论文标题
关于机器人导航的进化反向传播神经控制器的概括能力
On the Generalization Capability of Evolved Counter-propagation Neuro-controllers for Robot Navigation
论文作者
论文摘要
最近已经建议使用模拟机器人导航对不断发展的反向传播神经控制器（CPNC）（CPNC）（FFNC）（FFNC）进行了测试。人们已经被恶魔构成的是，通过不断发展的CPNC获得的收敛速率和最终性能都优于通过不断发展的FFNC获得的收敛速率和最终性能。在本文中，检查了两种进化导航控制器的迷宫概括特征。为此，在一个与用于训练的环境中大相径庭的环境中，对控制器进行了测试。此外，通过单目标和多目标进化方法获得的结果进行了比较。使用模拟的案例研究，在单个和多目标案例中都强调了进化的CPNC的迷宫概括能力。相反，发现进化的FFNC在两种方法中都缺乏这种能力。
Evolving Counter-Propagation Neuro-Controllers (CPNCs), rather than the traditional Feed-Forward Neuro-Controllers (FFNCs), has recently been suggested and tested using simulated robot navigation. It has been demon-strated that both convergence rate and final performance obtained by evolving CPNCs are superior to those obtained by evolving FFNCs. In this paper the maze generalization features of both types of evolved navigation controllers are examined. For this purpose the controllers are tested in an environment that drastically differs from the one used for their training. Moreover, a comparison is carried out of results obtained by single-objective and multi-objective evolution approaches. Using a simulated case-study, the maze generalization capability of the evolved CPNCs is highlighted in both the single and multi-objective cases. In contrast, the evolved FFNCs are found to lack such capabilities in both approaches.
