Research On Trim Of Multilayer FeedForward Small World Network Based On E-exponential Information Entropy

Complexity and complex systems are one of the middle research topics of the twenty-first Century.Complex networks generalize the characteristics of complex systems.Slowly,as the rapid development of computer data processing and computing,the fundamental change has taken place in this case.Slowly,because of the rapid development of computer data processing and computing power,this situation has changed radically.People began to study the topology of large-scale complex networks.The research found that although many networks have obvious complexity and randomness,there will be clear patterns and rules that can be described in mathematical and statistical languages,and the most important is the small world effect.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,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.For a long time,the structure of the network by experience,in order to ensure the accuracy is often biased in favor of redundancy,so the network training process required a longer time,increase the burden of learning algorithm in the training speed,and high accuracy of the resulting network is likely to be the result of the existence of redundant nodes,there have been fitted,as for the training sample data outside the accuracy declined sharply,the network generalization ability weak.In this paper,a pruning algorithm based on E exponent information entropy multi-layer feedforward small world network is proposed.Based on the principle of information entropy,we calculate the entropy value of each hidden node,prune the hidden nodes that have no significant change in entropy or entropy change is less than the threshold value,and constantly train the network until the network tends to be stable.Experimental results show that compared with unpruned networks,the pruning algorithm improves the accuracy significantly,and achieves the corresponding control in terms of error,and improves the over fitting problem to a certain extent.