查看更多>>摘要：In the paper, a class of unknown fractional-order nonlinear systems suffering from actuator faults are investigated. Meanwhile, an adaptive finite-time sliding mode control (SMC) approach based on approximation principle of fuzzy logic system (FLS) and backstepping layout is proposed. It is well known that the standard backstepping control has inherent computational complexity. Therefore, a type of fractional-order command filter (CF) is introduced to overcome such a shortcoming, that is, by means of fractional-order CF, the virtual input signal and its fractional derivative can be estimated properly as anticipated. Fractional-order sliding mode surfaces are constructed to diminish the filtering errors such that more better performance is guaranteed. Besides, compared to the conventional back stepping control, the CF-based fuzzy backstepping SMC approach presented in the paper not only shows the superior robustness, but also facilitates to accomplish the desired tracking control objective in finite time. The finite-time stability analysis is established on the basis of fractional-order Lyapunov method. Finally, the effectiveness of the proposed methodology is identified by numerical simulations.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this work, we propose a multi-agent learning framework to address the mobile sensor coverage problem in which the minimum mutual information between the agents (mobile sensors) and their environment defines the agent-strategy selection rule towards the system Nash equilibrium in a potential game setting. Initially, the agents infer the environment behavior by means of the Gaussian process regression (GPR) approach, using their own information and the information provided by their neighborhood. Then, the rate distortion function (RDF) is used to minimize the mutual information between each agent and its environment by means of the Blahut-Arimoto algorithm, from which, the resulting conditional probability and the parameter k have the highest relevance, since, the former describes the similitude between the agent information and its environment, and the latter, due to its influence in the distortion and gathered environment information, defines the rationality measure in the learning process. Finally, the expected distortion defined in the RDF, allows the formulation of a distortion based potential function and the consequent equilibrium convergence.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The hyperpower family of iterative methods with arbitrary convergence order is one of the most used methods for estimating matrix inverses and generalized inverses, whereas the zeroing neural network (ZNN) is a type of neural dynamics developed to solve time varying problems in science and engineering. Since the discretization of ZNN dynamics leads to the Newton iterative method for solving the matrix inversion and generalized inversion, this study proposes and investigates a family of ZNN dynamical models known as higher-order ZNN (HOZNN) models, which are defined on the basis of correlation with hyperpower iterations of arbitrary order. Because the HOZNN dynamical system requires error function powers, it is only applicable to square error functions. In this paper, we extend the original HOZNN dynamic flows to arbitrary time-dependent real matrices, both square and rectangular, and sign-bi-power activation is used to investigate the finite-time convergence of arbitrary order HOZNN dynamics. The proposed models are theoretically and numerically tested under three activation functions, and an application in solving the angle-of-arrival (AoA) localization problem demonstrates the effectiveness of the proposed design.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this work different repositioning rules are investigated and compared in the context of free-floating bike-sharing systems. A static complete reposition setting is adopted, i.e., the system is operated (re-balanced) when the number of users riding bicycles can be neglected as it happens in many cities, for instance during the night. The popular 'healthy or broken' repositioning rule is revisited and examined along with two newly proposed rules, i.e., 'pickup or delivery' and 'pickup and delivery' rules. A discussion is also provided in terms of measuring the degree of an unbalancing in such a system. A mathematical model is proposed for each repositioning rule. Depending on the rule one will be facing a multi-trip vehicle routing or a multi-trip pickup and delivery vehicle routing problem, which is a problem not much investigated in the literature. An approximate algorithm is also devised for the problem, which is adapted to the multi-trip vehicle routing, and also suitable for multi-trip pickup and delivery vehicle routing problem with some corresponding adjustment. Computational tests are reported on to assess the methodological contributions of this work. These tests consider both randomly generated instances and two instances using real data. The results shown that 'pickup and delivery' rule is better than others when distribution degree of repositioning scenario is less than 0.5, while if the distribution degree exceeds 0.5, the 'pickup or delivery' rule is the most cost-effective one.(c) 2022 Published by Elsevier Inc.
