DocumentCode :
2356534
Title :
Progressive and constant-speed order filtering neural network
Author :
Chen, Chi-Ming ; Yang, Jar-Ferr
Author_Institution :
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fYear :
1994
fDate :
5-8 Dec 1994
Firstpage :
13
Lastpage :
17
Abstract :
In this paper, a new order filtering neural network, which can select a specific ordered value from all inputs, is developed and analyzed. The proposed neural net in two-layer structure iteratively converges to the solution with low and constant convergent speed, which is independent of the number of inputs. With progressive behavior, the proposed neural net obtains the more accurate result when the number of iterations increases if the derived convergent condition is satisfied. From the view points of convergence speed and hardware complexity, the proposed order filtering neural network is suitable for various applications
Keywords :
convergence of numerical methods; filtering theory; iterative methods; neural nets; convergence speed; hardware complexity; iteration; order filtering neural network; two-layer structure; Algorithm design and analysis; Circuits; Filtering algorithms; Image recognition; Neural network hardware; Neural networks; Nonlinear filters; Signal processing algorithms; Speech recognition; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1994. APCCAS '94., 1994 IEEE Asia-Pacific Conference on
Conference_Location :
Taipei
Print_ISBN :
0-7803-2440-4
Type :
conf
DOI :
10.1109/APCCAS.1994.514516
Filename :
514516
Link To Document :
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