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Titlebook: Handbuch zum deutschen und europäischen Bankrecht; Peter Derleder,Kai-Oliver Knops,Heinz Georg Bamber Book 20092nd edition Springer-Verlag

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Wolfhard Kohteric variations. Neural networks are employed to approximate the uncertainties, including the parametric variations and the unknown load-resistance. The actual control laws are derived by using the dynamic surface control method. Furthermore, a linear tracking differentiator is introduced to replace
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Georg Borgesn images. It also demonstrates how important it is to choose the models type adequate to the systems under research..It also introduces an explication of the possibility to create the model of information and communication interactions that can illustrate transmission of information between two and
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of DNN application at different levels of speaker recognition system: i-vector extraction level and model Back-End level. Results of our study perform high efficiency of the proposed neural network based approaches for solving this problem. It is shown that the use of DNN technology at different le
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Peter Derledertimation of sufficiently large set of acoustic features, construction of multidimensional histograms and approximation arbitrary distributions of the components of acoustic features with probability density functions (PDF), with possibility of wide shape variation. Proposed method allowed to reduce
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Alexander Pallas,Kai-Oliver Knopssifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy usin
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Udo Reifnerceding horizon control problem for the HVAC system is formulated as sequential quadratic programs, which are solved by using a neurodynamic optimization model. Simulation results on temperature setpoint regulation of the HVAC system are discussed to substantiate the efficacy of the approach.
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Peter Derleder network online approximation and dynamic surface control. In the design, adaptive backstepping technique is employed to establish virtual control laws and actual control law recursively. The unknown functions contained in control laws are replaced by neural network online approximators. And dynamic
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Rolf Kronenburgestimation network for an unordered point cloud. Our approach utilizes EdgeConv layer as the basic element, where an attention embedding version EdgeConv layer is proposed for feature extraction in hand pose estimation task. To improve the result, we design a hand pose improvement network that input
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