查看更多>>摘要：Clustering by fast search and find of density peaks (DPC) is a widely used and studied clustering algorithm. In this article, we notice that DPC can achieve highly accurate clustering results when restricted to local neighborhoods. Therefore, by investigating density information in local neighborhoods, we propose to capture latent structures in data with family trees, which can reflect density dominations among nearest neighbors of data. A data set will then be partitioned into multiple family trees. In order to obtain the final clustering result, instead of exploiting the error-prone allocation strategy of DPC, we first elaborately design a novel similarity measure for family trees, characterizing not only the distance between data points, but also the structure of trees. Then, we adapt graph cut for the corresponding connection graph to also take global structural information into account. Extensive experiments on both real-world and synthetic data sets show that the proposed algorithm can outperform several prominent clustering algorithms for most of the cases, including the DPC and spectral clustering algorithms and some of their latest variants. We also analyze the robustness of the proposed algorithm w.r.t. hyper-parameters and its time complexity, as well as the necessity of its components through ablation study. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Graphs are effective for representing various relationships in the real world and have been successfully applied in many areas, such as publication citations and movie networks. Compared to homogeneous graphs (i.e., nodes and edges of a single relation type), heterogeneous graphs have heterogeneity and richer information (i.e., nodes and edges of different relation types). How to tackle complex non-pairwise graph-structured data and model various relation-types is a daunting challenge for heterogenous graphs. However, the existing unsupervised methods focus on node attribute learning, while node neighborhood information utilizes very limited because they only consider node propagation that is within few steps. In this paper, we propose an unsupervised method, called APPTE, that models adequate node neighborhood information in local context, and captures the global neighborhood information. Meanwhile, our method considers the robustness and generalization ability. Specifically, we construct approximate personalized propagation in local context to utilize an infinite number of neighborhood aggregation layers for extending node neighborhood propagation range, and then fuse these local context to capture global neighborhood information. Additionally, we improve the robustness and generalization ability of model, employing throwedge to increase the randomness and diversity of the graph connections by randomly deleting a part of edges. The experimental results on three benchmark datasets containing heterogeneous graphs demonstrate that our proposed method is superior to the available state-of-the-art methods.(c) 2022 Published by Elsevier Inc.
查看更多>>摘要：Modern industrial processes are confronted with a large-scale challenge in recent years. In this paper, a novel distributed kernel principal component regression (DKPCR) approach is proposed to study the plant-wide problem of quality-related process monitoring. Along with reducing the scale of abundant measurements, the proposed approach also focuses on the robust issues that arise from the large outliers. In the local phase, every agent of the DKPCR technique processes the data of the partial sections and sends the selected information to the centralized mainframe. The engineer gathers the data from the local agents and then makes a decision based on the Bayesian inference. Additionally, the corresponding weight diagnosis approach is devised to isolate the fault-relevant variables under the smear effect by virtue of the detection results and information. In the end, the Tennessee Eastman case (TEC) and the three-phase flow system (TPFS) are leveraged to demonstrate the detection and diagnosis performance of the proposed approach. (c) 2022 Elsevier Inc. All rights reserved.
Yuan, XinLiebelt, Michael JohnShi, PengPhillips, Braden J....
19页
查看更多>>摘要：We develop an agent-based artificial general intelligent system that can be implemented in compact and power-efficient electronic hardware. The hardware under development is called the Street Engine, which is a hardware-based cognitive architecture for implementing agent-based artificial intelligence. In this paper, we introduce an agent-based system to replicate simple cognitive behaviours. In the processes of this system, numerical data are converted into fuzzy symbolic representations of the surrounding environment, and reasoning rules are included in a modified Fuzzy Inference System to support the cognitive decision-making. We use a case study example, the homing behaviour of the honey bee, to demonstrate constructing production rules and implementing the cognitive and reasoning capabilities of agents. The low level cognitive behaviour is converted into a rule-based fuzzy system, and hardware-based experiments have been conducted to verify the effectiveness of the proposed technique. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Multi-objective optimization problems often load in the multi-scenario environment, and they can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far, existing research on MSMOs mainly focuses on the specific method in engineering applications, and general methods are quite scarce. This paper develops an integrated information-based evolutionary framework to find a set of scenario-based non dominated solutions for MSMOs. Sequentially, a scenario-based dominance principle evolutionary algorithm and a decomposition-based evolutionary algorithm are combined with the framework to construct two integration algorithms, respectively. Furthermore, the crucial components in both algorithms are developed under the multi-scenario environment, such as scenario-based constraint handling, scenario-based dominance concept, scenario based crowded degree and multi-scenario decomposition method. Additionally, we design a novel comprehensive evaluation indicator for MSMOs. Finally, the proposed framework is tested on numerous artificial and engineering problems. Experiments demonstrate that the framework has superiority over four existing state-of-the-art peer competitors concerning the indicator. Two integration algorithms are able to find a set of good scenario-based non dominated solutions.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, robust constraint-following control (RCFC) with the optimal design is developed to handle the trajectory tracking control issues for permanent magnet linear motor (PMLM), which control performance is deteriorated mainly by friction, ripple force, and external disturbance. Specifically, a fuzzy description for the main nonlinearity of the PMLM system and its fuzzy dynamic model is formulated. Then, the tracking specification is modeled as a performance constraint, the RCFC algorithm following the Udwadia-Kalaba theory is designed to comply with this constraint, and possess strong robustness to uncertainties simultaneously. The resulting controller is demonstrated to be uniform bounded and uniform ultimate bounded with Lyapunov analysis. Furthermore, the optimal design issue via a fuzzy approach is investigated to achieve the optimal tradeoff between control effort and system performance. Finally, the rapid control prototype platform CSPACE is employed to implement the real-time control while avoiding time-consuming repetitive programming and debugging. Simulation and experimental results illustrate the actual effectiveness of the proposed algorithm. (c) 2022 Elsevier Inc. All rights reserved.
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