Title :
Learning algorithm of large BP neural network
Author :
Chen Ming ; Li Minghui
Author_Institution :
Comput. Inst., Dalian Univ. of Technol., China
Abstract :
Training a neural network is a NP complete problem (S. Judd, 1987; A. Plum and R. L. Rivest, 1988). A learning algorithm is proposed. Theoretic analysis and simulation results show that this kind of algorithm, which is used to train a large BP (backpropagation) neural network, can speed up the learning rate and gain accurate results.<>
Keywords :
backpropagation; computational complexity; neural nets; NP complete problem; accurate results; backpropagation neural network; large BP neural network; learning algorithm; learning rate; simulation results; theoretic analysis; Algorithm design and analysis; Analytical models; Artificial intelligence; Computational modeling; Computer networks; Learning systems; Machine learning; Machine learning algorithms; Neural networks; Pattern recognition;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.320132
