Complicated Graphics Model based on Neural Network of Particle Swarm

The past decades have seen the great progress pattern recognition and image understanding, motivated by a wide range of real world applications. The previous approaches mostly based on the feature extraction and recognition methods, and usually suffer from the problem of weak discrimination power. In this paper, we propose a complex model based on particle swarm and neural network pattern recognition methods. The proposed approach uses the class label of the input sample as the actual output of the neural network which has the maximum node corresponds to the class. To evaluate the effectiveness of the proposed approach, we experimentally compare the particle swarm neural network based approach with and SVM classification algorithm. The experimental results show that, both the recognition accuracy and training time of the particle swarm and neural network based approach, are better than that of SVM. The particle swarm neural network pattern recognition in complex graphics showed a great advantage in application.