Article
Automation & Control Systems
Mingming Liang, Derong Liu
Summary: This article focuses on designing the optimal impulsive controller (IMC) of continuous-time nonlinear systems and proposes a new adaptive dynamic programming algorithm with high generality and feasibility. The introduced policy-improving mechanism makes the algorithm more flexible for memory-limited computing devices.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Antonio Sala, Leopoldo Armesto
Summary: This study introduces a new criterion for adaptive meshing in polyhedral partitions to interpolate value functions, employing an initial condition probability density function, uncertainty propagation, and temporal-difference error to determine the addition of new points. A collection of lemmas justifies the algorithmic proposal, with comparative analysis highlighting the advantages of this proposal over other options in literature. The developed methods are applied in simulation examples and an experimental robotic setup.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Linghuan Kong, Shuang Zhang, Xinbo Yu
Summary: An approximate optimal scheme is proposed for an uncertain n-link robot subject to saturation nonlinearity. The proposed method takes into account model uncertainty in robotic dynamics and designs an optimal control under the frame of adaptive dynamic programming. The method is proved to be effective in stabilizing the unknown system and reducing control cost.
Article
Automation & Control Systems
Derong Liu, Shan Xue, Bo Zhao, Biao Luo, Qinglai Wei
Summary: This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control, highlighting efficient algorithms and future research directions. ADP is applied in optimization, game theory, and large-scale systems, showing great potential in the era of artificial intelligence.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Longjie Zhang, Yong Chen
Summary: This article investigates the finite-time optimal control (FTOC) for affine-form nonlinear systems inspired by state optimization and finite-time convergence. The authors propose a novel finite-time adaptive dynamic programming (FTADP) approach to achieve optimal stability with finite response time. They also implement a novel adaptive dynamic programming (ADP) based on the finite-time critic-actor offline neural network (NN) approximation algorithm to solve the optimal controller with nonlinearity characteristic. The application analysis on circuit systems demonstrates the superiority of the proposed FTADP compared to general optimal control.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiong Yang, Yuanheng Zhu, Na Dong, Qinglai Wei
Summary: The study focuses on the decentralized event-driven control problem of nonlinear dynamical systems, transforming it into a group of nonlinear optimal control problems and solving it using event-driven Hamilton-Jacobi-Bellman equations to ensure overall system stability. The critic neural network architecture is utilized to address the problem, with Lyapunov method ensuring signal stability in closed-loop subsystems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Geyang Xiao, Huaguang Zhang
Summary: This article focuses on the convergence property and error bounds analysis of value iteration adaptive dynamic programming for continuous-time nonlinear systems. It introduces a contraction assumption to describe the relationship between the total value function and the single integral step cost. The convergence property of value iteration is proved under an arbitrary positive semidefinite function as the initial condition. The article also considers the accumulated effects of approximation errors generated in each iteration and proposes an error bounds condition to ensure the convergence of the approximated iterative results.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Fuyu Zhao, Weinan Gao, Zhong-Ping Jiang, Tengfei Liu
Summary: This article introduces an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. It reconstructs unmeasurable states and reduces controller updates through an event-based feedback strategy. The iterative solution to the discrete-time algebraic Riccati equation is carried out using event-triggered adaptive dynamic programming, with convergence and closed-loop stability verified using Lyapunov techniques.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Di Liu, Simone Baldi, Wenwu Yu, Guanrong Chen
Summary: Switch-based adaptive dynamic programming is an optimal control problem that minimizes cost by switching among dynamical modes. This correspondence proposes a distributed computational method to solve the problem by partitioning the system into agents and using a heuristic algorithm to avoid conflicts. The effectiveness of the method is verified through test cases.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Transportation Science & Technology
Hoa T. M. Nguyen, Andy H. F. Chow
