Article
Mathematics, Applied
Yanshou Dong, Junfang Zhao, Xu Miao, Ming Kang
Summary: This paper investigates the piecewise pseudo almost periodic solutions of a class of interval general BAM neural networks with mixed time-varying delays and impulsive perturbations. The existence conditions for these solutions are obtained through the exponential dichotomy of linear differential equations and the fixed point theory of contraction mapping. The global exponential stability of the piecewise pseudo almost periodic solutions is discussed using differential inequality techniques and mathematical methods of induction. An illustrative example is provided to demonstrate the effectiveness of the obtained results.
Article
Computer Science, Interdisciplinary Applications
G. Rajchakit, R. Sriraman, C. P. Lim, B. Unyong
Summary: This paper analyzes the global asymptotic stability and global exponential stability of Clifford-valued neutral-type neural network models with time delays. By considering the neutral term, a Clifford-valued neural network model with time delays is formulated, encompassing real-valued, complex-valued, and quaternion-valued neural network models as special cases. With the decomposition of the n-dimensional Clifford-valued neural network model into 2mn-dimensional real-valued models, a proper function is constructed to handle the neutral term and prove the existence of the equilibrium point. By utilizing homeomorphism theory, linear matrix inequality, and Lyapunov functional methods, sufficient conditions for the existence, uniqueness, and global asymptotic stability of the equilibrium point for the Clifford-valued neutral-type neural network model are derived. Numerical examples are provided to demonstrate the effectiveness of the results, and the simulation results are analyzed and discussed.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Computer Science, Artificial Intelligence
Moez Ayachi
Summary: This paper focuses on a class of Bi-directional associative memory neural networks (BAMNNs) with hybrid time-varying delays and D operator. The authors establish sufficient conditions to ensure the existence and global exponential stability of measure-pseudo almost periodic solutions for the considered BAMNNs. A numerical example and a graphical illustration are provided to support the effectiveness and feasibility of the obtained results. The results in this paper are original and complement the previous outcomes.
Article
Mathematics, Applied
Li Wan, Qinghua Zhou, Hongbo Fu, Qunjiao Zhang
Summary: This paper investigates the problem of exponential stability of Hopfield neural networks of neutral type with multiple time-varying delays. Novel sufficient conditions for the exponential stability are established using Lyapunov method and inequality techniques. The mathematical expression of the neutral-type system is more general and the established algebraic conditions are less conservative compared to some references.
Article
Computer Science, Artificial Intelligence
Yin Sheng, Tingwen Huang, Zhigang Zeng, Xiangshui Miao
Summary: This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays. The study uses inequality techniques, theories of the M-matrix, and the comparison strategy to consider the stability of the networks, providing less conservative methods for analyzing Lyapunov stability.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Daniel Melchor-Aguilar, Hector Arismendi-Valle
Summary: A complete type Lyapunov functional is presented for integral delay systems with constant kernels and multiple delays, introducing the concept of Lyapunov matrix and its properties for such systems. The constructed functionals are applied to derive exponential estimates for the solutions.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Da Chen, Xingwen Liu, Kaibo Shi, Shiquan Shao, Huazhang Wang
Summary: This paper addresses the issue of exponential stability for discrete-time systems with slowly time-varying delays. By utilizing the switching transformation approach, the original system is transformed into an equivalent switched system with a special switching signal. The method of switched homogeneous polynomial Lyapunov function is applied for the first time to general delayed systems, and some exponential stability conditions are proposed in various situations. Numerical results show that the conservativeness of the proposed conditions decreases as the degree of the switched homogeneous polynomial Lyapunov function increases.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Shang Gao, Keyu Peng, Chunrui Zhang
Summary: This paper presents a novel method to investigate the existence and global exponential stability of periodic solutions for feedback control complex dynamical networks with time-varying delays, utilizing the continuation theorem of coincidence degree theory, a combinatorial identity about Kirchhoff's matrix tree theorem in graph theory, and Lyapunov method. The effectiveness and practicability of the results are demonstrated through a numerical example and its simulation.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Automation & Control Systems
Bin Yang, Zefei Yan, Xuejun Pan, Xudong Zhao
Summary: This paper focuses on the stability analysis of linear systems with time-varying delays. By introducing a more suitable Lyapunov-Krasovskii functional and new delay-dependent estimation methods, less conservative criteria in the form of linear matrix inequality have been derived. The advantages and effectiveness of the newly proposed methods are verified through numerical examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Information Systems
Li Zhu, Er-Yong Cong, Xian Zhang
Summary: In this paper, the issue of state estimation is studied for a class of discrete-time bidirectional associative memory neural networks (DTBAMNNs) with multiple time-varying delays. By proposing a mathematical induction method, the authors investigate novel delay-dependent and -independent global exponential stability (GES) criteria of the error system. The obtained GES criteria are described by linear scalar inequalities. Then, a state observer is derived via the theory of generalized matrix inverses. These exponential stability conditions are very simple, which is convenient to verify based on the standard software tools (for example, YALMIP). Finally, two illustrative examples are presented to demonstrate the effectiveness of the theoretical results.
