DocumentCode :
395154
Title :
Time constrain optimal method to find the minimum architectures for feedforward neural networks
Author :
Tan, Teck-Sun ; Huang, Guang-Bin
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume :
1
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
338
Abstract :
Huang, et al. (1996, 2002) proposed architecture selection algorithm called SEDNN to find the minimum architectures for feedforward neural networks based on the Golden section search method and the upper bounds on the number of hidden neurons, as stated in Huang (2002) and Huang et al. (1998), to be 2√((m + 2)N) or two layered feedforward network (TLFN) and N for single layer feedforward network (SLFN) where N is the number of training samples and m is the number of output neurons. The SEDNN algorithm worked well with the assumption that time allowed for the execution of the algorithm is infinite. This paper proposed an algorithm similar to the SEDNN, but with an added time factor to cater for applications that requires results within a specified period of time.
Keywords :
feedforward neural nets; learning (artificial intelligence); neural net architecture; optimisation; Golden section search method; SEDNN algorithm; feedforward neural networks; hidden neurons; minimum network architecture; time constrain optimal method; training samples; upper bounds; Cost function; Electronic mail; Feedforward neural networks; Network topology; Neural networks; Neurons; Search methods; Time factors; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1202189
Filename :
1202189
Link To Document :
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