Results 71 to 80 of about 4,010,683 (211)

Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks [PDF]

, 2008
Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization.
Sendhoff, Bernhard, Yaochu Jin, Bernhard Sendhoff, Jin, Yaochu, Kowalczykiewicz, Michal, Yi Cao, Cao, Yi, Michal Kowalczykiewicz, Sendhoff, B, Kowalczykiewicz, M, Cao, Y, Jin, Y, Jin, Yaochu ; https://orcid.org/   +12 more
core   +1 more source

Interacting neural networks [PDF]

Physical Review E, 2000
Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbour. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated.
Kinzel, W., Metzler, R., Kanter, I.
openaire   +3 more sources

A supplemental receiver coil recovers frontal and subcortical functional magnetic resonance imaging signals under half-volume head coil configuration

Neuroscience Research
The need for multisensory devices such as virtual reality and touch during functional magnetic resonance imaging (fMRI) is increasing. However, implementation of those devices requires a large presentation system, and the face-covering receiver coil ...
Yucong Yuan, Toshiko Tanaka, Daisuke Nishiyama, Yasuhiro Shimada, Ichiro Fujimoto, Tomoki Haji, Atsushi Wada, Masahiko Haruno   +7 more
doaj   +1 more source

Application of a Modified Generalized Regression Neural Networks Algorithm in Economics and Finance [PDF]

, 2011
In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) in S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts
Giovanis, Eleftherios
core  

Cortical representational geometry of diverse tasks reveals subject-specific and subject-invariant cognitive structures

Communications Biology
The variability in brain function forms the basis for our uniqueness. Prior studies indicate smaller individual differences and larger inter-subject correlation (ISC) in sensorimotor areas than in the association cortex.
Tomoya Nakai, Rieko Kubo, Shinji Nishimoto   +2 more
doaj   +1 more source

A new delay-independent condition for global robust stability of neural networks with time delays

, 2021
This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope ...
Samli, RÜYA
core   +1 more source

Efficient musculoskeletal annotation using free-form deformation

Scientific Reports
Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability.
Norio Fukuda, Shoji Konda, Jun Umehara, Masaya Hirashima   +3 more
doaj   +1 more source

Structural and functional features characterizing the brains of individuals with higher controllability of motor imagery

Scientific Reports
Motor imagery is a higher-order cognitive brain function that mentally simulates movements without performing the actual physical one. Although motor imagery has attracted the interest of many researchers, and mental practice utilizing motor imagery has ...
Tomoya Furuta, Tomoyo Morita, Gen Miura, Eiichi Naito   +3 more
doaj   +1 more source

Merging of Neural Networks

Neural Processing Letters
AbstractWe propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the original ones. We do this by carefully selecting channels from each input network. Our procedure might be used as a finalization step after one tries multiple starting seeds to avoid an unlucky ...
Martin Pasen, Vladimír Boza
openaire   +3 more sources

A broad class of discrete-time hypercomplex-valued hopfield neural networks

, 2020
In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercomplex ...
Valle, Marcos Eduardo, Castro, Fidelis Zanetti de   +1 more
core   +1 more source