He Ming,Feng Boqin,Ma Zhaofeng,Fu Xianghua
(Department of Computer Science and Technology,Xi'an Jiaotong University,Xi'an 710049,China)
Abstract:Aiming at the problem that clear physical meanings can't be given by nerve cells and weights of neural networks,a neural network model based on rough set was proposed,in which rules were extracted from given training data firstly by utilizing numerical analysis ability of rough set theory, and then neurons number of the hidden layer were determined in terms of these rules to obtain the original topology of the rough neural network. Meanwhile,the input to the model was mapped into the output subspace by using rules acquired from the rough set and the expectation output could be approximated.Thus,a neural network that provides with good understandability and rapid convergence could be constructed. Experiments show that the proposed approach can deal with problems of neural network topology architecture,sample size and quality which directly influence the generalization ability and accuracy of neural network.While greatly reducing training time,the prediction precision of the model can be achieved 96.4£¥ which is 3.6£¥ higher than RBF £¨radial basis function£© neural network model under the same conditions£®
Keywords:rough set;neural network;rough set data analysis;rough neuron