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Nonlinear Dynamics

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Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning.

Neural networks : the official journal of the International Neural Network Society
This paper addresses learning with exploration driven by chaotic internal dynamics of a neural network. Hoerzer et al. showed that a chaotic reservoir network (RN) can learn with exploration driven by external random noise and a sequential reward. In...

Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

Computer methods in biomechanics and biomedical engineering
In order to have research on the deformation characteristics and mechanical properties of human red blood cells (RBCs), finite element models of RBC optical tweezers stretching and atomic force microscope (AFM) indentation were established. Non-linea...

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography.

Computational and mathematical methods in medicine
In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification syst...

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning.

Computational and mathematical methods in medicine
Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients' health...

Integral reinforcement learning based event-triggered control with input saturation.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel integral reinforcement learning (IRL)-based event-triggered adaptive dynamic programming scheme is developed for input-saturated continuous-time nonlinear systems. By using the IRL technique, the learning system does not requir...

A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling.

Neural networks : the official journal of the International Neural Network Society
Approaching to the biological neural network, small-world neural networks have been demonstrated to improve the generalization performance of artificial neural networks. However, the architecture of small-world neural networks is typically large and ...

Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Spatial and spatio-temporal epidemiology
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regress...

Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Neural networks : the official journal of the International Neural Network Society
Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. Th...

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

PloS one
Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of...

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

PloS one
OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to pre...