RESEARCH OF SIMULATION TECHNIQUES BASED ON ROUGH SET THEORY
Qinggong College, Hebei United University, Tangshan, Hebei 063000, China
Rough set theory can effectively analyse and deal with incomplete information in simulation techniques. This paper studied the knowledge reduction problem and discrete continuous attributes and improved the BP neural network on the basis of rough set theory. Firstly, methods of attribute reduction of classical are analysis. This paper proposes a heuristic algorithm for reduction of knowledge based on information entropy. Subsequently, it studied the combination algorithm of rough set and neural network. Pre-treatment of sample data based on rough set in dealing with imprecision and uncertainty issues on the edge. The decision rules obtained after reduction in order to map to the training sample of neural network. Finally, the neuron number of hidden layer of neural network and hidden layer makes the neural network more logical. The simulation results show that the simulation technique of rough set and neural network has obvious complementary and reduce the time to train the neural network. It improved the training accuracy and generalization ability simulation techniques achieved satisfactory results.