Title :
A structure trainable neural network with embedded gating units and its learning algorithm
Author :
Nakayama, Kenji ; Hirano, Akihiro ; Kanbe, Aki
Author_Institution :
Dept. of Electr. & Comput. Eng., Kanazawa Univ., Japan
Abstract :
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 is 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
Keywords :
feedforward neural nets; learning (artificial intelligence); neural net architecture; pattern matching; continuous activation functions; embedded gating units; linear connection weights; multilayer neural networks; pattern mapping; structure learning; structure trainable neural network; Annealing; Automatic control; Computer architecture; Computer simulation; Function approximation; Multi-layer neural network; Neural networks; Pattern analysis; Pattern classification;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861312
