A Mixed BP Neural Network Based on Ant Colony Algorithm and Rough Set

In order to solve the problem that when BP neural network classify high dimensional redundant sample the convergence speed is slow and is easy to fall in local minimum problem,a mixed BP neural network model based on ant colony algorithm and rough set is proposed.In the mixed BP neural network,rough set is used to reductive and reduce the dimension of sample,so the input layer neuron number is reduced,which reduce the computational complexity of the training of the neural network,and ant colony algorithm has solved the random of selection neural network weights and threshold,which has avoided the problem of being easy to fall in local minimum problem.The results gotten by testing data sets in the UCI database,the mixed BP neural network is feasible to classify high dimensional redundant sample,and the performance is more better than the traditional BP neural network and ant colony neural network.