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Wyszukujesz frazę "complex networks" wg kryterium: Temat

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    Wyświetlanie 1-4 z 4
    Tytuł:
    Complex-valued associative memories with projection and iterative learning rules
    Autorzy:
    Isokawa, T.
    Yamamoto, H.
    Nishimura, H.
    Yumoto, T.
    Kamiura, N.
    Matsui, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/91682.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    complex-valued neural networks
    associative memory
    projection
    Opis:
    In this paper, we investigate the stability of patterns embedded as the associative memory distributed on the complex-valued Hopfield neural network, in which the neuron states are encoded by the phase values on a unit circle of complex plane. As learning schemes for embedding patterns onto the network, projection rule and iterative learning rule are formally expanded to the complex-valued case. The retrieval of patterns embedded by iterative learning rule is demonstrated and the stability for embedded patterns is quantitatively investigated.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 3; 237-249
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A MLMVN with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using FRA
    Autorzy:
    Aizenberg, Igor
    Luchetta, Antonio
    Manetti, Stefano
    Piccirilli, Maria Cristina
    Powiązania:
    https://bibliotekanauki.pl/articles/91696.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    analog circuits
    complex-valued neural networks
    lumped model
    testability
    Opis:
    A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 5-19
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On the influence of topological characteristics on robustness of complex networks
    Autorzy:
    Kasthurirathna, D.
    Piraveenan, M.
    Thedchanamoorthy, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91836.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    topological characteristics
    complex network
    random networks
    robustness
    Opis:
    In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesised scale-free, small-world and random networks, we look at a number of network measures, including assortativity, modularity, average path length, clustering coefficient, rich club profiles and scale-free exponent (where applicable) of a network, and how each of these influence the robustness of a network under targeted attacks. We use an established robustness coefficient to measure topological robustness, and consider sustained targeted attacks by order of node degree. With respect to scale-free networks, we show that assortativity, modularity and average path length have a positive correlation with network robustness, whereas clustering coefficient has a negative correlation. We did not find any correlation between scale-free exponent and robustness, or rich-club profiles and robustness. The robustness of small-world networks on the other hand, show substantial positive correlations with assortativity, modularity, clustering coefficient and average path length. In comparison, the robustness of Erdos-Renyi random networks did not have any significant correlation with any of the network properties considered. A significant observation is that high clustering decreases topological robustness in scale-free networks, yet it increases topological robustness in small-world networks. Our results highlight the importance of topological characteristics in influencing network robustness, and illustrate design strategies network designers can use to increase the robustness of scale-free and small-world networks under sustained targeted attacks.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 2; 89-100
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Learning structures of conceptual models from observed dynamics using evolutionary echo state networks
    Autorzy:
    Abdelbari, H.
    Shafi, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91864.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    complex systems modeling
    conceptual models
    causal loop diagrams
    computational intelligence
    echo state networks
    evolutionary algorithms
    Opis:
    Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 133-154
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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