Results 111 to 120 of about 852,410 (222)

Nonlinear Systems Identification Using Deep Dynamic Neural Networks [PDF]

arXiv, 2016
Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with ...
arxiv  

Fetal Akinesia/Hypokinesia and Arthrogryposis of Neuromuscular Origin: Etiologic Groups, Genetics, and Phenotypic Spectrum

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To provide a comprehensive clinical and genetic characterization of individuals with arthrogryposis multiplex congenita (AMC), focusing on the distribution of genetic etiologies across the neuromuscular spectrum and comparing myogenic and neurogenic subtypes. Methods A total of 105 individuals with AMC were clinically and genetically
Florencia Pérez‐Vidarte, Berta Estévez‐Arias, Leslie Matalonga, Delia Yubero, Anna Codina, Carlos Ortez, Julita Medina, Lidia DeSena DeCabo, Laura Carrera‐García, Jesica Expósito‐Escudero, Cristina Jou, Eduardo F. Tizzano, Andres Nascimento, Daniel Natera‐de Benito   +13 more
wiley   +1 more source

Boosted Jet Tagging with Jet-Images and Deep Neural Networks

EPJ Web of Conferences, 2016
Building on the jet-image based representation of high energy jets, we develop computer vision based techniques for jet tagging through the use of deep neural networks.
Kagan Michael, de Oliveira Luke, Mackey Lester, Nachman Benjamin, Schwartzman Ariel   +4 more
doaj   +1 more source

Longitudinal Relationship Between Pain and Depression in People With Inflammatory Arthritis: A Narrative Review

Arthritis Care &Research, EarlyView.
As many patients with inflammatory arthritis (IA) have chronic pain, understanding how to best assess and manage pain in IA is a priority. Comorbid depression is prevalent in adults with IA, affecting 15% to 39% of people. Although pain and depression are thought to be associated in IA, this concept is largely based on cross‐sectional data.
Natasha Cox, Saeed Farooq, Helen Twohig, Ian C. Scott   +3 more
wiley   +1 more source

Deep Neural Networks for Pattern Recognition [PDF]

arXiv, 2018
In the field of pattern recognition research, the method of using deep neural networks based on improved computing hardware recently attracted attention because of their superior accuracy compared to conventional methods. Deep neural networks simulate the human visual system and achieve human equivalent accuracy in image classification, object ...
arxiv  

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks [PDF]

arXiv, 2017
A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy.
arxiv  

Performance Triggered Adaptive Model Reduction for Soil Moisture Estimation in Precision Irrigation

International Journal of Adaptive Control and Signal Processing, EarlyView.
ABSTRACT Accurate soil moisture information is essential for precise irrigation to enhance water use efficiency. Estimating soil moisture based on limited soil moisture sensors is especially critical for obtaining comprehensive soil moisture information when dealing with large‐scale agricultural fields.
Sarupa Debnath, Bernard Twum Agyeman, Soumya Ranjan Sahoo, Xunyuan Yin, Jinfeng Liu   +4 more
wiley   +1 more source

Neural Network Adaptive Control With Long Short‐Term Memory

International Journal of Adaptive Control and Signal Processing, EarlyView.
ABSTRACT In this study, we propose a novel adaptive control architecture that provides dramatically better transient response performance compared to conventional adaptive control methods. This is accomplished by the synergistic employment of a traditional adaptive neural network (ANN) controller and a long short‐term memory (LSTM) network.
Emirhan Inanc, Abdullah Habboush, Yigit Gurses, Yildiray Yildiz, Anuradha M. Annaswamy   +4 more
wiley   +1 more source