Temat: complex-valued neural networks

Skocz do pozycji: 1.

Tytuł:: Complex-valued associative memories with projection and iterative learning rules
Autorzy:: Isokawa, T.
Yamamoto, H.
Nishimura, H.
Yumoto, T.
Kamiura, N.
Matsui, N.
Tematy:: complex-valued neural networks
associative memory
projection; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91682.pdf Link otwiera się w nowym oknie
Opis:: In this paper, we investigate the stability of patterns embedded as the associative memory distributed on the complex-valued Hopfield neural network, in which the neuron states are encoded by the phase values on a unit circle of complex plane. As learning schemes for embedding patterns onto the network, projection rule and iterative learning rule are formally expanded to the complex-valued case. The retrieval of patterns embedded by iterative learning rule is demonstrated and the stability for embedded patterns is quantitatively investigated.
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: A MLMVN with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using FRA
Autorzy:: Aizenberg, Igor
Luchetta, Antonio
Manetti, Stefano
Piccirilli, Maria Cristina
Tematy:: analog circuits
complex-valued neural networks
lumped model
testability; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91696.pdf Link otwiera się w nowym oknie
Opis:: A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.
Dostawca treści:: Biblioteka Nauki

Artykuł

Informacja