Summary: This paper presents an adaptive optimization framework for dynamic rail transit network operations using a rollout surrogate-approximate dynamic programming method. The proposed framework reduces passengers' waiting times significantly with reasonable computational time. The results suggest the potential of the proposed optimizer for real-time applications in large-scale rail transit networks.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Computer Science, Artificial Intelligence
Zijie Guo, Hongyi Li, Hui Ma, Wei Meng
Summary: This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) using the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. The article introduces a one-to-one mapping technique to transform the constrained systems into equivalent unconstrained systems. It also develops an improved nonquadratic cost function and a novel tuning rule of critic neural network (NN) weights to overcome the issue of difficult persistence of excitation (PE) condition. The stability of the closed-loop system and the convergence of critic NN weights are proved using the Lyapunov stability theorem. Simulation results show the effectiveness of the proposed control strategy for multiple QUAVs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Yongliang Yang, Kyriakos G. Vamvoudakis, Hamidreza Modares, Yixin Yin, Donald C. Wunsch
Summary: This article introduces a model-based hybrid adaptive dynamic programming framework, including policy evaluation, improvement, and implementation steps, investigates the effect of sampling on communication bandwidth and control performance, shows a tradeoff between communication burden and performance, and demonstrates that developed policies exhibit Zeno behavior.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Mingming Ha, Ding Wang, Derong Liu
Summary: In this paper, a new approach is proposed to address the tracking control problem. By introducing a new cost function and a novel stability analysis method, the issue of incomplete elimination of tracking error in traditional approaches is solved. The specific implementation scheme for the special case of linear systems is also provided.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Automation & Control Systems
Qinglai Wei, Tianmin Zhou, Jingwei Lu, Yu Liu, Shuai Su, Jun Xiao
Summary: In this article, a new stochastic adaptive dynamic programming (ADP) method is developed to solve the optimal control problem of continuous-time (CT) time-invariant nonlinear systems with stochastic nonlinear disturbances. The method simultaneously approximates the value function and the control law under the conditional expectation. The asymptotic stability of the closed-loop stochastic system in probability is analyzed using the stochastic Lyapunov direct method, and the convergence of the developed ADP method is proven. Four simulations are conducted to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xuguang Hu, Zhaokang Zhan, Dazhong Ma, Tianbiao Wang, Hui Liu
Summary: This article proposes a data-driven signal evaluation method based on adaptive dynamic programming (ADP) to address the emergency handling of leak events in energy transportation systems. A three-layer neural network is used to establish a signal estimation model for pressure signal changes, and a flow signal evaluation method based on a value iteration scheme is developed to determine the leak flow rate. By incorporating abnormal signal analysis principles into the iterative solving process, the method is able to obtain results that closely align with actual leak event situations. Tested with different leak scenarios, the proposed method demonstrates reasonable and reliable evaluation outcomes.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yury Sokolov, Robert Kozma, Ludmilla D. Werbos, Paul J. Werbos
Article
Mathematics, Interdisciplinary Applications
Paul J. Werbos
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2015)
Article
Quantum Science & Technology
Paul J. Werbos, Ludmilla Dolmatova
QUANTUM INFORMATION PROCESSING
(2016)
Article
Computer Science, Artificial Intelligence
Paul J. Werbos
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2011)
Article
Computer Science, Artificial Intelligence
Roman Ilin, Robert Kozma, Paul J. Werbos
IEEE TRANSACTIONS ON NEURAL NETWORKS
(2008)
Editorial Material
Automation & Control Systems
Paul J. Werbos
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
(2008)
Article
Physics, Multidisciplinary
Paul J. Werbos
INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS
(2008)
Article
Computer Science, Artificial Intelligence
Ludmilla Werbos, Robert Kozma, Rodrigo Silva-Lugo, Giovanni E. Pazienza, Paul J. Werbos
Article
Computer Science, Artificial Intelligence
Paul J. Werbos
Proceedings Paper
Engineering, Electrical & Electronic
Paul J. Werbos
QUANTUM INFORMATION AND COMPUTATION XIII
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Paul J. Werbos
QUANTUM INFORMATION AND COMPUTATION XII
(2014)
Article
Economics
Paul J. Werbos
Review
Mathematics, Interdisciplinary Applications
PJ Werbos
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2002)
Article
Biology
WJ Freeman, R Kozma, PJ Werbos
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.