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    Wyświetlanie 1-7 z 7
    Tytuł:
    Optimizing information processing in brain-inspired neural networks
    Autorzy:
    Paprocki, B.
    Pregowska, A.
    Szczepanski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/202095.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural network
    entropy
    mutual information
    noise
    inhibitory neuron
    Opis:
    The way brain networks maintain high transmission efficiency is believed to be fundamental in understanding brain activity. Brains consisting of more cells render information transmission more reliable and robust to noise. On the other hand, processing information in larger networks requires additional energy. Recent studies suggest that it is complexity, connectivity, and function diversity, rather than just size and the number of neurons, that could favour the evolution of memory, learning, and higher cognition. In this paper, we use Shannon information theory to address transmission efficiency quantitatively. We describe neural networks as communication channels, and then we measure information as mutual information between stimuli and network responses. We employ a probabilistic neuron model based on the approach proposed by Levy and Baxter, which comprises essential qualitative information transfer mechanisms. In this paper, we overview and discuss our previous quantitative results regarding brain-inspired networks, addressing their qualitative consequences in the context of broader literature. It is shown that mutual information is often maximized in a very noisy environment e.g., where only one-third of all input spikes are allowed to pass through noisy synapses and farther into the network. Moreover, we show that inhibitory connections as well as properly displaced long-range connections often significantly improve transmission efficiency. A deep understanding of brain processes in terms of advanced mathematical science plays an important role in the explanation of the nature of brain efficiency. Our results confirm that basic brain components that appear during the evolution process arise to optimise transmission performance.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 2; 225-233
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimal training strategies for locally recurrent neural networks
    Autorzy:
    Patan, K.
    Patan, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1396735.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    training schedule
    neural network
    Fisher information matrix
    network parameters
    optimal experimental design
    convex optimization theory
    Opis:
    The problem of determining an optimal training schedule for locally recurrent neural network is discussed. Specifically, the proper choice of the most informative measurement data guaranteeing the reliable prediction of neural network response is considered. Based on a scalar measure of performance defined on the Fisher information matrix related to the network parameters, the problem was formulated in terms of optimal experimental design. Then, its solution can be readily achieved via adaptation of effective numerical algorithms based on the convex optimization theory. Finally, some illustrative experiments are provided to verify the presented approach.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 2; 103-114
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuronowe mapy cech w systemie diagnostycznym elektrowni
    Neural feature maps in power plant diagnostic system
    Autorzy:
    Gibiec, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/328332.pdf
    Data publikacji:
    2002
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    przetwarzanie informacji
    informacja pomiarowa
    system diagnostyczny
    sieć neutronowa
    processing information
    measurement information
    diagnostic system
    neural network
    Opis:
    Dążenie do zapewnienia jak najdłuższej i bezawaryjnej eksploatacji obiektów technicznych powoduje zapotrzebowanie na dokładną informację stanie obiektu. Instaluje się więc coraz więcej czujników i systemów pomiarowych tworząc systemy diagnostyki. Ilość gromadzonych informacji jest jednak tak duża, że rodzi to problemy z jej przetwarzaniem. W przedstawionej pracy podjęto próbę wykorzystania sztucznych sieci neuronowych typu Kohonena do analizy dużej liczby sygnałów zbieranych w trakcie pracy typowego turbozespołu elektrowni i jego instalacji pomocniczych. Uzyskane sieci neuronowe realizują zadanie wykrywania zmiany stanu maszyny. Zaprezentowano wyniki działania opracowanego oprogramowania do przetwarzania odpowiedzi zaimplementowanych sieci. Jego działanie ukierunkowano na wizualizację graficzną położenia aktywnego neuronu na tle regionów ilustrujących stan maszyny. W pracy pokazano także możliwości korzystania z sieci neuronowych do wykrywania sygnałów, których zmiany umożliwiają określenie stanu maszyny.
    Aspiration for assertion of the longest and nondefect technical machinery exploitation causes demand for high accuracy information of machinery condition. A growing number of sensors and measurement systems one install in the machinery creating diagnostic systems. A quantity of acquired information is so big that one have problems with its analysing. This case study presents an application of a Kohonen's type artificial neural network utilisation for parallel analysing of a big number of signals measured during typical power plant machinery exploitation. Implemented artificial neural networks accomplish detection of the machinery condition change. Results of neural networks answers postprocessing programs are presented. A visualisation of network activity on the map of machinery state regions is done. Detection of signals which changes make possible machinery state assessing using neural networks is implemented.
    Źródło:
    Diagnostyka; 2002, 26; 121-126
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Information fusion method of multichannel nanosensors based on neural network
    Autorzy:
    Li, Chaoke
    Powiązania:
    https://bibliotekanauki.pl/articles/2173647.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nanosensors
    multiplexing
    information fusion
    data fusion
    neural network
    nanoczujniki
    multipleksowanie
    fuzja informacji
    fuzja danych
    sieć neuronowa
    Opis:
    Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 2; art. no. e140258
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Influence of the training set value on the quality of the neural network to identify selected moulding sand properties
    Autorzy:
    Jakubski, J.
    Dobosz, S. M.
    Major-Gabryś, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/381338.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    information technology
    foundry industry
    quality management
    green moulding sands
    neural network
    technologia informacyjna
    przemysł odlewniczy
    zarządzanie jakością
    masa formierska
    sieć neuronowa
    Opis:
    Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.
    Źródło:
    Archives of Foundry Engineering; 2013, 13, 2; 49-52
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fading Channel Prediction for 5G and 6G Mobile Communication Systems
    Autorzy:
    Soszka, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/2055227.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    5G
    6G
    channel prediction
    channel state information
    sub-6 GHz
    millimetre-wave
    neural network
    artificial intelligence
    narrowband
    wide band
    ultra wide band
    Opis:
    Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultra-wide-band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 153--160
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Building computer vision systems using machine learning algorithms
    Autorzy:
    Boyko, N.
    Sokil, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/410768.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    algorithm
    information system
    neural network
    machine learning
    client-server architecture
    script
    artificial system
    machine learning algorithms
    algorytm
    systemy informacyjne
    sieci neuronowe
    systemy uczące
    architektura klient-serwer
    skrypt
    Opis:
    In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 15-20
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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