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 "back propagation neural network" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-11 z 11
    Tytuł:
    Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria
    Autorzy:
    Beddal, Dalila
    Achite, Mohammed
    Baahmed, Djelloul
    Powiązania:
    https://bibliotekanauki.pl/articles/1844406.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    Algeria
    back propagation neural network
    BPNN
    multi linear regression
    MLR
    streamflow
    Wadi Hounet
    Opis:
    Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.
    Źródło:
    Journal of Water and Land Development; 2020, 47; 16-24
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network
    Autorzy:
    Pourseiedrezaei, Mehdi
    Loghmani, Ali
    Keshmiri, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/1953511.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    analytic wavelet transform
    AWT
    sound quality evaluation
    SQE
    psychoacoustic metrics
    back propagation neural network
    BPNN
    Opis:
    The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.
    Źródło:
    Archives of Acoustics; 2021, 46, 1; 55-65
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration
    Autorzy:
    Arthur, Clement Kweku
    Temeng, Victor Amoako
    Ziggah, Yao Yevenyo
    Powiązania:
    https://bibliotekanauki.pl/articles/1839011.pdf
    Data publikacji:
    2019
    Wydawca:
    Główny Instytut Górnictwa
    Tematy:
    radial basis function neural network
    back propagation neural network
    generalized regression neural network
    wavelet neural network
    group method of data handling
    ground vibration
    radialna funkcja bazowa
    sieć neuronowa
    GRNN
    sieć falkowo-neuronowa
    grupowa metoda przetwarzania danych
    drgania gruntu
    Opis:
    The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.
    Źródło:
    Journal of Sustainable Mining; 2019, 18, 4; 287-296
    2300-1364
    2300-3960
    Pojawia się w:
    Journal of Sustainable Mining
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system
    Autorzy:
    Li, Y.
    Zhang, B.
    Xu, X.
    Powiązania:
    https://bibliotekanauki.pl/articles/200933.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electric vehicle
    permanent magnet in-wheel motor
    back-propagation neural network
    inverse system
    internal model control
    pojazd elektryczny
    silnik z napędem na magnesy stałe
    inwersja systemu
    propagacja wsteczna
    model odwrotny
    system odwrotny
    Opis:
    The permanent magnet in-wheel motor (PMIWM) is a nonlinear, multivariable, strongly coupled and highly complex system. The key to the development and application of the PMIWM consists in the improvement of its control accuracy and dynamic performance. In order to effectively decouple the PMIWM, this paper presents a novel internal model control (IMC) approach based on the back-propagation neural network inverse (BPNNI) control method. First, theoretical analysis is conducted to show the existence of the PMIWM inverse system, to be modeled mathematically. The inverse system approximated and identified by the back-propagation neural network (BPNN) constitutes the back-propagation neural network inverse (BPNNI) system. Then, by cascading the BPNNI system on the left side of the original PMIWM system, a new decoupling, pseudo-linear system is established. Moreover, the 2-DOF internal model control (IMC) method is employed to design the extra closed-loop controller that further improves disturbance rejection and robustness of the whole system. Consequently, the proposed decoupling control approach incorporates the advantages of both the BPNNI and the IMC. Effectiveness of thus proposed control approach is verified by means of simulation and real-time hardware-in-the-loop (HIL) experiments.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 961-972
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Non-Invasive Hemoglobin Monitoring Device Using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms
    Autorzy:
    Munadi, R.
    Sussi, S.
    Fitriyanti, N.
    Ramadan, D. N.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055237.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    invasive
    non-invasive
    k-nearest neighbor
    artificial neural network
    back propagation
    Opis:
    The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 13--18
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm
    Autorzy:
    Khuat, T. T.
    Le, Q. C.
    Nguyen, B. L.
    Le, M. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/308651.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    Artificial Bee Colony algorithm
    Artificial Neural Network
    back-propagation algorithm
    stock price forecasting
    wavelet transform
    Opis:
    Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
    Źródło:
    Journal of Telecommunications and Information Technology; 2016, 2; 43-52
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives
    Autorzy:
    Sedhuraman, K.
    Himavathi, S.
    Muthuramalingam, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/141220.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sensorless indirect vector controlled IM drives
    speed estimator
    reactive power
    MRAS
    neural network
    back propagation algorithm
    Opis:
    This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.
    Źródło:
    Archives of Electrical Engineering; 2013, 62, 1; 25-41
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training
    Autorzy:
    Al_Duais, M. S.
    Mohamad, F. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/307920.pdf
    Data publikacji:
    2017
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    artificial neural network (ANN)
    batch back propagation algorithm
    dynamic training rate
    speed up training
    accuracy training
    Opis:
    The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.
    Źródło:
    Journal of Telecommunications and Information Technology; 2017, 4; 82-89
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network
    Autorzy:
    Niksa-Rynkiewicz, Tacjana
    Szewczuk-Krypa, Natalia
    Witkowska, Anna
    Cpałka, Krzysztof
    Zalasiński, Marcin
    Cader, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/2031128.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    recurrent neural network
    intelligent industrial monitoring
    Almeida–Pineda recurrent back-propagation
    regenerative heat exchanger
    steam power plant
    Opis:
    Artificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper presents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to similar monitoring applications of other industrial dynamic objects.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 2; 143-155
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of neural networks structure selection in modelling spheroidal graphite cast iron for automotive camshafts
    Optymalizacja doboru struktury sztucznych sieci neuronowych w modelowaniu zużycia żeliwa sferoidalnego na samochodowe wałki rozrządu
    Autorzy:
    Witaszek, Kazimierz
    Garbala, Krzysztof
    Witaszek, Mirosław
    Rychter, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/317234.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Naukowo-Wydawniczy "SPATIUM"
    Tematy:
    artificial neural networks
    structure optimization
    wear
    spheroidal cast iron
    Stuttgart neural network simulator
    resilient back-PROPagation
    sztuczne sieci neuronowe
    optymalizacja struktury
    zużycie
    żeliwo sferoidalne
    resilient back-ROPagation
    Opis:
    The present article discusses the process of optimizing the structure of artificial neural networks applied in modelling the wear of spheroidal graphite cast iron (SG cast iron). The networks were trained using the RPROP gradient method with the application of the SNNS package supported by original self-developed software, which enabled automatic creation, training and testing of networks with different sizes of hidden layers. Based on the results of an analysis of learning process and testing a package of 625 networks, the network was selected which – when modelling the process of spheroidal cast iron wear – generates the slightest errors during testing.
    W pracy przedstawiono proces optymalizacji struktury sztucznych sieci neuronowych użytych do modelowania zużycia żeliwa sferoidalnego. Sieci uczono metodą gradientową RPROP przy użyciu pakietu SNNS wspomaganego autorskim oprogramowaniem, które umożliwiało automatyczne tworzenie, uczenie i testowanie sieci o różnych wielkości warstw ukrytych. Na podstawie analizy wyników procesu uczenia i testowania pakietu 625 sieci dobrano tę, która modelując proces zużycia żeliwa sferoidalnego generuje najmniejsze błędy podczas testowania.
    Źródło:
    Autobusy : technika, eksploatacja, systemy transportowe; 2019, 20, 12; 215-220
    1509-5878
    2450-7725
    Pojawia się w:
    Autobusy : technika, eksploatacja, systemy transportowe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

      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