Titlebook: Artificial Intelligence and Soft Computing; 11th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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https://doi.org/10.1007/978-3-658-26632-5lem which must be solved on-line. A linear approximation of the model for the current operating point can be used for prediction in MPC, but for significantly nonlinear processes control accuracy may be not sufficient. MPC algorithm in which the neural model is linearised on-line along a trajectory

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FANG

Fachgespräche auf der 14. GI-Jahrestagungg lattices or fully connected graphs. We present numerical results showing that as the spectrum (set of eigenvalues of adjacency matrix) of the resulting activity-based network develops a scale-free dependency. Moreover it strengthens and becomes valid for a wider segment along with the simulation p

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An Innovative Hybrid Neuro-wavelet Method for Reconstruction of Missing Data in Astronomical Photomebservations the most important difficulties in properly identifying the true oscillation frequencies of the stars are produced by the gaps in the observation time-series and the presence of atmospheric plus the intrinsic stellar granulation noise, unavoidable also in the case of space observations.

capillaries

Speeding Up the Training of Neural Networks with CUDA Technologyorithms like Levenberg-Marquardt. Parallel architectures have been a common solution in the area of high performance computing, since the technology used in current processors is reaching the limits of speed. An architecture that has been gaining popularity is the GPGPU (General-Purpose computing on

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CRAMP

Selection of Activation Functions in the Last Hidden Layer of the Multilayer Perceptrone least squares method is used. The proposed ways make it possible to decrease the cost function value. They enable achievement of a good compromise between the network complexity and the results being obtained. The methods do not require a start of learning of neural networks from the very beginnin

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On Learning in a Time-Varying Environment by Using a Probabilistic Neural Network and the Recursive in time-varying environment. The general regression neural network is based on the orthogonal-type kernel functions. The appropriate algorithm is presented in a recursive form. Sufficient simulations confirm empirically the convergence of the algorithm.