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Titolo: Improving the capacity of complexvalued 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:
 10459227(200103)12:2<439:ITCOCN>2.0.ZU;2N
 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 complexvalued neural networks with a modified gradient descent learning rule", IEEE NEURAL, 12(2), 2001, pp. 439443
Abstract
Jankowski et al. have proposed a complexvalued neural network (CVNN) which is capable of storing and recalling grayscale 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.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 14/07/20 alle ore 07:03:41