Application of GMDH Neural Network to Air Attack Target Identification Based on Rough Sets

In the modern aerial defense fight,target attributes recognition is related to many factors,recognition process is complex,which calls for high time efficiency.A group method of data handling neural networks classification model is set up based on rough sets,aimed at characteristics of target attributes recognition.By using the model a lot of problems are solved,such as the low efficiency while high dimension data sets are used to train the neural networks and the neural networks configuration scale is great.Meanwhile,in order to boost the attributes reduction efficiency of high dimension data sets,the set approximate quality reduction algorithm is improved.Finally,in contrast with the simulation result of BP neural networks,the result shows that the classification quality of group method of data handling neural networks classification model based on rough sets is better than that of BP neural networks model,which satisfies the requirement for target attributes recognition in modern aerial defense fight,the attributes reduction algorithm based on speediness seeking core and set approximate quality is rapid and efficient.