scholarly journals Special Section on Current Topics on Neural Networks and Stochastic Models for Information Processing

2011 ◽  
Vol 2 (2) ◽  
pp. 152-152
Author(s):  
Yoshinori Hayakawa
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Stochastic Models ◽  
Special Section

Information processing in synchronous neural networks

1988 ◽  
Vol 49 (1) ◽  
pp. 13-23 ◽  
Author(s):  
J.F. Fontanari ◽  
R. Köberle
Keyword(s):  
Neural Networks ◽  
Information Processing

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

Bayesian and neural networks for geographic information processing

1996 ◽  
Vol 17 (13) ◽  
pp. 1325-1330 ◽  
Author(s):  
A. Stassopoulou ◽  
M. Petrou ◽  
J. Kittler
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Geographic Information

MODELS OF HOPFIELD-TYPE QUATERNION NEURAL NETWORKS AND THEIR ENERGY FUNCTIONS

2005 ◽  
Vol 15 (01n02) ◽  
pp. 129-135 ◽  
Author(s):  
MITSUO YOSHIDA ◽  
YASUAKI KUROE ◽  
TAKEHIRO MORI
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Energy Function ◽  
Point Of View ◽  
Complex Numbers ◽  
Energy Functions ◽  
Fully Connected ◽  
Complex Valued

Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. Furthermore models of neural networks that can deal with quaternion numbers, which is the extension of complex numbers, have also been proposed. However they are all multilayer quaternion neural networks. This paper proposes models of fully connected recurrent quaternion neural networks, Hopfield-type quaternion neural networks. Since quaternion numbers are non-commutative on multiplication, some different models can be considered. We investigate dynamics of these proposed models from the point of view of the existence of an energy function and derive their conditions for existence.

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