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    Wyświetlanie 1-3 z 3
    Tytuł:
    The N queens problem - new variants of the Wirth algorithm
    Autorzy:
    Łajtar, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/106228.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    N-Queens Problem
    filtering algorithm
    backtracking
    Opis:
    The paper presents new ways of n-queens problem solving . Briefly,this is a problem on a nxn chessboard of a set n-queens, so that any two of the mare not in check. At the beginning, currently used algorithm to find solutions is discussed. Then sequentially 4 new algorithms, along with the interpretation of changes are given. The research results, including comparison, of calculation times of all algorithms together with their interpretation are discussed. Finally, conclusions are given. The results were obtained thanks to the pre-created application.Chapters except for By filtering ver. 2 were based on the previous studies carried out during the Bachelor course [1].
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 53-61
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural networks for the N-Queens Problem : a review
    Autorzy:
    Mańdziuk, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/205945.pdf
    Data publikacji:
    2002
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    optymalizacja kombinatoryczna
    problem n-hetmanów
    sieć Hopfielda
    sieć neuronowa
    combinatorial optimization
    Hopfield network
    N-Queens Problem
    neural networks
    Opis:
    Neural networks can be successfully applied to solving certain types of combinatorial optimization problems. In this paper several neural approaches to solving constrained optimization problems are presented and their properties discussed. The main goal of the paper is to present various improvements to the wellknown Hopfield models which are intensively used in combinatorial optimization domain. These improvements include deterministic modifications (binary Hopfield model with negative self-feedback connections and Maximum Neural Network model), stochastic modifications (Gaussian Machine), chaotic Hopfield-based models (Chaotic Neural Network and Transiently Chaotic Neural Network), hybrid approaches (Dual-mode Dynamic Neural Network and Harmony Theory approach) and finally modifications motivated by digital implementation feasibility (Strictly Digital Neural Network). All these models are compared based on a commonly used benchmark prohlem - the N-Queens Problem (NQP). Numerical results indicate that each of modified Hopfield models can be effectively used to solving the NQP. Coonvergence to solutions rate of these methods is very high - usually close to 100%. Experimental time requirements are generally low - polynomial in most casos. Some discussion of non-neural, heuristic approaches to solving the NQP is also presented in the paper.
    Źródło:
    Control and Cybernetics; 2002, 31, 2; 217-248
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Binary neural networks for N-queens problems and their VLSI implementations
    Autorzy:
    Funabiki, N.
    Kurokawa, T.
    Ohta, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/205704.pdf
    Data publikacji:
    2002
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    algorytm
    binarna sieć neuronowa
    N-queens problem
    optymalizacja kombinatoryczna
    problem n-hetmanów
    projekt VLSI
    binary neural network
    combinatorial optimization
    NP-hard
    VLSI design
    algorithm
    Opis:
    Combinatorial optimization problems compose an important class of matliematical problems that include a variety of practical applications, such as VLSI design automation, communication network design and control, job scheduling, games, and genome informatics. These problems usually have a large number of variables to be solved. For example, problems for VLSI design automation require several million variables. Besides, thieir computational complexity is often intractable due to NP-hardness. Neural networks have provided elegant solutions as approximation algorithms to these hard problems due to their natural parallelism and their affinity to hardware realization. Particularly, binary neural networks have great potential to conform to current digital VLSI design technology, because any state and parameter in binary neural networks are expressed in a discrete fashion. This paper presents our studies on binary neural networks to the N-queens problem, and the three different approaches to VLSI implementations focusing on the efficient realization of the synaptic connection networks. Reconfigurable devices such as CPLDs and FPGAs contribute the realization of a scalable architecture with the ultra high speed of computation. Based on the proposed architecture, more than several thousands of binary neurons can be realized on one FPGA chip.
    Źródło:
    Control and Cybernetics; 2002, 31, 2; 271-296
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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