Article
Computer Science, Artificial Intelligence
Mengying Yan, Jigui Jian, Sheng Zheng
Summary: This paper investigates the passivity of uncertain BAM inertial neural networks with time-varying delays, proposing new Lyapunov functionals and delay-dependent criteria based on linear matrix inequalities to ensure the passivity of the systems. Numerical simulations demonstrate the effectiveness of the proposed approach.
Article
Automation & Control Systems
Malek Ghanes, Jaime A. Moreno, Jean-Pierre Barbot
Summary: In this paper, an arbitrary order differentiator with time-varying homogeneity degree is proposed, and its convergence properties and robustness are analyzed. Two possible applications are also introduced, one ensures convergence in finite time and the other improves the differentiator's behavior with respect to measurement noise.
Article
Computer Science, Artificial Intelligence
Shaoxin Sun, Xin Dai, Ruipeng Xi, Juan Zhang
Summary: This paper discusses passive fault-tolerant control for uncertain switched nonlinear random systems with multiple interval time-varying delays and intermittent faults. A practical method is suggested, showing the feasibility of noise-to-state exponentially mean-square stability under various time delay cases.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Automation & Control Systems
Xiaonan Liu, Minghui Song, Yonggui Kao
Summary: This paper investigates the synchronization between two hyperbolic coupled networks (HCNs) with time-varying delays using aperiodically intermittent pinning control (AIPC). Sufficient criteria for HCNs with internal delays only and with hybrid delays are obtained based on a Lyapunov function with a piecewise continuous function. It is found that HCNs with hybrid delays have a slower convergence speed compared to those with internal delays only. Additionally, two simulation results are presented to validate the theoretical findings.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Qiang Xiao, Zhenkun Huang, Zhigang Zeng, Tingwen Huang, Frank L. Lewis
Summary: This note focuses on the stability analysis of positive homogeneous nonlinear systems with bounded time-varying delays. It extends previous results by studying the exponential stability of a class of delayed homogeneous systems with degree confined to an interval based on the positivity property. The state boundary of the system at any fixed time is further investigated, providing more precise results in some cases. Finite-time stability is also obtained for a delay-free homogeneous system, along with an explicit boundary.
Article
Multidisciplinary Sciences
Felicitas Perez-Ornelas, Olivia Mendoza, Patricia Melin, Juan R. Castro, Antonio Rodriguez-Diaz, Oscar Castillo
Article
Computer Science, Information Systems
Juan R. Castro, Oscar Castillo, Mauricio A. Sanchez, Olivia Mendoza, Antonio Rodriguez-Diaz, Patricia Melin
INFORMATION SCIENCES
(2016)
Article
Computer Science, Information Systems
Juan R. Castro, Oscar Castillo, Patricia Melin, Antonio Rodriguez-Diaz
INFORMATION SCIENCES
(2009)
Article
Computer Science, Information Systems
Cecilia Leal-Ramirez, Oscar Castillo, Patricia Melin, Antonio Rodriguez-Diaz
INFORMATION SCIENCES
(2011)
Article
Engineering, Environmental
Gabriela Lozano-Olvera, Sara Ojeda-Benitez, Juan Ramon Castro-Rodriguez, Miguel Bravo-Zanoguera, Antonio Rodriguez-Diaz
RESOURCES CONSERVATION AND RECYCLING
(2008)
Article
Computer Science, Artificial Intelligence
Oscar Castillo, Juan R. Castro, Patricia Melin, Antonio Rodriguez-Diaz
Proceedings Paper
Computer Science, Artificial Intelligence
Gabriela E. Martinez, Olivia Mendoza, Juan R. Castro, A. Rodriguez-Diaz, Patricia Melin, Oscar Castillo
2015 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY DIGIPEN NAFIPS 2015
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
C. I. Gonzalez, J. R. Castro, O. Mendoza, A. Rodriguez-Diaz, P. Melin, O. Castillo
2015 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY DIGIPEN NAFIPS 2015
(2015)
Proceedings Paper
Computer Science, Theory & Methods
Ernesto R. Alvarez-Molina, Luis G. Martinez, Manuel Castanon-Puga, Antonio Rodriguez-Diaz
2014 SECOND WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS)
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Juan Paulo Alvarado-Magana, Antonio Rodriguez-Diaz, Juan R. Castro, Oscar Castillo
SOFT COMPUTING APPLICATIONS IN OPTIMIZATION, CONTROL, AND RECOGNITION
(2013)
Article
Computer Science, Artificial Intelligence
Oscar Castillo, Juan R. Castro, Patricia Melin, Antonio Rodriguez-Diaz
ADVANCES IN FUZZY SYSTEMS
(2013)
Proceedings Paper
Computer Science, Artificial Intelligence
Luis G. Martinez, Juan R. Castro, Guillermo Licea, Antonio Rodriguez-Diaz
ADVANCES IN SOFT COMPUTING, PT II
(2011)
Review
Medicine, General & Internal
Maria Evangelina Herran Paz, Raul Ortiz Monasterio, Maria Amparo Herran Ramirez, Antonio Rodriguez-Diaz, Ana Karen Garcia Villalpando
Article
Computer Science, Artificial Intelligence
Luis G. Martinez, Juan R. Castro, Antonio Rodriguez-Diaz, Guillermo Licea
INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS
(2012)
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.
