Wandong ZhangYimin YangQ. M. Jonathan WuTianlong Liu...
7119-7132页
查看更多>>摘要：The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data。 However， the rapid expansion of big data has resulted in an overwhelming amount of tabular data， necessitating the development of specialized tools for effective management and analysis。 This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis。 In addition， a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS。 The main contributions of this article are: 1) by retracing the network’s error from the output space to the latent space， the OBLS adjusts parameters in the feature and enhancement node layers。 This process aims to achieve more resilient representations， resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs， wherein each OBLS connects to the input and output layers， enabling direct data propagation。 This design helps reduce information loss between layers， ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications， including multiview feature embedding， one-class classification (OCC)， camera model identification， electroencephalogram (EEG) signal processing， and radar signal analysis。 Experimental results validate the effectiveness of the proposed models。 To ensure reproducibility， the source code is available at https://github。com/1027051515/OBLS_DOBLS。
查看更多>>摘要：This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints。 A critic MNN， which approximates the value function， and a second NN identifier are together used to generate the optimal control policies。 The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method， which can be extended to MNN with the N-hidden layers。 Moreover， an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate the problem of catastrophic forgetting in the multitasking systems。 Additionally， the proposed optimal framework addresses state constraints by utilizing a time-varying barrier function (TVBF)。 The uniform ultimate boundedness (UUB) of the overall closed-loop system is shown using the Lyapunov stability analysis。 A two-link robotic manipulator that compares to recent literature shows a 47% total cost reduction， demonstrating the effectiveness of the proposed method。
查看更多>>摘要：There are many applications where an autonomous agent can perform many sets of actions。 It must choose one set of actions based on some behavioral constraints on the agent。 Past work has used deontic logic to declaratively express such constraints in logic， and developed the concept of a feasible status set (FSS)， a set of actions that satisfy these constraints。 However， multiple FSSs may exist and an agent needs to choose one in order to act。 As there may be many different objective functions to evaluate status sets， we propose the novel concept of Pareto-optimal FSSs or POSS。 We show that checking if a status set is a POSS is co-NP-hard。 We develop an algorithm to find a POSS and in special cases when the objective functions are monotonic (or anti-monotonic)， we further develop more efficient algorithms。 Finally， we conduct experiments to show the efficacy of our approach and we discuss possible ways to handle multiple Pareto-optimal Status Sets。
查看更多>>摘要：This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks。 Since the leader and its high-order derivatives are unknown under jointly connected switching networks， a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them。 Meanwhile， the continuous information transmission among agents is avoided and the network channel utilization is optimized。 Subsequently， fuzzy logic systems (FLSs) are employed to approximate unknown dynamics， and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology。 It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB)， with the tracking errors converging to a small neighborhood around zero。 Finally， we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs)， and the simulation results verify the effectiveness of the proposed ET consensus control algorithm。
Maryam ZareParham M. KebriaAbbas KhosraviSaeid Nahavandi...
7173-7186页
查看更多>>摘要：In recent years， the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable。 As these systems continue to evolve， they are being utilized in increasingly complex and unstructured environments， such as autonomous driving， aerial robotics， and natural language processing。 As a consequence， programming their behaviors manually or defining their behavior through the reward functions [as done in reinforcement learning (RL)] has become exceedingly difficult。 This is because such environments require a high degree of flexibility and adaptability， making it challenging to specify an optimal set of rules or reward signals that can account for all the possible situations。 In such environments， learning from an expert’s behavior through imitation is often more appealing。 This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert’s behavior， which is provided through demonstrations。This article aims to provide an introduction to IL and an overview of its underlying assumptions and approaches。 It also offers a detailed description of recent advances and emerging areas of research in the field。 Additionally， this article discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research。 Overall， the goal of this article is to provide a comprehensive guide to the growing field of IL in robotics and AI。
查看更多>>摘要：Industrial multivariate time series (MTS) is a critical view of the industrial field for people to understand the state of machines。 However， due to data collection difficulty and privacy concerns， available data for building industrial intelligence and industrial large models is far from sufficient。 Therefore， industrial time series data generation is of great importance。 Existing research usually applies generative adversarial networks (GANs) to generate MTS。 However， GANs suffer from the unstable training process due to the joint training of the generator and discriminator。 This article proposes a temporal-augmented conditional adaptive diffusion model， termed Diff-MTS， for MTS generation。 It aims to better handle the complex temporal dependencies and dynamics of MTS data。 Specifically， a conditional adaptive maximum-mean discrepancy (Ada-MMD) method has been proposed for the controlled generation of MTS， which does not require a classifier to control the generation。 It improves the condition consistency of the diffusion model。 Moreover， a temporal decomposition reconstruction UNet (TDR-UNet) is established to capture complex temporal patterns and further improve the quality of the synthetic time series。 Comprehensive experiments on the C-MAPSS and FEMTO datasets demonstrate that the proposed Diff-MTS performs substantially better in terms of diversity， fidelity， and utility compared with the GAN-based methods。 These results show that Diff-MTS facilitates the generation of industrial data， contributing to intelligent maintenance and the construction of industrial large models。
NETL
NSTL
IEEE