Titlebook: Artificial Intelligence and Soft Computing; 15th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

GROWL

https://doi.org/10.1007/978-3-531-91703-0fficulty is learning these networks. The article presents a analysis of deep neural network nonlinearity with polynomial approximation of neuron activation functions. It is shown that nonlinearity grows exponentially with the depth of the neural network. The effectiveness of the approach is demonstr

BUOY

Thomas Sommerer,Stephan Heichel M.A.in the process of neural network weight adaptation. The rest of the network weights is locked out (frozen). In contrast to the “dropout” method introduced by Hinton et al. [.], the neurons (along with their connections) are not removed from the neural network during training, only their weights are

Digest

Aprope

GRIEF

Harald Germann,Silke Raab,Martin Setzerlength of the type-reduced set as a measure of the uncertainty in an interval set. Greenfield and John argue that the volume under the surface of the type-2 fuzzy set is a measure of the uncertainty relating to the set. For an interval type-2 fuzzy set, the volume measure is equivalent to the area o

Scintigraphy

使隔離

Parallel Learning of Feedforward Neural Networks Without Error Backpropagationd on a new idea of learning neural networks without error backpropagation. The proposed solution is based on completely new parallel structures to effectively reduce high computational load of this algorithm. Detailed parallel 2D and 3D neural network learning structures are explicitely discussed.

鍍金

變色龍

Artificial Intelligence and Soft Computing978-3-319-39378-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

BLA

https://doi.org/10.1007/978-3-658-28770-2are nonlinear. A simple approximation of an often applied hyperbolic tangent activation function is presented. This proposed function is computationally highly effective. Computational comparisons for two well-known test problems are discussed. The results are very promising in potential applications to FPGA chips designing.