This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions an...
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe other agents' historical actions and choose actions that best respond to other agents' previous actions; we call this a best response scheme. We start by analyzing the convergence rate of this best response scheme for standard time-invariant games. Specifically, we provide a sufficient condition on the strong monotonicity parameter of the time-invariant games under which the proposed best response algorithm achieves exponential convergence to the static Nash equilibrium. We further illustrate that this best response algorithm may oscillate when the proposed sufficient condition fails to hold, which indicates that this condition is tight. Next, we analyze this best response algorithm for time-varying games where the cost functions of each agent change over time. Under similar conditions as for time-invariant games, we show that the proposed best response algorithm stays asymptotically close to the evolving equilibrium. We do so by analyzing both the equilibrium tracking error and the dynamic regret. Numerical experiments on economic market problems are presented to validate our analysis.
Decentralized deep learning has made significant success since it avoids the single point of failure in centralized solutions. However, the system might deviate from the correct model due to Byzantine attacks. Existin...
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Decentralized deep learning has made significant success since it avoids the single point of failure in centralized solutions. However, the system might deviate from the correct model due to Byzantine attacks. Existing Byzantine-resilient defense models are mainly of a one-step evaluation fashion, making them vulnerable to rigorous topology and sophisticated cyber-attacks due to lack of historical evaluations. This paper proposes a credibility assessment based parameter aggregation rule (CA-PAR) that evaluates each neighboring node by its long-term performance. For each node and its neighbors, two concepts, immediate reward and history information based credibility are firstly proposed to describe the immediate reliability at current iteration and the comprehensive assessment of the reliability respectively. Thereafter, all the received parameters are aggregated in linear combination, in which the adjacent weight is determined by credibility value. Finally, the influences of suspicious nodes can gradually be reduced and eliminated. Experimental results in MNIST and CIFAR-10 datasets indicate the algorithm’s tolerance for five state-of-the-art attack methods against an arbitrary number of faulty nodes. Compared with the previous defense models, the proposed algorithm in this paper outperforms in topology constraints, training accuracy and computation cost. IEEE
Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with class...
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Quantum random-number generators (QRNGs) can offer a means to generate information-theoretically provable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixed with classical randomness due to classical noises. To distill this quantum randomness, one needs to quantify the randomness of the source and apply a randomness extractor. Here, we propose a generic framework for evaluating quantum randomness of real-life QRNGs by min-entropy, and apply it to two different existing quantum random-number systems in the literature. Moreover, we provide a guideline of QRNG data postprocessing for which we implement two information-theoretically provable randomness extractors: Toeplitz-hashing extractor and Trevisan's extractor.
The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for ...
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The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for improving the efficiency of such system. The aim of this approach is to classify the objectives for a real-world case study which presents a major problem for controlling the quality levels of its production lines. This approach provided a significant improvement when the testing data are various or complex.
This paper considers the design problem of the robust optimal state-feedback controllers for the uncertain nonlinear dynamic systems, where the uncertain nonlinear dynamic systems can be represented by the Takagi-Suge...
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This paper considers the design problem of the robust optimal state-feedback controllers for the uncertain nonlinear dynamic systems, where the uncertain nonlinear dynamic systems can be represented by the Takagi-Sugeno (TS) fuzzy-model-based dynamic systems with both elemental parametric uncertainties and norm-bounded approximation error. An integrative method, which complementarily fuses the robust stabilizability condition, the orthogonal-functions approach (OFA) and the hybrid Taguchi-genetic algorithm (HTGA), is presented in this paper to design the robust quadratic-optimal state-feedback controllers, in which the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). A design example is given to demonstrate the applicability of the proposed integrative optimization approach.
A large variety of sophisticated metaheuristic methods have been proposed for photovoltaic parameter extraction. Our aim is not to develop another metaheuristic method but to investigate two practically important yet ...
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A typical servicing operation in space mainly includes three phases: capturing the target, berthing and docking the target, and repairing the target. The attitude of a satellite usually changes after the capturing, be...
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A typical servicing operation in space mainly includes three phases: capturing the target, berthing and docking the target, and repairing the target. The attitude of a satellite usually changes after the capturing, because the control system is turned off during this phase for safety reasons. In this paper, a method is proposed to achieve the berthing of the target and reorientating the satellite attitude at the same time, both by involving manipulator motion only. Firstly, the constraints on the manipulator and the objective function are defined according to the planning problem. Then the joint trajectory is parameterized by sinusoidal function, whose argument is the polynomial. Finally, genetic algorithm is used to search for the global optimal resolution of the parameters. When the parameters are found, each joint trajectory can be determined. This planned trajectory is smooth and more applicable for the control of the free-floating robotic system. Our proposed method is verified by simulation
Reinforcement learning is a widely used approach to autonomous navigation, showing potential in various tasks and robotic setups. Still, it often struggles to reach distant goals when safety constraints are imposed (e...
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The work addresses PID control design based on the velocity-pausing particle swarm optimization (VPPSO) technique. The suggested control design is utilized to develop a load frequency control (LFC) approach for an iso...
The work addresses PID control design based on the velocity-pausing particle swarm optimization (VPPSO) technique. The suggested control design is utilized to develop a load frequency control (LFC) approach for an isolated microgrid (MG) with heat-pumps (HPs) and electric-vehicles (EVs). The efficiency and effectiveness of suggested control design are assessed when there are randomized solar power and load-demand disturbances. Furthermore, the robustness and effectiveness of suggested control schemes are evaluated under uncertainty in system parameters and different EVs & HPs controller’s operating time-scheduling situations. The simulation results demonstrated the efficiency and usefulness of the recommended VPPSO-PID control strategy under several practical situations.
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