescato, Marco Carli, Ruggero Schenato, Luca Univ Padua Dept Informat Engn I-35131 Padua Italy

In this paper, we address the problem of optimal estimating the position of each agent in a network from relative noisy vectorial distances with its neighbors by means of only local communication and bounded complexity, independent of network size and topology. We propose a consensus-based algorithm with the use of local memory variables which allows asynchronous implementation, has guaranteed exponential convergence to the optimal solution under simple deterministic and randomized communication protocols, and requires minimal packet transmission. In the randomized scenario, we then study the rate of convergence in expectation of the estimation error and we argue that it can be used to obtain upper and lower bound for the rate of converge in mean square. In particular, we show that for regular graphs, such as Cayley, Ramanujan, and complete graphs, the convergence rate in expectation has the same asymptotic degradation of memoryless asynchronous consensus algorithms in terms of network size. In addition, we show that the asynchronous implementation is also robust to delays and communication failures. We finally complement the analytical results with some numerical simulations, comparing the proposed strategy with other algorithms which have been recently proposed in the literature.

关键词： Wireless-sensor networks (WSNs) distributed localization algorithms consensus algorithms

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A Distributed Economic Dispatch Algorithm Based on Multi-Agent consensus Control and Incremental Power Supplying

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IFAC-PapersOnLine 2018年第23期51卷 7-12页

作者： Chen, Wushun Li, Tao School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Key Laboratory of Pure Mathematics and Mathematical Practice School of Mathematical Sciences East China Normal University Shanghai200241 China

In this paper, the economic dispatch problem (EDP) is studied for an energy internet, which is composed of energy routers (ERs) and intelligent microgrids interconnected with the main grid. The intelligent microgrid consists of several distributed generators (DGs), energy storage units, loads and intelligent control units. A distributed economic dispatch algorithm is proposed based on the leader-follower consensus and the average-consensus protocols. The average power mismatch of all buses of the whole microgrid is iteratively estimated by the average-consensus algorithm. After each iteration, the increment of power is adjusted by the energy router. By algebraic graph theory and limit theory, it is proved that if the network topology of the energy internet contains a spanning tree with the energy router as the root node, then the algorithm ensures that the incremental costs (ICs) of all distributed generators tend to the electricity price obtained by the energy router, the balance of energy supply and demand is asymptotically achieved for the whole microgrid system, and the economic dispatch is asymptotically optimal. The simulation results demonstrate the effectiveness of the proposed algorithm. © 2018

关键词： Electric load dispatching Distributed power generation Economics Electric energy storage Graph algorithms Intelligent agents Iterative methods Microgrids Multi agent systems Scheduling Topology Trees (mathematics) Average consensus protocol consensus algorithms Distributed generator (DGs) Economic Dispatch Economic dispatch problems Energy internet Intelligent control unit Leader follower consensus

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