Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "recurrent neural network" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Basic concepts of dynamic recurrent neural networks development
Autorzy:
Boyko, N.
Pobereyko, P.
Powiązania:
https://bibliotekanauki.pl/articles/410971.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
recurrent neural network
dynamic system
learning algorithms
reservoir computing
unsteady dynamics
Opis:
In this work formulated relevance, set out an analytical review of existing approaches to the research recurrent neural networks (RNN) and defined precondition appearance a new direction in the field neuroinformatics – reservoir computing. Shows generalized classification neural network (NN) and briefly described main types dynamics and modes RNN. Described topology, structure and features of the model NN with different nonlinear functions and with possible areas of progress. Characterized and systematized wellknown learning methods RNN and conducted their classification by categories. Determined the place RNN with unsteady dynamics of other classes RNN. Deals with the main parameters and terminology, which used to describe models RNN. Briefly described practical implementation recurrent neural networks in different areas natural sciences and humanities, and outlines and systematized main deficiencies and the advantages of using different RNN. The systematization of known recurrent neural networks and methods of their study is performed and on this basis the generalized classification of neural networks was proposed.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2016, 5, 2; 63-68
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting economic and financial indicators by supply of deep and recovery neural networks
Autorzy:
Boyko, N.
Ivanets, A.
Bosik, M.
Powiązania:
https://bibliotekanauki.pl/articles/411261.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
neural network
deep
recurrent
activation function
feedforward
neuron
hidden layer
stock price prediction
Opis:
This paper studies the potential of the application of the Recurrent Neural Networks, as well as the Deep Neural Networks in the field of the finances and trading. In particular, their use in the stock price predicting software. The concepts of the RNNs and DNNs are provided and explained thoroughly. Both techniques RNNs and DNNs are utilized in the implementation of the stock price predicting software. Two separate versions of the software are created in order to demonstrate the main differences between the algorithms, as well as to determine the best of the two. Each version is thoroughly examined. The comparison of each of the algorithms is performed and highlighted. Examples of the implementations of the software, utilizing each of the algorithms on big volumes of stock data, for stock price prediction are provided. The article summarizes the concept of stock price prediction backed by the popular machine learning algorithms and its application in the nowadays world.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2018, 7, 2; 3-8
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies