一种改进的基于案例推理的建模算法

基于案例推理的方法是一种基于知识获取的方法，也是一种新型的基于数据驱动的建模方法。基于案例推理的核心是案例检索。针对基于案例推理系统中案例检索工作，本文提出了一种改进的K最近邻回归建模算法。首先，基于聚类思想的最近邻回归算法可以实现对案例库的有效划分，从而提高案例检索质量；其次，针对K最近邻算法中邻居个数的选取问题，采用粒子群算法确定需要的邻居个数，取代传统的依靠经验确定邻居个数K的做法。通过对Mackey Glass混沌时间序列数据进行仿真预测，验证了该方法的可行性。

An Improved Case Based Reasoning Modeling Algorithm

Abstract: Case based reasoning (CBR) approach is based on knowledge acquisition and it is a new method for data driven modeling. The key stage of CBR system is the case retrieval one. This paper presents an improved modeling method of K nearest neighbor regression for case retrieval system. Firstly, a cluster based nearest neighbor algorithm is used to divide the case library for improving the quality of case retrieval. Secondly, for the problem of selecting the most suitable number of neighbors, this paper adopted the particle swarm algorithm, instead of the traditional empirical way. The simulation via Mackey Glass chaotic time series data validated the feasibility of the proposed method.