Results 201 to 210 of about 3,700 (260)
Uncertainty-aware gamma interaction localization and reconstruction in PET. [PDF]
Med PhysThull J +5 more
europepmc +1 more source
Multilayer perceptron neural network approach for power quality improvement in a grid integrated PV and electric vehicle systems. [PDF]
PLoS OneDas SR +5 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Evolving Multilayer Perceptrons
Neural Processing Letters, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pedro Ángel Castillo Valdivieso +5 more
openaire +2 more sources
A hyperbolic multilayer perceptron
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000We present a novel MLP-type neural network based on hyperbolic numbers $the hyperbolic multilayer perceptron (HMLP). The neurons of the HMLP compute 2D-hyperbolic orthogonal transformations as weight propagation functions. The HMLP can therefore be seen as the hyperbolic counterpart of the known complex MLP.
Sven Buchholz 0001, Gerald Sommer
openaire +1 more source
Multilayer perceptrons and fractals
Information Sciences, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
C. A. Murthy, Jennifer Pittman
openaire +2 more sources
2011 24th SIBGRAPI Conference on Graphics, Patterns and Images, 2011
We enhance the Multi layer Perceptron to map a feature vector not only from the original d-dimensional feature space, but from an intermediate implicit Hilbert feature space in which kernels calculate inner products. The kernel substitutes the usual inner product between weight vectors and the input vector (or the feature vector of the hidden layer ...
Thomas W. Rauber, Karsten Berns
openaire +1 more source
We enhance the Multi layer Perceptron to map a feature vector not only from the original d-dimensional feature space, but from an intermediate implicit Hilbert feature space in which kernels calculate inner products. The kernel substitutes the usual inner product between weight vectors and the input vector (or the feature vector of the hidden layer ...
Thomas W. Rauber, Karsten Berns
openaire +1 more source
Adaptive multilayer perceptrons
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003Multilayer perceptrons (MLP) with long- and short-term memories (LASTM) are proposed for adaptive processing. The activation functions of the output neurons of such a network are linear and thus the weights in the last layer affect the outputs of the network linearly and are called linear weights.
James T. Lo, Devasis Bassu
openaire +1 more source
Robustness in Multilayer Perceptrons
Neural Computation, 1993In this paper, we study the robustness of multilayer networks versus the destruction of neurons. We show that the classical backpropagation algorithm does not lead to optimal robustness and we propose a modified algorithm that improves this capability.
P. Kerlirzin, F. Vallet
openaire +1 more source
Hyperconic Multilayer Perceptron
Neural Processing Letters, 2016This paper introduces the design of the hyperconic multilayer perceptron (HC-MLP). Complex non-linear decision regions for classification purposes are generated by quadratic hyper-surfaces spawned by the hyperconic neurons in the hidden layer (for instance, spheres, ellipsoids, paraboloids, hyperboloids and degenerate conics).
Juan Pablo Serrano-Rubio +2 more
openaire +1 more source
Self-Organizing MultiLayer Perceptron
IEEE Transactions on Neural Networks, 2010In this paper, we propose an extension of a self-organizing map called self-organizing multilayer perceptron (SOMLP) whose purpose is to achieve quantization of spaces of functions. Based on the use of multilayer perceptron networks, SOMLP comprises the unsupervised as well as supervised learning algorithms.
openaire +3 more sources