A review on varying-parameter convergence differential neural network
Inspired by the nature of actual dynamics systems with time-varying parameters, varying-
parameter convergence differential neural network (termed as VP-CDNN) has been put …
parameter convergence differential neural network (termed as VP-CDNN) has been put …
A deep ensemble dynamic learning network for corona virus disease 2019 Diagnosis
Z Zhang, B Chen, Y Luo - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently
recognizing X-ray chest radiography images for automatically identifying corona virus …
recognizing X-ray chest radiography images for automatically identifying corona virus …
A jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problems
Z Zhang, Y Song, L Zheng, Y Luo - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Nonlinear inequalities are widely used in science and engineering areas, attracting the
attention of many researchers. In this article, a novel jump-gain integral recurrent (JGIR) …
attention of many researchers. In this article, a novel jump-gain integral recurrent (JGIR) …
An FPGA-implemented antinoise fuzzy recurrent neural network for motion planning of redundant robot manipulators
Z Zhang, H He, X Deng - IEEE transactions on neural networks …, 2023 - ieeexplore.ieee.org
When a robot completes end-effector tasks, internal error noises always exist. To resist
internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed …
internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed …
A punishment mechanism-combined recurrent neural network to solve motion-planning problem of redundant robot manipulators
In order to make redundant robot manipulators (RRMs) track the complex time-varying
trajectory, the motion-planning problem of RRMs can be converted into a constrained time …
trajectory, the motion-planning problem of RRMs can be converted into a constrained time …
A regularized orthogonal activated inverse-learning neural network for regression and classification with outliers
Z Zhang, Y Song, T Chen, J He - Neural Networks, 2024 - Elsevier
A novel regularized orthogonal activated inverse-learning (ROAIL) neural network is
proposed and investigated for reducing the impact of outliers in regression and classification …
proposed and investigated for reducing the impact of outliers in regression and classification …
Two types of anti-noise integral enhanced recurrent neural networks for solving time-varying complex quadratic programming
The time-varying complex quadratic programming problem plays an important role in
scientific research and engineering applications, and has received extensive attention. In …
scientific research and engineering applications, and has received extensive attention. In …
A mixture varying-gain dynamic learning network for solving nonlinear and nonconvex constrained optimization problems
R Lu, G Qiu, Z Zhang, X Deng, H Yang, Z Zhu, J Zhu - Neurocomputing, 2021 - Elsevier
Nonlinear and nonconvex optimization problem (NNOP) is a challenging problem in control
theory and applications. In this paper, a novel mixture varying-gain dynamic learning …
theory and applications. In this paper, a novel mixture varying-gain dynamic learning …
Traffic Flow Prediction via Weighted Combination of ARIMA and WASDNN Models
X Liu, Y Zhang, M Yang, Z Xue… - 2021 33rd Chinese …, 2021 - ieeexplore.ieee.org
The modeling and prediction of traffic flow are important in transportation study and traffic
network management. Different from individual models, a hybrid model which is a …
network management. Different from individual models, a hybrid model which is a …
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification
J He, T Chen, Z Zhang - arXiv preprint arXiv:1910.11552, 2019 - arxiv.org
Single-hidden layer feed forward neural networks (SLFNs) are widely used in pattern
classification problems, but a huge bottleneck encountered is the slow speed and poor …
classification problems, but a huge bottleneck encountered is the slow speed and poor …