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Adaptation and learning of a fuzzy system by nearest neighbor clustering

机译：最近邻聚类对模糊系统的适应与学习

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摘要

Conventional hill-climbing type adaptive algorithms suffer from large transient error in abrupt changing environments. In this paper, we suggest a novel idea to address this problem. A data re-initialization (DR) scheme is proposed using multiple models approach with special initialization technique to smooth transition between different environment. The design is based on adaptive fuzzy nearest neighbor clustering algorithm with on-line enhancements. The fast adaptation is realized by the data-re-initialization and switching between different models; learning is realized by realized by recording and retrieving the trained up models. The algorithm is conceptually simple and feasible for real-time applications. The performance of the algorithm is tested with simulation studies.
机译：常规的爬山型自适应算法在突然变化的环境中遭受较大的瞬态误差。在本文中，我们提出了解决此问题的新思路。提出了一种使用多种模型方法和特殊初始化技术的数据重新初始化（DR）方案，以平滑不同环境之间的过渡。该设计基于具有在线增强功能的自适应模糊最近邻聚类算法。通过数据重新初始化和不同模型之间的切换来实现快速适应。通过记录和检索经过训练的模型来实现学习。该算法在概念上对于实时应用而言是简单且可行的。该算法的性能通过仿真研究进行了测试。

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