Given that extreme weather disturbances frequently threaten the safe and stable operation of new power systems, the uncertainty of source–load forecasting has become a particular bottleneck affecting ...
Control of complex flows plays a central role in many processes, where the typical parameter space for optimization is huge. Breakthroughs in direct numerical simulation over the last century have ...
Abstract: This paper presents the hierarchical Q-learning path planning (HQPP) architecture for solving the cooperative tracking control problem of multi-agent systems (MASs) with lumped uncertainties ...
Faculty of Engineering Sciences, Kyushu University, Kasuga, Fukuoka 816-8580, Japan Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) ...
In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed.
Abstract: We introduce an optimization assisted by a neural network (ONN) predictor to the electromagnetic community. ONN belongs to the class of the surrogate model-based optimization approaches, and ...
The uncertainty of renewable energy and demand response brings many challenges to the microgrid energy management. Driven by the recent advances and applications of deep reinforcement learning a ...
In many of the cases, we see that the traditional neural networks are not capable of holding and working on long and large information. attention layer can help a neural network in memorizing the ...
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