Latest research trends in gait analysis using wearable sensors and machine learning: A systematic review
Gait is the locomotion attained through the movement of limbs and gait analysis examines
the patterns (normal/abnormal) depending on the gait cycle. It contributes to the …
the patterns (normal/abnormal) depending on the gait cycle. It contributes to the …
Memristor devices for neural networks
H Jeong, L Shi - Journal of Physics D: Applied Physics, 2018 - iopscience.iop.org
Neural network technologies have taken center stage owing to their powerful computing
capability for supporting deep learning in artificial intelligence. However, conventional …
capability for supporting deep learning in artificial intelligence. However, conventional …
A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function
F Yu, L Liu, L Xiao, K Li, S Cai - Neurocomputing, 2019 - Elsevier
Nonlinear activation functions play an important role in zeroing neural network (ZNN), and it
has be proved that ZNN can achieve finite-time convergence when the sign-bi-power (SBP) …
has be proved that ZNN can achieve finite-time convergence when the sign-bi-power (SBP) …
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Speech separation algorithms are often used to separate the target speech from other
interfering sources. However, purely neural network based speech separation systems often …
interfering sources. However, purely neural network based speech separation systems often …
Control framework for cooperative robots in smart home using bio-inspired neural network
In this paper, we present a model-free tracking controller for a cooperative mobile-
manipulators, which are the cornerstone for future smart homes. The mobile-manipulators …
manipulators, which are the cornerstone for future smart homes. The mobile-manipulators …
Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function
Bartels–Stewart algorithm is an effective and widely used method with an O (n 3) time
complexity for solving a static Sylvester equation. When applied to time-varying Sylvester …
complexity for solving a static Sylvester equation. When applied to time-varying Sylvester …
Integration-enhanced Zhang neural network for real-time-varying matrix inversion in the presence of various kinds of noises
Matrix inversion often arises in the fields of science and engineering. Many models for matrix
inversion usually assume that the solving process is free of noises or that the denoising has …
inversion usually assume that the solving process is free of noises or that the denoising has …
Noise-suppressing zeroing neural network for online solving time-varying matrix square roots problems: A control-theoretic approach
In this paper, the noise-suppressing zeroing neural network models (NSZNNMs) for online
solving time-varying matrix square roots problems (TVMSRPs) are revisited and redesigned …
solving time-varying matrix square roots problems (TVMSRPs) are revisited and redesigned …
[HTML][HTML] Zeroing neural networks: A survey
Using neural networks to handle intractability problems and solve complex computation
equations is becoming common practices in academia and industry. It has been shown that …
equations is becoming common practices in academia and industry. It has been shown that …
Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations
In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this
paper proposes two nonlinear recurrent neural networks based on two nonlinear activation …
paper proposes two nonlinear recurrent neural networks based on two nonlinear activation …