The task of compressed nonlinear time series prediction with nonstationary characteristics is solved. The new architecture of a neural network is proposed that consists of a three-layer “bottle-neck” perceptron and a counterpropagation neo-neuro-fuzzy networkРешена задача прогнозирования сжатых нелинейных временных рядов с нестационарными характеристиками. Предложена нейросетевая архитектура, состоящая из трехслойного персептрона и нейро-нео-фаззи сети встречного распространенияРешена задача прогнозирования сжатых нелинейных временных рядов с нестационарными характеристиками. Предложена нейросетевая архитектура, состоящая из трехслойного персептрона и нейро-нео-фаззи сети встречного распространени
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
Викладені основи методики застосування нейронних мереж для моделювання нелінійних процесів. Дослідже...
The probabilistic modeling of nonlinear regressional, stochastic and functional dependences based on...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The article describes the main features of different neural structures that are used for time series...
The article describes the main features of different neural structures that are used for time series...
The article describes the main features of different neural structures that are used for time series...
SAVICKI Y.V. Adaptive algorithm of generation there is a lot of layer nejron’s of a network of forec...
The paper describes an approach on the defining a set of neural network model inputs analyzing their ...
Проведен анализ архитектуры нейронной сети ARTMAP, обрабатывающей нестационарные последовательности,...
У роботі було досліджено класичну модель прогнозування типу SARIMA та проведено аналіз часового ряду...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
Викладені основи методики застосування нейронних мереж для моделювання нелінійних процесів. Дослідже...
The probabilistic modeling of nonlinear regressional, stochastic and functional dependences based on...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The task of compressed nonlinear time series prediction with nonstationary characteristics is solved...
The article describes the main features of different neural structures that are used for time series...
The article describes the main features of different neural structures that are used for time series...
The article describes the main features of different neural structures that are used for time series...
SAVICKI Y.V. Adaptive algorithm of generation there is a lot of layer nejron’s of a network of forec...
The paper describes an approach on the defining a set of neural network model inputs analyzing their ...
Проведен анализ архитектуры нейронной сети ARTMAP, обрабатывающей нестационарные последовательности,...
У роботі було досліджено класичну модель прогнозування типу SARIMA та проведено аналіз часового ряду...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
The paper presents a new solution of the problem of normalization of the input vectors for neural n...
Викладені основи методики застосування нейронних мереж для моделювання нелінійних процесів. Дослідже...
The probabilistic modeling of nonlinear regressional, stochastic and functional dependences based on...