Research on Trim of Multilayer Feedforward Small World Network Based on E-Exponential Information Entropy

In recent years, it has been recognized that the there is a gapbetween artificial neural network and the brain neural network. The biological neural network is neither a random network nor a regular network, it is a network structure between the two. While the small world network has both a larger clustering coefficient of a regular network and a smaller average path length of a random network [1], so the superiority of the small world network has aroused people's attention. As the BP algorithm converges slowly in the process of error back propagation, it is easy to fall into the local minimum point in the modified weight stage, so this paper Optimizes the BP algorithm to improve the convergence rate of the network and improve the problem that the network is easy to fall into the local minimal. The connection between nodes is too close because there are too many hidden nodes, as a result, the problem of overfitting is arisen. In other words, as for data that are not in the training sample, the learning ability of the network is not strong, resulting in a decrease in the practical value of the network. So we need to find a suitable network structure [2]. Therefore, this paper proposes a e-exponential information entropy multilayer feedforward small world network pruning algorithm based on Shannon entropy principle. By constantly training the network, the network is trimmed according to the entropy of the hidden nodes until the network tends to be stable. The experimental results show that the it has been improved obviously in terms of calculated error and test accuracy for trimmed network, which improved the problem of over-fitting to some extent.