Two-level classification of target recognition based on neural network

In this paper,we present a classification method,which contains a competitive learning algorithm with a nonlinear map function. Since leakage and error exist ill one - level classification, then this method is particularly effective in, recognition. A new concept, which is the "two - level classification": is proposed, it and its application to the feature extraction and the data association are also studied. The aim is to produce a, useful track file,which contains groups of information of the moving target. The training of the connection weight in two - level classification is a key of problem, and thr storage capacity is an important question too. The key to the settlement of the question lies in adjusting adaptive learning rates on parallel distribution. The effectiveness and the correctness of the proposed method are shown in given results. An input pattern sample is either classified effectively by the former, or classified effectively hy the latter.