IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 A Structure Trainable Neural Network with Embedded Gating Units and Its Learning Algorithm Como, Italy July 24-July 27 ISBN: 0-7695-0619-4
Many problems solved by multilayer neural networks (MLNNs) are reduced into pattern mapping. If the mapping includes several different rules, it is difficult to solve these problems by using a single MLNN with linear connection weights and continuous activation functions. In this paper, a structure trainable neural network has been proposed. The gate units are embedded, which can be trained together with the connection weights. Pattern mapping problems, which include several different, mapping rules, can be realized using a single new network. Since, some parts of the network can be commonly used for different mapping rules, the network size can be reduced compared with the modular neural networks, which consists of several independent expert networks.
Index Terms:
Multilayer neural networks, Modular neural networks, Pattern mapping, Gate units, Structure learning
Citation:
Kenji Nakayama, Akihiro Hirano, Aki Kanbe, "A Structure Trainable Neural Network with Embedded Gating Units and Its Learning Algorithm," ijcnn, vol. 3, pp.3253, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb