A5105821300- Advanced Memory and Neural Computing
- Neural Networks Stability and Synchronization
- stochastic dynamics and bifurcation
- Chaos control and synchronization
- Stability and Control of Uncertain Systems
- Neural dynamics and brain function
- Advanced Neural Network Applications
- Neural Networks and Applications
- Quantum chaos and dynamical systems
- Distributed Control Multi-Agent Systems
- Combustion and Detonation Processes
- Nonlinear Dynamics and Pattern Formation
- Chaos-based Image/Signal Encryption
- Neuroscience and Neural Engineering
- Music and Audio Processing
- Brain Tumor Detection and Classification
- Speech and Audio Processing
- Risk and Safety Analysis
- AI in cancer detection
- Fire dynamics and safety research
- Machine Learning and ELM
- Stability and Controllability of Differential Equations
- VLSI and FPGA Design Techniques
- VLSI and Analog Circuit Testing
- Topic Modeling
University of Electronic Science and Technology of China
2016-2025
Changzhou University
2022-2024
National Archaeology Museum
2024
Defence Electronics Research Laboratory
2021-2023
Harbin Institute of Technology
2022
Nanjing University of Aeronautics and Astronautics
2015
Heilongjiang Provincial Hospital
2012
Qinhuangdao Second Hospital
2006
Activation functions facilitate deep neural networks by introducing non-linearity to the learning process. The feature gives network ability learn complex patterns. Recently, most widely used activation function is Rectified Linear Unit (ReLU). Though, other various existing including hand-designed alternatives ReLU have been proposed. However, none has succeeded in replacing due their inconsistencies. In this work, called ReLUMemristor-like Function (RMAF) proposed leverage benefits of...
Abstract This paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) novel (HSquash) function. The EFF aggregates extracted from 2‐lane convolutional layers, which provides rich information for better accuracy. HSquash proposed as function ensure that vectors are effectively squashed produces sparsity high discriminative extract important images with...
This article investigates the problem of robust exponential stability fuzzy switched memristive inertial neural networks (FSMINNs) with time-varying delays on mode-dependent destabilizing impulsive control protocol. The model presented here is treated as a system rather than employing theory differential inclusion and set-value map. To optimize exponentially stable process reduce cost time, hybrid adaptive feedback controllers are simultaneously applied to stabilize FSMINNs. In new model,...
This paper proposes a mem-computing model of memristive network-based genetic algorithm (MNGA) by building up the relationship between network (MN) and (GA), new edge detection where image pixels are defined as individuals population. First, computing MNGA is designed to perform mem-computing, which brings possibility hardware implementation GA. Secondly, MNGA-based integrating filter GA operator deployed MN proposed. Finally, simulation results demonstrate that figure merit (FoM) our better...
This article investigates the heterogeneous impulsive synchronization for T-S fuzzy probabilistic coupled delayed neural networks (CDNNs) with mode-dependent parameters and piecewise membership functions. To begin with, a novel CDNNs model adjustable coupling strength delays is designed to ensure accuracy of model. Meanwhile, generalized isolated node four types mismatched parameters, named network, first considered extend problem. Then, rules are introduced design model, which implies that...
In recent years, deep learning has been applied to many medical imaging fields, including image processing, bioinformatics, classification, segmentation, and prediction tasks. Computer-aided detection systems have widely adopted in brain tumor prediction, detection, diagnosis, segmentation This work proposes a novel model that combines the Bayesian algorithm with depth-wise separable convolutions for accurate classification predictions of tumors. We combine modeling Convolutional Neural...
This paper highlights the memristor bridge-based lowpass filter (LPF) and improved image processing algorithms along with a novel adaptive Gaussian for denoising new pyramid scale invariant feature transform (SIFT). First, kind of LPF based on bridge is designed, whose cut-off frequency other traits are demonstrated to change different time memristance. In light changeable parameter LPF, an SIFT algorithm presented. Finally, experiment results show that peak signalto-noise ratio (PSNR) our...
This article investigates the stability and stabilization problem for delay-dependent Takagi–Sugeno (T–S) fuzzy load-frequency control (LFC) power system with energy storage (ESS). First, a unified T–S LFC model is constructed ESS by further analyzing nonlinear characteristics existing in turbine governor dynamics. Second, proportional-integral strategy proposed to stabilize system. Then, based on approaches handle quadratic function respect time-varying delay, an improved...
Matrix operation is easy to be paralleled by hardware, and the memristor network can realize a parallel matrix computing model with in-memory computing. This article proposes matrix-friendly genetic algorithm (MGA), in which population represented evolution of realized operations. Compared performance baseline (GA) on solving maximum value binary function, MGA converge better faster. In addition, more efficient because its parallelism operations, runs 2.5 times faster than GA when using...
We propose an impulsive control scheme for fractional-order chaotic systems. Based on the Takagi–Sugeno (T-S) fuzzy model and linear matrix inequalities (LMIs), some sufficient conditions are given to stabilize system via control. Numerical simulation shows effectiveness of this approach.
This article addresses the problem of dynamic pinning synchronization fuzzy-dependent-switched (Fds) coupled memristive neural networks (CMNNs) with mismatched dimensions on time scales. To begin with, probabilistic coupling delays, scales, dimensions, and function projective rules are considered to design novel CMNNs improve reliability generalization ability model. Then Fds control (DPC) method adopted CMNNs, which can effectively promote information exchange between switching signals...