Deep residual learning for nonlinear regression
D Chen, F Hu, G Nian, T Yang - Entropy, 2020 - mdpi.com
Deep learning plays a key role in the recent developments of machine learning. This paper
develops a deep residual neural network (ResNet) for the regression of nonlinear functions …
develops a deep residual neural network (ResNet) for the regression of nonlinear functions …
[HTML][HTML] Tunable superconducting neurons for networks based on radial basis functions
AE Schegolev, NV Klenov… - Beilstein Journal of …, 2022 - beilstein-journals.org
The hardware implementation of signal microprocessors based on superconducting
technologies seems relevant for a number of niche tasks where performance and energy …
technologies seems relevant for a number of niche tasks where performance and energy …
Stochastic neural network control for stochastic nonlinear systems with quadratic local asymmetric prescribed performance
This article presents an adaptive neural network control scheme with prescribed
performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control …
performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control …
Human activity recognition using accelerometer and photoplethysmographic signals
This paper presents an efficient technique for real-time recognition of human activities by
using accelerometer and photoplethysmography (PPG) data. It is based on singular value …
using accelerometer and photoplethysmography (PPG) data. It is based on singular value …
Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique
Z Naghibi, SA Sadrossadat… - International journal of …, 2019 - Wiley Online Library
In this paper, a new method called local‐global feedback recurrent neural network
(LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure …
(LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure …
The space–time kernel-based numerical method for Burgers' equations
It is well known that major error occur in the time integration instead of the spatial
approximation. In this work, anisotropic kernels are used for temporal as well as spatial …
approximation. In this work, anisotropic kernels are used for temporal as well as spatial …
Characterisation of the effects of sleep deprivation on the electroencephalogram using permutation Lempel–Ziv complexity, a non-linear analysis tool
Specific patterns of brain activity during sleep and waking are recorded in the
electroencephalogram (EEG). Time-frequency analysis methods have been widely used to …
electroencephalogram (EEG). Time-frequency analysis methods have been widely used to …
Speaker identification in noisy conditions using short sequences of speech frames
The application of speaker recognition technologies on domotic systems, cars, or mobile
devices such as tablets, smartphones and smartwatches faces with the problem of ambient …
devices such as tablets, smartphones and smartwatches faces with the problem of ambient …
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application
This paper presents the estimation of the state of charge (SOC) for a lithium-ion battery using
feature selection and an optimal NN algorithm. Principle component analysis (PCA) is used …
feature selection and an optimal NN algorithm. Principle component analysis (PCA) is used …
Combined Method of Neurocontrol for Nonlinear Non-Stationary Object
S Frolov, S Sindeev, A Korobov… - 2020 2nd International …, 2020 - ieeexplore.ieee.org
Problem of finding of optimal turning parameters for neurocontrol of nonlinear non-stationary
object in a presence of random disturbances is formulated. Combined method of …
object in a presence of random disturbances is formulated. Combined method of …