An Automated Hybrid CBR System for Forecasting
An Automated Hybrid CBR System for Forecasting
复制标题
用于预测的自动化混合 CBR 系统
DOI：
10.1007/3-540-46119-1_38
复制
发表时间：
2002
影响因子：
2.1
通讯作者：
Jesús M. Torres
中科院分区：
文献类型：
--
作者：
F. F. Riverola;J. Corchado;Jesús M. Torres
提出了一种神经-符号混合问题求解模型，其目标是以无监督的方式预测复杂动态环境的参数。在确定系统的规则未知的情况下，确定系统的特征行为的参数值的预测可能是有问题的任务。该系统采用了基于案例的推理模型，结合了不断增长的细胞结构网络，径向基函数网络和一组Sugeno模糊模型，以提供准确的预测。这些技术中的每一种都用于基于案例的推理系统的推理周期的不同阶段，以检索、适应和审查对问题的建议解决方案。该系统已被用于预测出现在伊比利亚半岛西北部沿海沃茨的赤潮。从这些实验中获得的结果。
A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The proposed system employs a case-based reasoning model that incorporates a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case-based reasoning system to retrieve, to adapt and to review the proposed solution to the problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from those experiments are presented.