Study of the Counterpropagation Network Based on the EM Algorithm and Its Application

The paper is concerned with the counterpropagation network(CPN) based on the soft-competition mechanism instead of that of the winner-take-all to overcome the later's faults, and the complexity of the network is raised too. By regarding the output of the hidden node in the competition layer as the unknown default random variable, the CPN based on the soft-competition mechanism is trained by the EM algorithm in order to decrease the complexity and accelerate the velocity of convergence of the network. In the M step of the EM algotithm, the algorithm in common use is modified according to the characterstic of the competition layer of the improved CPN, replacing the iteratively reweighted least-squares method with the sample reweighted average method. The results of simulating experiments show that the modified network has a better performance while the training time is greatly reduced. The practicability and convergence of the network is improved rapidly, being particularly useful in the field of classification of patterns.