DocumentCode
2971468
Title
An extended BAM neural network model
Author
Zhenjiang, Miao ; Baozong, Yuan
Author_Institution
Inst. of Inf. Sci., Northern Jiaotong Univ., Beijing, China
Volume
3
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
2682
Abstract
Proposes an extended bidirectional associative memory (BAM) neural network model which can do auto- and hetero-associative memory. The theoretical proof for this neural network model´s stability is given. Experiments show that this neural network model is much more powerful than the M-P model, discrete Hopfield neural network, continuous Hopfield neural network, discrete bidirectional associative memory neural network, continuous and adaptive bidirectional associative memory neural network, backpropagation neural network and optimal designed nonlinear continuous neural network. Experimental results also show that, when it does auto-associative memory, the power of this model is the same as the loop neural network model which can only do auto-associative memory.
Keywords
content-addressable storage; neural nets; M-P model; auto-associative memory; backpropagation neural network; continuous Hopfield neural network; discrete Hopfield neural network; discrete bidirectional associative memory neural network; extended BAM neural network model; extended bidirectional associative memory neural network; hetero-associative memory; model stability; optimal designed nonlinear continuous neural network; Adaptive systems; Artificial neural networks; Associative memory; Hopfield neural networks; Information science; Intelligent control; Magnesium compounds; Neural networks; Neurofeedback; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.714276
Filename
714276
Link To Document