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A new multi-agent reinforcement learning approach is proposed to learn the optimal behaviors among cooperative agent teams. The approach combines advantages of the integer programming, single agent learning and repeated game in a multi-agent framework. The integer programming is used to build cooperative teams in order to prevent the curse of dimensionality. Every cooperative team learns independently,...
In this work, the R_T_S model based on simultaneous equations (R, T and S) and Catastrophe Theory is performed to describe the behavior of fault injection and the influence of fault injection on a system. The system was selected as a chip in a microelectronic device of a satellite, which is often failed to work by an error of the single-event upset (SEU). A fault-injection algorithm (FI_S) based on...
The thesis presents a fast compositive training algorithm of forward neural network, which integrates the advantages of traditional BP algorithm and single parameter dynamic searching algorithm (SPDS algorithm). It is well known that the BP algorithm, mostly used in many fields, has the disadvantages of slow convergent speed and the possibility of network paralysis. But SPDS algorithm overcomes these...
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