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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Self-Learning Immune Co-Evolutionary Network for Multiple Escaping Targets Search With Random Observable Conditions
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A Self-Learning Immune Co-Evolutionary Network for Multiple Escaping Targets Search With Random Observable Conditions

机译：用于多个逃逸目标的自学习免疫共同进化网络，随机可观察条件搜索

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摘要

The search for multiple escaping targets is a significant issue of cooperative control in multi-agent systems since targets consciously seek to avoid being captured. Moreover, the assumption of continuous observations in existing works is not always suitable due to the limit of measuring equipment and uncertain movement of targets. Therefore, the problem with searching for escaping targets, which can be more aptly labeled "multiple escaping-targets search with random observation conditions" (MESROC), is difficult to address by conventional methods. Inspired by machine learning and the immune response mechanism of human bodies, a self-learning immune co-evolutionary network (SLICEN) is proposed. The SLICEN consists mainly of an immune cellular network (ICN) and an immune learning algorithm (ILA). The ICN provides feasible solutions to MESROC. Different kinds of network models are introduced to work as an ICN, such as convolutional neural networks, extreme learning machines, and support vector machines. The ILA evaluates the performance of feasible solutions and selects the optimal ones to further strengthen ICN reversely. Solutions are repeatedly improved through the co-evolution of ICN and ILA. An essential distinction to conventional machine learning approaches is that SLICEN works well without training samples. Simulations and comparisons demonstrate that patterns of advanced cooperative behavior among searchers function properly. SLICEN is an efficient method for solving MESROC.
机译：对于多种转义目标的搜索是多代理系统中的重要问题，因为有意识地寻求避免被捕获的目标。此外，由于测量设备的限制和目标的不确定运动，现有工程中连续观测的假设并不总是适合。因此，通过传统方法难以通过传统方法来搜索逃离目标的问题，该问题可以更恰当地标记为随机观察条件“（Mesroc）（Mesroc）。通过机器学习的启发和人体的免疫应答机制，提出了一种自学免疫共同进化网络（SLICEN）。 Slicen主要由免疫细胞网络（ICN）和免疫学习算法（ILA）组成。 ICN为Mesroc提供了可行的解决方案。引入不同类型的网络模型作为ICN，如卷积神经网络，极端学习机和支持向量机。 ILA评估可行解决方案的性能，并选择最佳的解决方案，以相反地进一步加强ICN。通过ICN和ILA的共同演变反复改善解决方案。传统机器学习方法的基本区别是Slicen在没有训练样本的情况下运行良好。仿真和比较表明，搜索者之间的高级合作行为模式正常。 Slicen是求解Mesroc的一种有效方法。

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