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    Wyświetlanie 1-14 z 14
    Tytuł:
    An arma type pi-sigma artificial neural network for nonlinear time series forecasting
    Autorzy:
    Akdeniz, E.
    Egrioglu, E.
    Bas, E.
    Yolcu, U.
    Powiązania:
    https://bibliotekanauki.pl/articles/91816.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    high order artificial neural networks
    pi-sigma neural network, forecasting
    recurrent neural network
    particle swarm optimization (PSO)
    Opis:
    Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 121-132
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural Based Autonomous Navigation of Wheeled Mobile Robots
    Autorzy:
    Al-Sagban, M.
    Dhaouadi, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/384293.pdf
    Data publikacji:
    2016
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    reactive navigation
    obstacle avoidance
    autonomous ground robots
    recurrent neural networks
    Opis:
    This paper presents a novel reactive navigation algorithm for wheeled mobile robots under non-holonomic constraints and in unknown environments. Two techniques are proposed: a geometrical based technique and a neural network based technique. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment by modulating its steering angle and turning radius. The dimensions and shape of the robot are incorporated to determine the set of all possible collision-free steering angles. The algorithm then selects the best steering angle candidate. In the geometrical navigation technique, a safe turning radius is computed based on an equation derived from the geometry of the problem. On the other hand, the neural-based technique aims to generate an optimized trajectory by using a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The experimental results demonstrate that the algorithms are capable of driving the robot safely across a variety of indoor environments.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 2; 64-72
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new cast-resin transformer thermal model based on recurrent neural networks
    Autorzy:
    Azizian, D.
    Bigdeli, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/141202.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    cast-resin transformer
    dynamics
    recurrent neural networks
    thermal modeling
    Opis:
    Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.
    Źródło:
    Archives of Electrical Engineering; 2017, 66, 1; 17-28
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition
    Autorzy:
    Brocki, Ł.
    Marasek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/177625.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep belief neural networks
    long-short term memory
    bidirectional recurrent neural networks
    speech recognition
    large vocabulary continuous speech recognition
    Opis:
    This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (LSTM) hybrid used as an acoustic model for Speech Recognition. It was demonstrated by many independent researchers that DBNNs exhibit superior performance to other known machine learning frameworks in terms of speech recognition accuracy. Their superiority comes from the fact that these are deep learning networks. However, a trained DBNN is simply a feed-forward network with no internal memory, unlike Recurrent Neural Networks (RNNs) which are Turing complete and do posses internal memory, thus allowing them to make use of longer context. In this paper, an experiment is performed to make a hybrid of a DBNN with an advanced bidirectional RNN used to process its output. Results show that the use of the new DBNN-BLSTM hybrid as the acoustic model for the Large Vocabulary Continuous Speech Recognition (LVCSR) increases word recognition accuracy. However, the new model has many parameters and in some cases it may suffer performance issues in real-time applications.
    Źródło:
    Archives of Acoustics; 2015, 40, 2; 191-195
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new approach to image reconstruction from projections using a recurrent neural network
    Autorzy:
    Cierniak, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/907945.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rekonstrukcja obrazu
    sieć neuronowa
    sieć rekurencyjna
    image reconstruction from projections
    neural networks
    recurrent net
    Opis:
    A new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 147-157
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic network functional comparison via approximate-bisimulation
    Autorzy:
    Donnarumma, F.
    Murano, A.
    Prevete, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/206758.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    continuous time recurrent neural network
    dynamic networks
    bisimulation
    network equivalence
    Opis:
    It is generally unknown how to formally determine whether different neural networks have a similar behaviour. This question intimately relates to the problem of finding a suitable similarity measure to identify bounds on the input-output response distances of neural networks, which has several interesting theoretical and computational implications. For example, it can allow one to speed up the learning processes by restricting the network parameter space, or to test the robustness of a network with respect to parameter variation. In this paper we develop a procedure that allows for comparing neural structures among them. In particular, we consider dynamic networks composed of neural units, characterised by non-linear differential equations, described in terms of autonomous continuous dynamic systems. The comparison is established by importing and adapting from the formal verification setting the concept of δ−approximate bisimulations techniques for non-linear systems. We have positively tested the proposed approach over continuous time recurrent neural networks (CTRNNs).
