论文标题
弦乐作为一种自组织现象的拧紧：最短同位路径的计算，光滑路径和凸面船体
String Tightening as a Self-Organizing Phenomenon: Computation of Shortest Homotopic Path, Smooth Path, and Convex Hull
论文作者
论文摘要
数十年来，自我组织的现象一直引起神经网络社区的特殊兴趣。在本文中，我们研究了自组织图（SOM）的一种变体，该变体模拟了当从一个或两端拧紧字符串时形成字符串的颗粒的自组织现象。所提出的称为琴弦拧紧自组织神经网络（Ston）的变体可用于解决某些实际问题，例如计算最短同位路径的计算，平滑路径以避免急转弯以及凸船体的计算。这些问题对计算几何，机器人路径计划，AI（图表推理），VLSI路由和地理信息系统具有很大的兴趣。给定一组障碍物和一个在二维空间中具有两个固定端子点的绳子，Ston模型连续拧紧给定的字符串，直到达到欧几里得公制的唯一最短配置为止。 Ston通过以竞争方式动态创建和选择特征向量，从而最大程度地减少了融合的总长度。本文列出了此算法的正确性证明和通过其部署获得的实验结果的证明。
The phenomenon of self-organization has been of special interest to the neural network community for decades. In this paper, we study a variant of the Self-Organizing Map (SOM) that models the phenomenon of self-organization of the particles forming a string when the string is tightened from one or both ends. The proposed variant, called the String Tightening Self-Organizing Neural Network (STON), can be used to solve certain practical problems, such as computation of shortest homotopic paths, smoothing paths to avoid sharp turns, and computation of convex hull. These problems are of considerable interest in computational geometry, robotics path planning, AI (diagrammatic reasoning), VLSI routing, and geographical information systems. Given a set of obstacles and a string with two fixed terminal points in a two dimensional space, the STON model continuously tightens the given string until the unique shortest configuration in terms of the Euclidean metric is reached. The STON minimizes the total length of a string on convergence by dynamically creating and selecting feature vectors in a competitive manner. Proof of correctness of this anytime algorithm and experimental results obtained by its deployment are presented in the paper.
