Informacja

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

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Neural Network" wg kryterium: Temat

  Lista filtrów

Dostawca treści

Wyrównaj
Wyświetlanie 1-15 z 887
Tytuł:
Noise quantization simulation analysis of optical convolutional networks
Autorzy:
Zhang, Ye
Zhang, Saining
Zhang, Danni
Su, Yanmei
Yi, Junkai
Wang, Pengfei
Wang, Ruiting
Luo, Guangzhen
Zhou, Xuliang
Pan, Jiaoqing
Powiązania:
https://bibliotekanauki.pl/articles/27310111.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
optical neural network
convolutional neural network
noise
quantization
Opis:
Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.
Źródło:
Optica Applicata; 2023, 53, 3; 483--493
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of neural networks to detect eccentricity of induction motors
Autorzy:
Ewert, P.
Powiązania:
https://bibliotekanauki.pl/articles/1193467.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
neural network
general regression neural network
multilayer perceptron
eccentricity
induction motor
Opis:
The possibility of using neural networks to detect eccentricity of induction motors has been presented. A field-circuit model, which was used to generate a diagnostic pattern has been discussed. The formulas describing characteristic fault frequencies for static, dynamic and mixed eccentricity, occurring in the stator current spectrum, have been presented. Teaching and testing data for neural networks based on a preliminary analysis of diagnostic signals (phase currents) have been prepared. Two types of neural networks were discussed: general regression neural network (GRNN) and multilayer perceptron (MLP) neural network. This paper presents the results obtained for each type of the neural network. Developed neural detectors are characterized by high detection effectiveness of induction motor eccentricity.
Źródło:
Power Electronics and Drives; 2017, 2, 37/2; 151-165
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bitmap Image Recognition with Neural Networks
Autorzy:
Uchkin, Dmytro
Korotyeyeva, Tetyana
Shestakevych, Tetiana
Powiązania:
https://bibliotekanauki.pl/articles/1833890.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
neural network
digitized image
Opis:
Logistics, finance, science, and trade are just some of the areas that require computer vision technology, which includes number recognition. The need to recognize numbers in images or photographs is found in tasks such as recognizing car numbers, reading values from paper bills, recognizing object identification numbers, and reading credit card numbers. The development of an online application for recognition numbers in bitmap images using machine training technologies, namely an artificial neural network based on the class of neural networks perceptron, is an actual task.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2020, 9, 1; 30--35
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network simulation in running of acetic acid synthesis unit while start-up
Nejjroetevoe modelirovanie dlja upravlenija kolonnojj sinteza uksusnojj kisloty v period puska
Autorzy:
Porkuian, O.
Samojlova, Z.
Powiązania:
https://bibliotekanauki.pl/articles/792304.pdf
Data publikacji:
2013
Wydawca:
Komisja Motoryzacji i Energetyki Rolnictwa
Tematy:
neural network
artificial neural network
automated control system
acetic acid
MATLAB software
Źródło:
Teka Komisji Motoryzacji i Energetyki Rolnictwa; 2013, 13, 3
1641-7739
Pojawia się w:
Teka Komisji Motoryzacji i Energetyki Rolnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural modeling of plant tissue cultures: a review
Autorzy:
Zielinska, S.
Kepczynska, E.
Powiązania:
https://bibliotekanauki.pl/articles/81293.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial neural network
biomass
plant tissue
neural model
tissue culture
in vitro condition
micropropagation
radial neural network
neural network
somatic embryo
Źródło:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2013, 94, 3
0860-7796
Pojawia się w:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
FPGA Implementation of Neural Nets
Autorzy:
Kumari, B A Sujatha
Kulkarni, Sudarshan Patil
Sinchana, C. G.
Powiązania:
https://bibliotekanauki.pl/articles/27311922.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
artificial neural network
Spartan-6
field programmable gate arrays (FPGAs)
convolutional neural network
Opis:
The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distributed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 599--604
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Synchronization analysis of inertial memristive neural networks with time-varying delays
Autorzy:
Wei, R.
Cao, J.
Powiązania:
https://bibliotekanauki.pl/articles/91767.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
inertial
memristive
neural network
synchronization
Opis:
This paper investigates the global exponential synchronization and quasi-synchronization of inertial memristive neural networks with time-varying delays. By using a variable transmission, the original second-order system can be transformed into first-order differential system. Then, two types of drive-response systems of inertial memristive neural networks are studied, one is the system with parameter mismatch, the other is the system with matched parameters. By constructing Lyapunov functional and designing feedback controllers, several sufficient conditions are derived respectively for the synchronization of these two types of drive-response systems. Finally, corresponding simulation results are given to show the effectiveness of the proposed method derived in this paper.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 4; 269-282
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network approach to compressor modelling with surge margin consideration
Autorzy:
Loryś, Sergiusz Michał
Orkisz, Marek
Powiązania:
https://bibliotekanauki.pl/articles/2091364.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
modelling
compressor map
neural-network
Opis:
Artificial neural networks are gaining popularity thank to their fast and accurate response paired with low computing power requirements. They have been proven as a method for compressor performance prediction with satisfactory results. In this paper a new approach of artificial neural networks modelling is evaluated. The auxiliary parameter of ‘relative stability margin Z’ was introduced and used in learning process. This approach connects two methods of compressor modelling such as neural networks and auxiliary parameter utilization. Two models were created, one with utilization of the ‘relative stability margin Z’ as a direct indication of surge margin of any estimated condition, and other with standard compressor parameters. The results were compared by determination of fitting, interpolation and extrapolation capabilities of both approaches. The artificial neural networks used during the process was a two-layer feed-forward neural-network with Levenberg–Marquardt algorithm with Bayesian regularization. The experimental data was interpolated to increase the amount of learning data for the neural network. With the two models created, capabilities of this relatively simple type of neural-network to approximate compressor map was also assessed.