    Źródło:
    Control and Cybernetics; 2015, 44, 1; 99-127
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Structural damage detection in moving load problem using JRNNs based method
    Autorzy:
    Jena, Shakti P.
    Parhi, Dayal R.
    Powiązania:
    https://bibliotekanauki.pl/articles/279388.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
    Tematy:
    Jordan’s Recurrent Neural Networks (JRNNs)
    crack depth
    crack location
    Opis:
    Damage detection in a structure using the vibration signature is a quiet smart method for condition monitoring of the structure. In this problem, the Recurrent Neural Networks (RNNs) based method has been implemented for damage detection in the moving load problem as an inverse method. A multi-cracked simply supported beam under a traversing load has been considered for the present problem. The localization and severities of the supervised cracks on the structure are determined using the adapted Jordan’s Recurrent Neural Networks (JRNNs) approach. The mechanism of Levenberg-Marquardt’s back propagation algorithm has been implemented to train the networks. To check the adoptability of the proposed JRNNs method, numerical analyses along with laboratory test verifications have been conducted and found to be well emerged.
    Źródło:
    Journal of Theoretical and Applied Mechanics; 2019, 57, 3; 665-676
    1429-2955
    Pojawia się w:
    Journal of Theoretical and Applied Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    How to fly to safety without overpaying for the ticket
    Autorzy:
    Kaczmarek, Tomasz
    Grobelny, Przemysław
    Powiązania:
    https://bibliotekanauki.pl/articles/9252090.pdf
    Data publikacji:
    2023-07-19
    Wydawca:
    Uniwersytet Ekonomiczny w Poznaniu
    Tematy:
    asset allocation strategy
    target volatility
    flight-to-safety
    recurrent neural networks
    machine learning
    Opis:
    For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries.
    Źródło:
    Economics and Business Review; 2023, 9, 2; 160-183
    2392-1641
    Pojawia się w:
    Economics and Business Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics
    Autorzy:
    Kobojek, P.
    Saeed, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/307650.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    biometrics
    GRU networks
    keystroke dynamics
    LSTM networks
    recurrent neural networks
    user verification
    Opis:
    Keystroke dynamics is one of the biometrics techniques that can be used for the verification of a human being. This work briefly introduces the history of biometrics and the state of the art in keystroke dynamics. Moreover, it presents an algorithm for human verification based on these data. In order to achieve that, authors’ training and test sets were prepared and a reference dataset was used. The described algorithm is a classifier based on recurrent neural networks (LSTMand GRU). High accuracy without false positive errors as well as high scalability in terms of user count were chosen as goals. Some attempts were made to mitigate natural problems of the algorithm (e.g. generating artificial data). Experiments were performed with different network architectures. Authors assumed that keystroke dynamics data have sequence nature, which influenced their choice of classifier. They have achieved satisfying results, especially when it comes to false positive free setting.
    Źródło:
    Journal of Telecommunications and Information Technology; 2016, 3; 80-90
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems
    Autorzy:
    Koprinkova-Hristova, P. D.
    Hadjiski, M. B.
    Doukovska, L. A.
    Beloreshki, S. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/226304.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technical diagnosis
    Thermal Power Plant (TPP)
    Recurrent Neural Networks (RNN)
    distributed control system (DCS)
    predictive maintenance
    Opis:
    In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant - a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose, we compared two types of recurrent neural networks: historical Elman architecture and a recently developed kind of RNN named Echo stet networks (ESN). The preliminary investigations showed better approximation and faster training abilities of ESN in comparison to the Elman network. Direction of future work will be increasing of predications time horizon and inclusion of our predictor at lower level of a complex predictive maintenance system.
