Temat: neural network - Prolib Integro

Skocz do pozycji: 1.

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
Tematy:: optical neural network
convolutional neural network
noise
quantization; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/27310111.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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.
Tematy:: neural network
artificial neural network
automated control system
acetic acid
MATLAB software; Pokaż więcej
Wydawca:: Komisja Motoryzacji i Energetyki Rolnictwa
Powiązania:: https://bibliotekanauki.pl/articles/792304.pdf Link otwiera się w nowym oknie
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Application of neural networks to detect eccentricity of induction motors
Autorzy:: Ewert, P.
Tematy:: neural network
general regression neural network
multilayer perceptron
eccentricity
induction motor; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/1193467.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Neural modeling of plant tissue cultures: a review
Autorzy:: Zielinska, S.
Kepczynska, E.
Tematy:: artificial neural network
biomass
plant tissue
neural model
tissue culture
in vitro condition
micropropagation
radial neural network
neural network
somatic embryo; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/81293.pdf Link otwiera się w nowym oknie
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Analysis of the Stage
Autorzy:: Jaworowski, Karol
Opis:: The primary objective of this paper is to design a method of detecting road edges without using complex algorithms to identify and analyze images. Instead, neural networks are used, which allows to enhance and facilitate this process. The paper describes a program that recognizes the road in a picture with the use of a neural network trained on 500 samples. The samples contain original photos and images with a selected road. In the course of the research two solutions arose. The first solution is to use a single Perceptron to recognize the road. The second solution is to classify the photos using a Kohonen network and establish a separate network for each class.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 6.

Tytuł:: Neural networks application to reduction of train caused distortions in magnetotelluric measurement data
Autorzy:: Wojdyła, Marek
Baran, Grzegorz
Bielecka, Marzena
Danek, Tomasz
Opis:: Artificial intelligence methods for MT data processing are proposed. Distortions having a complex structure created by external artificial sources such as, for example, passing train were investigate. In the first part of this paper the time intervals with such type of distortions were found by using a special neuronal system. Next for time intervals found in the first stage the measure curve fragment is removed and then it is replied by the fragment created by a trained perceptron. The experiment showed that used method are effective.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 7.

Tytuł:: Bitmap Image Recognition with Neural Networks
Autorzy:: Uchkin, Dmytro
Korotyeyeva, Tetyana
Shestakevych, Tetiana
Tematy:: neural network
digitized image; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Oddział w Lublinie PAN
Powiązania:: https://bibliotekanauki.pl/articles/1833890.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Synchronization analysis of inertial memristive neural networks with time-varying delays
Autorzy:: Wei, R.
Cao, J.
Tematy:: inertial
memristive
neural network
synchronization; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91767.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Neural network approach to compressor modelling with surge margin consideration
Autorzy:: Loryś, Sergiusz Michał
Orkisz, Marek
Tematy:: modelling
compressor map
neural-network; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2091364.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: FPGA Implementation of Neural Nets
Autorzy:: Kumari, B A Sujatha
Kulkarni, Sudarshan Patil
Sinchana, C. G.
Tematy:: artificial neural network
Spartan-6
field programmable gate arrays (FPGAs)
convolutional neural network; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/27311922.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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