Target Recognition in Naval Battlefield Based on Principal Component Analysis and Neural Networks

The Principal Component Analysis(PCA) is used to aggregate the recognition attribute, in order to decrease the association of each attribute and reduce the attribute. The Neural Networks is used to recognize the target. The use of optimizing policy can improve the constringency speed and the generalization ability of the Neural Networks. The combination of Principal Component Analysis and Neural Networks not only can recognize the target in high efficiency, but also can have the ability of self-study and adapting which can recognize the target in naval battlefield. A simulation is given to prove the efficiency of this algorithm.