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Titolo:
Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule
Autore:
Lee, DL;
Indirizzi:
Ta Hwa Inst Technol, Dept Elect Engn, Hsinchu 307, Taiwan Ta Hwa Inst Technol Hsinchu Taiwan 307 t Elect Engn, Hsinchu 307, Taiwan
Titolo Testata:
IEEE TRANSACTIONS ON NEURAL NETWORKS
fascicolo: 2, volume: 12, anno: 2001,
pagine: 439 - 443
SICI:
1045-9227(200103)12:2<439:ITCOCN>2.0.ZU;2-N
Fonte:
ISI
Lingua:
ENG
Soggetto:
ASSOCIATIVE MEMORY; NEURONS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
11
Recensione:
Indirizzi per estratti:
Indirizzo: Lee, DL Ta Hwa Inst Technol, Dept Elect Engn, Hsinchu 307, Taiwan Ta Hwa Inst Technol Hsinchu Taiwan 307 Engn, Hsinchu 307, Taiwan
Citazione:
D.L. Lee, "Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule", IEEE NEURAL, 12(2), 2001, pp. 439-443

Abstract

Jankowski et al. have proposed a complex-valued neural network (CVNN) which is capable of storing and recalling gray-scale images. The convergence property of the CVNN has also been proven by means of the energy function approach. However, the memory capacity of the CVNN is very low because they use a generalized Hebb rule to construct the connection matrix. In this letter, a modified gradient descent learning rule (MGDR) is proposed to enhance the capacity of the CVNN. The proposed technique is derived by applying gradient search over a complex error surface. Simulation shows that the capacity of CVNN with MGDR is greatly improved.

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Documento generato il 14/07/20 alle ore 07:03:41