A MODEL FOR CASE RETRIEVAL BASED ON ANN AND NEAREST NEIGHBOR ALGORITHM

摘要：

To improve efficiency and quality of case retrieval in case- based reasoning system, a case retrieval model based on the artificial neural network (ANN) and nearest neighbor (NN) algorithm is presented. Firstly, the indexes of cases are created in order to shrink the case-searching range, and the BP neural network is applied to memorize the product cases that are indexed. Secondly, the similar cases, which are retrieved by ANN for the first matching, are computed by NN for the second matching, while the weights of NN are given by customers. Thus, the retrieval efficiency and quality are improved through combining customers subjective desire with the objective retrieval of ANN. Finally, an example of motorcycle is given to illustrate the working process of the model, which proves the effectiveness and feasibility of case retrieval model.

关键词： Case-based reasoning (CBR) BP neural network (BPNN) Case retrieval Nearest neighbor algorithm

作者: ZHI-YING ZHANG JIAN-WEI WANG XIAO-PENG WEI WEN-JING YU

作者单位: Center for Advanced Design Technology, Dalian University, Dalian 116622, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 142-147

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）