The Experts below are selected from a list of 471759 Experts worldwide ranked by ideXlab platform
Masaki Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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Multistate vector product hopfield Neural Networks
Neurocomputing, 2018Co-Authors: Masaki KobayashiAbstract:Abstract Several high-dimensional models of Hopfield Neural Networks, such as complex-valued and quaternionic Hopfield Neural Networks, have been proposed. However, it has been hard to construct three-dimensional models of Hopfield Neural Networks. A split type of vector product Hopfield Neural network (VPHNN) was proposed as a special case of ordinary Hopfield Neural Networks. It is easier to construct split types of Hopfield Neural Networks than multistate types of ones, because the split types can be often regarded as special cases of ordinary ones. In the present work, we extend the split VPHNN to the multistate VPHNN. We define its energy and a primitive learning algorithm, the Hebbian learning rule. In addition, we prove the stability of multistate VPHNNs. Furthermore, we investigated the fundamentals of multistate VPHNNs, such as the storage capacity and noise tolerance, by computer simulations.
D. Casasent - One of the best experts on this subject based on the ideXlab platform.
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Optical processing in Neural Networks
IEEE Intelligent Systems, 1992Co-Authors: D. CasasentAbstract:Hybrid Neural network hardware and several algorithms that use optical processing at various stages for image processing and pattern recognition are described. The implementations of the algorithms in associative processors, optimization Neural Networks, symbolic correlator Neural Networks, production system Neural Networks, and adaptive Neural Networks are discussed. Some of the optical processing concepts used in the Networks are presented. >
Tadao Nakamura - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Regression Quantum Neural Networks
arXiv: Emerging Technologies, 2020Co-Authors: Ngoc Diep, Koji Nagata, Tadao NakamuraAbstract:In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum Neural Networks (LS-QNN), and ploynomial interpolation quantum Neural Networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum Neural Networks (LR-QNN), polynomial regression quantum Neural Networks (PR-QNN), chi-squared quantum Neural netowrks ($\chi^2$-QNN). We observed that the method works also in the cases by using nonparametric statistics. In this paper we analyze and implement the nonparametric tests on QNN such as: linear nonparametric regression quantum Neural Networks (LNR-QNN), polynomial nonparametric regression quantum Neural Networks (PNR-QNN). The implementation is constructed through the Gauss-Jordan Elimination quantum Neural Networks (GJE-QNN).The training rule is to use the high probability confidence regions or intervals.
Nakamura Tadao - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Regression Quantum Neural Networks
2020Co-Authors: Diep, Do Ngoc, Nagata Koji, Nakamura TadaoAbstract:In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum Neural Networks (LS-QNN), and ploynomial interpolation quantum Neural Networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum Neural Networks (LR-QNN), polynomial regression quantum Neural Networks (PR-QNN), chi-squared quantum Neural netowrks ($\chi^2$-QNN). We observed that the method works also in the cases by using nonparametric statistics. In this paper we analyze and implement the nonparametric tests on QNN such as: linear nonparametric regression quantum Neural Networks (LNR-QNN), polynomial nonparametric regression quantum Neural Networks (PNR-QNN). The implementation is constructed through the Gauss-Jordan Elimination quantum Neural Networks (GJE-QNN).The training rule is to use the high probability confidence regions or intervals.Comment: 4 pages, no figure, LaTeX2
Katarzyna Czeczot - One of the best experts on this subject based on the ideXlab platform.
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Creativity of Neural Networks
Lecture Notes in Computer Science, 2006Co-Authors: Urszula Markowska-kaczmar, Katarzyna CzeczotAbstract:In the paper the ability of Neural Networks in creativity is tested. The creation of new words was chosen as an example task of creativity. Three different approaches based on the Neural Networks were designed and implemented to perform experiments. From all concerned solutions the best results was produced by the recurrent Neural network.
