Results 31 to 40 of about 66,291 (256)
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes
, 2014 A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes. The method unifies and generalizes two known separate modeling approaches, Echo State Networks (ESN) and Linear Inverse Modeling ...
Galtier, Mathieu N. +3 more
core +2 more sources
On the tractability of multivariate integration and approximation by neural networks
Journal of Complexity, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based
E3S Web of Conferences, 2018 Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model.
Yasin Hasbi +3 more
doaj +1 more source
MathematicsThis paper deals with a family of normalized multivariate neural network (MNN) operators of complex-valued continuous functions for a multivariate context on a box of RN¯, N¯∈N.
Seda Karateke
doaj +1 more source
Higher Order Multivariate Fuzzy Approximation by basic Neural Network Operators [PDF]
Cubo (Temuco), 2014 Summary: Here are studied in terms of multivariate fuzzy high approximation to the multivariate unit basic sequences of multivariate fuzzy neural network operators. These operators are multivariate fuzzy analogs of earlier studied multivariate real ones. The produced results generalize earlier real ones into the fuzzy setting. Here the high order multi-
openaire +2 more sources
A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions.
PLoS ONE, 2015 Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural ...
Patricia Wollstadt +2 more
doaj +1 more source
Control of Complex Dynamic Systems by Neural Networks [PDF]
, 1993 This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law.
Cristion, John A., Spall, James C.
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou +15 more
wiley +1 more source
Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
Advanced Functional Materials, EarlyView.An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source