Źródło:
Archives of Thermodynamics; 2022, 43, 1; 89--108
1231-0956
2083-6023
Pojawia się w:
Archives of Thermodynamics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A mobile robot navigation with use of CUDA parallel architecture
Autorzy:
Siemiątkowska, B.
Szklarski, J.
Gnatowski, M.
Borkowski, A.
Węclewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/384808.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
navigation
neural network
parallel computing
Opis:
In this article we present a navigation system of a mobile robot based on parallel calculations. It is assumed that the robot is equipped with a 3D laser range scanner. The system is essentially based on a dual grid-object, where labels are attached to detected objects (such maps can be used in navigation based on semantic information). We use a classical SMPA (Sense - Model - Plan - Act) architecture for navigation, however, some steps concerning object detection, planning and localization are parallelized in order to speed up the entire process. The CUDA (Compute Unified Device Architecture) technology allows us to execute our algorithms on many processing units with use of a inexpensive graphics card which makes it possible to apply the proposed navigation system in a real time.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 3; 79-84
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
How to reconstruct the unknown physical quantities using neural networks?
Rekonstrukcja wielkości fizycznych z użyciem sieci neuronowych
Autorzy:
Wolter, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/905690.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
reconstruction
physics
Bayesian
neural network
Opis:
In this article an application of neural networks to the reconstruction of unknown physical quantities in particle physics is presented. As an example the mass reconstruction of the hypothetical Higgs boson in the typical high energy physics experiment is used. Monte Carlo events are used to determine the probability distributions of observables (energies of two jets and the angle between them) for various Higgs boson mass, which are later fitted using a Neural Network. These distributions are used to determine the mass probability distribution of the measured particle. The mass is reconstructed without knowing the functional dependence between the observables and the measured quantity. The miscalibration of the measured quantities is automatically corrected in this method.
W artykule zaprezentowane jest zastosowanie sieci neuronowych do rekonstrukcji nieznanych wielkości w fizyce cząstek elementarnych. Jako przykład użyta jest rekonstrukcja masy hipotetycznego bozonu Higgsa oparta na symulowanych danych. Dane te zostały użyte do wyznaczenia rozkładów prawdopodobieństwa mierzonych wielkości (energie dwóch dżetów oraz kąt pomiędzy nimi) dla różnych mas cząstki Higgsa. Rozkłady te zostały następnie sparametryzowane za pomocą sieci neuronowych oraz wyznaczenia rozkładu prawdopodobieństwa masy mierzonej cząstki. Masa jest wyznaczona bez użycia zależności funkcyjnej pomiędzy mierzonymi wielkościami a rekonstruowaną masą. Kalibracja wielkości pomiarowych jest automatycznie korygowana poprzez rozkłady prawdopodobieństwa.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2008, 216
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of artificial intelligence in project management under risk condition
Autorzy:
Kutschenreiter-Praszkiewicz, I.
Powiązania:
https://bibliotekanauki.pl/articles/117725.pdf
Data publikacji:
2009
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
risk
neural network
project scheduling
Opis:
Risk management problem was shown in the paper. Relationship between risk management and production process scheduling was analyzed. Different types of data analysis were presented. Toothed gear production process was taken as an example of task timing estimation.
Źródło:
Applied Computer Science; 2009, 5, 1; 69-80
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected problem of structure optimization for Artificial Neural Networks with forward connections
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/376117.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
network structure
structure optimization
Opis:
The problem of Artificial Neural Network (ANN) structure optimization related to the definition of optimal number of hidden layers and distribution of neurons between layers depending on selected optimization criterion and inflicted constrains. The article presents the resolution of the optimization problem. The function describing the number of subspaces is given, and the minimum number of layers as well as the distribution of neurons between layers shall be found.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 80; 191-197
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
Autorzy:
Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/2201329.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting currency exchange rate time series with fireworks algorithm-based higher order neural network, with special attention to training data enrichment
Autorzy:
Sahu, Kishore Kumar
Nayak, Sarat Chandra
Behera, Himansu Sekhar
Powiązania:
https://bibliotekanauki.pl/articles/1839247.pdf
Data publikacji:
2020
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
exchange rate
virtual data point
interpolation
artificial neural network
fireworks algorithm
functional link neural network
Opis:
Exchange rates are highly fluctuating by nature; thus, they are difficult to forecast. Artificial neural networks (ANNs) have proven to be better than statistical methods. Inadequate training data may lead the model to reach sub-optimal solutions, resulting in poor accuracy (as ANN-based forecasts are data-driven). To enhance forecasting accuracy, we suggests a method of enriching training datasets through exploring and incorporating virtual data points (VDPs) by an evolutionary method called the fireworks algorithm-trained functional link artificial neural network (FWA-FLN). The model maintains a correlation between current and past data, especially at the oscillation point on the time series. The exploration of a VDP and forecast of the succeeding term go consecutively by FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other similarly trained models and produces far better prediction accuracy.
Źródło:
Computer Science; 2020, 21 (4); 463-488
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company
Autorzy:
Ruciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/1819257.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Perceptron Artificial Neural Network
Radial Artificial Neural Network
Recursive Artificial Neural Network
neural model quality
Day-Ahead Market
Polish Power Exchange
Mean square error
determination index
Opis:
The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 77--93
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 887

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