Neural networks: An overview of early research, current frameworks and new challenges
This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …
of neural networks, taking into account that two aims have emerged in this area: the …
Machine learning methods for turbulence modeling in subsonic flows around airfoils
In recent years, the data-driven turbulence model has attracted widespread concern in fluid
mechanics. The existing approaches modify or supplement the original turbulence model by …
mechanics. The existing approaches modify or supplement the original turbulence model by …
A distributed dynamic load identification method based on the hierarchical-clustering-oriented radial basis function framework using acceleration signals under …
Load identification is a hotly studied topic due to the widespread recognition of its
importance in structural design and health monitoring. This paper explores an effective …
importance in structural design and health monitoring. This paper explores an effective …
[ΒΙΒΛΙΟ][B] Complex-valued neural networks
A Hirose - 2006 - Wiley Online Library
Complex-valued neural networks Complex-Valued Neural Networks Page 2 IEEE Press 445
Hoes Lane Piscataway, NJ 08854 IEEE Press Editorial Board 2013 John Anderson, Editor in …
Hoes Lane Piscataway, NJ 08854 IEEE Press Editorial Board 2013 John Anderson, Editor in …
Enhanced probabilistic neural network with local decision circles: A robust classifier
In recent years the Probabilistic Neural Network (PPN) has been used in a large number of
applications due to its simplicity and efficiency. PNN assigns the test data to the class with …
applications due to its simplicity and efficiency. PNN assigns the test data to the class with …
New diagnostic EEG markers of the Alzheimer's disease using visibility graph
A new chaos–wavelet approach is presented for electroencephalogram (EEG)-based
diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph …
diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph …
Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder
A method is presented for investigation of EEG of children with autistic spectrum disorder
using complexity and chaos theory with the goal of discovering a nonlinear feature space …
using complexity and chaos theory with the goal of discovering a nonlinear feature space …
Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD
A multi-paradigm methodology is presented for electroencephalogram (EEG) based
diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) through adroit integration of …
diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) through adroit integration of …
Boundedness and complete stability of complex-valued neural networks with time delay
B Zhou, Q Song - IEEE Transactions on Neural Networks and …, 2013 - ieeexplore.ieee.org
In this paper, the boundedness and complete stability of complex-valued neural networks
(CVNNs) with time delay are studied. Some conditions to guarantee the boundedness of the …
(CVNNs) with time delay are studied. Some conditions to guarantee the boundedness of the …
Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms
Interval time series prediction is one of the most challenging research topics in the field of
time series modeling and prediction. In view of the remarkable function approximation …
time series modeling and prediction. In view of the remarkable function approximation …