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

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ELUC

based on mutual information and extreme learning machines is proposed in this paper. Simple mutual information based feature selection method is integrated with the fast learning kernel based extreme learning machines to obtain better modeling performance. In the method, optimal number of the featur

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激怒

Franz H?useron unknown functions of the master system, an adaptive neural synchronization scheme is proposed with the combination of backstepping technique and dynamic surface control (DSC). This proposed method, similar to backstepping but with an important addition, can overcome the “explosion of complexity”

剝皮

Matthias Casperthe T-S fuzzy system using ellipsoidal basis functions. The salient characteristics of this approach are as follows: 1) A novel structure learning algorithm incorporating a pruning strategy into new growth criteria is developed. 2) Compact fuzzy rules can be extracted from training data. 3) The line

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Martin Tonnered neural network. By applying feedback control on a small fraction of network nodes with the dynamical feedback gains turning adaptively and combining the Lyapunov function method, an easy-to-verify sufficient condition is derived for globally asymptotically synchronization for the coupled network.

細(xì)絲

Walter Rudolf,Katja K?tterheinrichof firing activity followed a power law and the firing pattern was accompanied by nested .-and .-/.-oscillations. These results provided important evidence that neural oscillations could be formed during neuronal avalanches. The relationship between neuronal avalanches and oscillations has not been

種族被根除

Michael Beckhusenr systems in the presence of wind turbines and generation rate constraint (GRC). Neural networks are adopted to compensate the entire uncertainties. Simulation results show the validity and robustness of the presented method.

歌劇等

Stefan Frisch this paper. Only one NN (neural network) approximator is used to tackle unknown nonlinear functions at the last step and only one actual control law and one adaptive law are contained in the designed controller. This approach simplifies the controller design and alleviates the computational burden.