Construction of feedforward neural networks with simple architectures and approximation abilities
Sign Up to like & getrecommendations! Published in 2020 at "Mathematical Methods in the Applied Sciences"
DOI: 10.1002/mma.6876
Abstract: The universal approximation property of feedforward neural networks (FNNs) is the basis for all FNNs applications. In almost all existing FNN approximation studies, it is always assumed that the activation function of the network satisfies… read more here.
Keywords: feedforward neural; neural networks; activation function; function ... See more keywords
An online self-organizing algorithm for feedforward neural network
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04907-6
Abstract: Feedforward neural network (FNN) is the most popular network model, and the appropriate structure and learning algorithms are the key of its performance. This paper proposes an online self-organizing algorithm for feedforward neural network (OSNN)… read more here.
Keywords: network; self organizing; structure; feedforward neural ... See more keywords
Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?
Sign Up to like & getrecommendations! Published in 2019 at "Journal of affective disorders"
DOI: 10.1016/j.jad.2019.07.063
Abstract: BACKGROUND Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to evaluate whether machine learning models can improve upon statistical models for quantifying… read more here.
Keywords: feedforward neural; neural network; logistic regression; administrative health ... See more keywords
Online gradient method with smoothing ℓ0 regularization for feedforward neural networks
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.10.057
Abstract: źp regularization has been a popular pruning method for neural networks. The parameter p was usually set as 0 < p ź 2 in the literature, and practical training algorithms with ź0 regularization are lacking… read more here.
Keywords: neural networks; smoothing regularization; method; regularization ... See more keywords
Functional connectome fingerprinting using shallow feedforward neural networks
Sign Up to like & getrecommendations! Published in 2021 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2021852118
Abstract: Although individual subjects can be identified with high accuracy using correlation matrices computed from resting-state functional MRI (rsfMRI) data, the performance significantly degrades as the scan duration is decreased. Recurrent neural networks can achieve high… read more here.
Keywords: neural networks; connectome fingerprinting; functional connectome; shallow feedforward ... See more keywords
Temperature Estimation of PMSM Using a Difference-Estimating Feedforward Neural Network
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3009503
Abstract: Keeping the temperature of a permanent magnet synchronous machine (PMSM) in a safe range is essential for maintaining machine performance. In this paper, a feedforward neural network (FNN) for the temperature estimation of a PMSM… read more here.
Keywords: neural network; pmsm; temperature; feedforward neural ... See more keywords
Feedforward Neural Network-Based Data Aggregation Scheme for Intrabody Area Nanonetworks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Systems Journal"
DOI: 10.1109/jsyst.2020.3043827
Abstract: An intrabody area nanonetwork (intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling noninvasive healthcare monitoring and disease diagnosis. In this article, we design a novel feedforward… read more here.
Keywords: based data; data aggregation; intrabody area; scheme ... See more keywords
A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2017.2748585
Abstract: In this paper, we propose four new variants of the backpropagation algorithm to improve the generalization ability for feedforward neural networks. The basic idea of these methods stems from the Group Lasso concept which deals… read more here.
Keywords: neural networks; novel pruning; group; pruning algorithm ... See more keywords
Improved Architecture and Configurations of Feedforward Neural Networks to Increase Forecasting Accuracy for Moments of Finite Normal Mixtures
Sign Up to like & getrecommendations! Published in 2019 at "Pattern Recognition and Image Analysis"
DOI: 10.1134/s1054661819010115
Abstract: The methodology of forecasting using neural networks for modified data with original values replaced by discrete ones was successfully applied in previous authors’ works for mixture moments approximated with method of moving separation of mixtures.… read more here.
Keywords: configurations feedforward; accuracy; neural networks; improved architecture ... See more keywords
On the Comparison of Line Spectral Frequencies and Mel-Frequency Cepstral Coefficients Using Feedforward Neural Network for Language Identification
Sign Up to like & getrecommendations! Published in 2018 at "Indonesian Journal of Electrical Engineering and Computer Science"
DOI: 10.11591/ijeecs.v10.i1.pp168-175
Abstract: Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden… read more here.
Keywords: mel frequency; network; spectral frequencies; line spectral ... See more keywords
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
Sign Up to like & getrecommendations! Published in 2022 at "Future Internet"
DOI: 10.3390/fi14080240
Abstract: Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration… read more here.
Keywords: network; neural networks; accuracy; detection ... See more keywords