Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function
Z Huang, X Du, L Chen, Y Li, M Liu, Y Chou… - IEEE Access, 2020 - ieeexplore.ieee.org
The diagnosis of brain tumor types generally depends on the clinical experience of doctors,
and computer-assisted diagnosis improves the accuracy of diagnosing tumor types …
and computer-assisted diagnosis improves the accuracy of diagnosing tumor types …
Memory recurrent Elman neural network-based identification of time-delayed nonlinear dynamical system
R Kumar - IEEE Transactions on Systems, Man, and …, 2022 - ieeexplore.ieee.org
In this article, an attempt has been made to propose an improved version of the classical
Elman neural network (ENN) and its application is presented to identify the unknown …
Elman neural network (ENN) and its application is presented to identify the unknown …
Intelligent maximum power factor searching control using recurrent Chebyshev fuzzy neural network current angle controller for SynRM drive system
To develop a high-performance synchronous reluctance motor (SynRM) drive system, a
novel maximum power factor control (MPFC) using a current angle controller with stator …
novel maximum power factor control (MPFC) using a current angle controller with stator …
A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment
This article presents a novel adaptive iterative learning control (AILC), and designs a human-
in-loop control pattern (HIL-CP), which simulates the proposed approach using different …
in-loop control pattern (HIL-CP), which simulates the proposed approach using different …
A novel neural network classifier using beetle antennae search algorithm for pattern classification
Traditional training algorithms in artificial neural networks (ANNs) show some inherent
weaknesses, such as the possibility of falling into local optimum, slow learning speed, and …
weaknesses, such as the possibility of falling into local optimum, slow learning speed, and …
Quantum neural networks
K Beer - arxiv preprint arxiv:2205.08154, 2022 - arxiv.org
This PhD thesis combines two of the most exciting research areas of the last decades:
quantum computing and machine learning. We introduce dissipative quantum neural …
quantum computing and machine learning. We introduce dissipative quantum neural …
[PDF][PDF] Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot.
Deep learning models often have complicated struc-tures with low computational speed and
the requirement of a large amount of storage space, which limits their own practical …
the requirement of a large amount of storage space, which limits their own practical …
Noise-tolerant zeroing neural network for solving non-stationary Lyapunov equation
As a crucial means for stability analysis in control systems, the Lyapunov equation is applied
in many fields of science and engineering. There are some methods proposed and studied …
in many fields of science and engineering. There are some methods proposed and studied …
A multitask learning framework for multi-property detection of wine
D Yu, X Wang, H Liu, Y Gu - IEEE Access, 2019 - ieeexplore.ieee.org
The electronic nose (E-nose) is a bionic olfactory system and a powerful tool in many fields.
Sample classification and parameter prediction are the core functions of the E-nose. We …
Sample classification and parameter prediction are the core functions of the E-nose. We …
Modified weights-and-structure-determination neural network for pattern classification of flatfoot
Flatfoot is a common disease in children and juveniles. If the disease is not controlled and
treated in time, it may last into adulthood, which can bring a great deal of inconvenience and …
treated in time, it may last into adulthood, which can bring a great deal of inconvenience and …