Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, h...Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, however, a novel additive penalty term which represents the features extracted by hidden units is introduced to eliminate the overtraining of multilayer feedfoward networks. Computer simulations demonstrate that by using this unsupervised fashion penalty term, the generalization ability is greatly improved.展开更多
A new class of neural systems called locally binary oscillator networks is proposed. It consists of a large number of binary oscillators, which couple with each other directly only through its nearest neighbours. Th...A new class of neural systems called locally binary oscillator networks is proposed. It consists of a large number of binary oscillators, which couple with each other directly only through its nearest neighbours. The properties of synchronization and desynchronization of the networks are investigated. We find that under some criteria locally coupled binary oscillators can yield global synchronization.展开更多
文摘Generalization ability is a major problem encountered when using neural networks to find the structures in noisy data sets. Controlling the network complexity is a common method to solve this problem. In this paper, however, a novel additive penalty term which represents the features extracted by hidden units is introduced to eliminate the overtraining of multilayer feedfoward networks. Computer simulations demonstrate that by using this unsupervised fashion penalty term, the generalization ability is greatly improved.
文摘A new class of neural systems called locally binary oscillator networks is proposed. It consists of a large number of binary oscillators, which couple with each other directly only through its nearest neighbours. The properties of synchronization and desynchronization of the networks are investigated. We find that under some criteria locally coupled binary oscillators can yield global synchronization.
