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Multi AUV Intelligent Autonomous Learning Mechanism Based on QPSO Algorithm

机译:基于QPSO算法的多AUV智能自主学习机制

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Intelligent autonomous learning is a hotspot of research on multi-agent system research, because of the PSO algorithm and multiple AUV system has loose coupling intelligent group structure, multiple AUV system flexibility and openness, multiple AUV system can be combined with swarm intelligence algorithm. So, using the swarm intelligence research of particle swarm optimization (pso) autonomous intelligent AUV underwater robot autonomous learning mechanism, can greatly improve the performance of many of AUV system. Quantum PSO algorithm and PSO algorithm based on the experimental verification, to prove QPSO algorithm has the certain superiority in the AUV more autonomous learning, in the process of each iteration self-learning optimal state, not only improve the efficiency of the algorithm, but also for the current to search for the optimal state, improve the accuracy of search algorithm.
机译:智能自主学习是多智能体系统研究的研究热点,由于PSO算法和多个AUV系统具有松散耦合的智能组结构,多个AUV系统的灵活性和开放性,因此多个AUV系统可以与群体智能算法结合使用。因此,利用群体智能研究的粒子群优化(pso)自主智能AUV水下机器人自主学习机制,可以大大提高许多AUV系统的性能。基于量子PSO算法和PSO算法的实验验证,证明了QPSO算法在AUV更自主学习中具有一定优势,在每次迭代过程中自学习最优状态,不仅提高了算法的效率,而且还为电流搜索最优状态,提高搜索算法的准确性。

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