    Źródło:
    International Journal of Electronics and Telecommunications; 2011, 57, 3; 401-406
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An intelligent approach to short-term wind power prediction using deep neural networks
    Autorzy:
    Niksa-Rynkiewicz, Tacjana
    Stomma, Piotr
    Witkowska, Anna
    Rutkowska, Danuta
    Słowik, Adam
    Cpałka, Krzysztof
    Jaworek-Korjakowska, Joanna
    Kolendo, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/23944826.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    renewable energy
    wind energy
    wind power
    wind turbine
    short-term wind power prediction
    deep learning
    convolutional neural networks
    gated recurrent unit
    hierarchical multilayer perceptron
    deep neural networks
    Opis:
    In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 197--210
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single-ended quality measurement of a music content via convolutional recurrent neural networks
    Autorzy:
    Organiściak, Kamila
    Borkowski, Józef
    Powiązania:
    https://bibliotekanauki.pl/articles/1849158.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    audio data analysis
    artefacts detection
    convolutional neural networks
    recurrent neural networks
    classification model
    Opis:
    The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content. The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal. Since real music content may include some modes of instrumental sounds, speech and singing voice or different audio effects, it is more complex to analyze than clean speech or artificial signals, especially without a comparison to the known reference content. The presented results might be treated as a proof of concept, since some specific types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound). However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes. To examine the model efficiency several experiments have been performed and reported in the paper. The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, first independently, then along with additional features (Zero Crossing Rate, Spectral Contrast). According to the obtained results, there is a significant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms. The paper examines also the influence of recursive layers on effectiveness of the artefact classification task.
    Źródło:
    Metrology and Measurement Systems; 2020, 27, 4; 721-733
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear model predictive control of a boiler unit: a fault tolerant control study
    Autorzy:
    Patan, K.
    Korbicz, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/331450.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rekurencyjna sieć neuronowa
    model procesu
    sterowanie predykcyjne
    detekcja uszkodzeń
    zbiornik przepływowy
    recurrent neural networks
    process model
    predictive control
    fault detection
    boiler unit
    Opis:
    This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 1; 225-237
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fault detection and isolation for dynamic processes using recurrent neural networks
    Detekcja i lokalizacja uszkodzeń procesów dynamicznych z użyciem sieci rekurencyjnych
    Autorzy:
    Przystałka, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/329218.pdf
    Data publikacji:
    2009
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    detekcja uszkodzeń
    lokalizacja uszkodzeń
    sieć Jordana i Elmana
    sieć lokalnie rekurencyjna
    teoria chaosu
    fault detection
    fault isolation
    Jordan and Elman neural networks
    locally recurrent neural networks
    chaos theory
    Opis:
    The paper focuses on the problem of fault detection and isolation for dynamic processes using selected recurrent neural networks. The main objective is to show how to employ some discoveries of the chaos theory for modeling processes by means of globally and locally recurrent neural networks. Both types of neural models are used in fault detection and isolation block. The performance of the FDI system is examined using two types of neural models: Jordan/Elman tower neural networks and networks with dynamic neural units. The paper contains numerical examples that illustrate the merits and limits of these two approaches.
    Treść artykuł wiąże się z problemem detekcji i lokalizacji uszkodzeń dla szerokiej gamy procesów dynamicznych z użyciem wybranych rekurencyjnych sieci neuronowych. Głównym celem jest pokazanie w jaki sposób mogą zostać zastosowane niektóre z odkryć teorii chaosu do modelowania procesów z użyciem globalnych i lokalnych struktur neuronowych. Oba typy modeli neuronowych zostały użyte w bloku detekcji i lokalizacji uszkodzeń. Sprawność układu diagnostycznego porównana została dla modeli procesów z zastosowaniem: sieci wielo-kontekstowych Jordana/Elmana i sieci z neuronami dynamicznymi. W artykule zamieszczono przykłady numeryczne wskazujące na zalety i wady obu podejść.
    Źródło:
    Diagnostyka; 2009, 1(49); 33-40
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-14 z 14

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