基于模糊粗糙模型的粗神经网络建模方法研究

提出一种基于模糊粗糙模型的粗神经网络建模(FRM_RNN_M)方法. 该方法通过自适应G-K聚类实现输入输出积空间的模糊划分, 进而在聚类数和约简属性搜索的基础上, 提取优化的模糊粗糙模型(Fuzzy rough model, FRM), 并在融合神经网络后实现粗神经网络建模. 分类实验表明, FRM_RNN_M的分类性能优于传统贝叶斯和LVQ方法, 而且比单纯的FRM模型具有更强的综合决策能力, 和传统的粗逻辑神经网络(Rough logic neural network, RLNN)相比, FRM_RNN_M方法建立的神经网络结构精简, 收敛速度快, 具有更强的泛化能力.

Fuzzy Rough Model Based Rough Neural Network Modeling

The rough neural network model based on fuzzy rough model (FRM),FRM_RNN_M,is addressed.By means of adaptive Ganstafason Kessel (G-K) algorithm,fuzzy partition can be implemented in the input-output product space.Based on the search of cluster number and feature reduction,optimized FRM can be extracted and rough neural network model can be constructed after integrating the neural network technique.Ex- periment results indicate that FRM_RNN_M is superior to con- ventional Bayesian and LVQ methods.Moreover,it has a better synthesis decision-making ability than the single FRM.Com- pared with conventional rough logic neural network (RLNN), the neural network based on FRM_RNN_M has superiorities in size of structure,convergence speed,and generalization ability.