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Adaptive dynamic programming (ADP) is a prevalent way to solve the coupled Hamilton-Jacobi-Bellman (HJB) equations of the optimal consensus control for multi-agent systems (MAS). Neural networks (NNs) are normally used to approximate the value functions in ADP. However, NNs with manually designed features may influence the approximation ability. In this study, kernel-based methods which do not need...
A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and cooperation. Since the dynamics of physical agents are so complex that the environment of learning is indeed stochastic, the paper introduces the decentralized multi-agent reinforcement learning (MARL) algorithm, named as Decentralized Concurrent Learning with Cooperative Policy Exploration...
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nested Expectation-Maximization (EM) algorithms. Under suitable sparsity assumptions...
The data centers of cloud video surveillance (CVS) systems based on Hadoop have a couple of common problems such as large energy consumption, low power utilization, etc. Addressing to the issue of reducing energy consumption while guaranteeing quality of service, we propose an energy-aware workload balancing method for efficient data storage management in cloud video data centers. A dynamic adjusting...
The paper reviews the technology and methods used in optimizing supply chain inventory, and summarizes the common inventory optimization algorithms — the mathematical analysis and heuristic algorithms. At last, it points out the several key factors of optimizing supply chain inventory — the lead time, the credit of enterprises and the fuzzy level of some parameters during the optimization process,...
The golden section method is utilized to improve attitude updating algorithm. The performance of the modified 2-sample rotation vector algorithm under coning motion is validated. By contrast with algorithms proposed by Jordan, Miller, Lee, and Savage, under different coning frequence and coning angle, the modified algorithm's precision turns out to be about 1 magnitude higher than the traditional...
This paper addressed a robot path planning algorithm based on improved ant colony optimization. The ant colony algorithm is used for a global path planning in robot rescue. A target attracting function is introduced to guide the searching process which can improve the search quality of ant colony algorithm in the complex and dynamic environment. The affectivity of proposed algorithm is verified in...
In order to model the dynamics of a billet reheating furnace, a multi-input multi-output radial-basis-function neural network is constructed based on an improved sequential-learning algorithm. The algorithm employs an improved growing-and-pruning algorithm based on the concept of the significance of hidden neurons, and an extended Kalman filter improves the learning accuracy. Verification results...
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