Title :
A liked-BAM neural network for image recognition
Author :
Shen, D.G. ; Qi, F.H.
Author_Institution :
Res. Inst. of Optical Fibre Eng., Shanghai Jiaotong Univ., China
Abstract :
A neural network model and its application to image recognition are proposed in this paper. This model consists of a mapping network (MN) and liked bidirectional associative memory (LBAM). Invariant mapping is used in MN in order to decrease the number of dimensions of image samples and not to change the distance between them. LBAM´s structure is simple and its convergence speed is fast.<>
Keywords :
content-addressable storage; image recognition; learning (artificial intelligence); neural nets; computer simulations; convergence speed; image recognition; image samples; invariant mapping; liked bidirectional associative memory; liked-BAM neural network; mapping network; noise-added targets; Acceleration; Application software; Associative memory; Computer simulation; Convergence; Equations; Image recognition; Neural networks; Optical fibers; Target 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.1993.320174
