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arXiv 2019年

作者： Wu, Dongrui Yuan, Ye Huang, Jian Tan, Yihua Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., mini-batch gradient descent (MBGD), regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and Drop-Membership) specifically for training TSK fuzzy systems. Our final algorithm, MBGD with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size;however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

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A Mixed Gaussian Membership Function Fuzzy CMAC for a Three-Link Robot

A Mixed Gaussian Membership Function Fuzzy CMAC for a Three-...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Tuan-Tu Huynh Chih-Min Lin Tien-Loc Le Zhixiong Zhong Department of Electrical Engineering Yuan Ze University Taoyuan Taiwan R.O.C. Department of Electrical Electronic and Mechanical Engineering Lac Hong University Bien Hoa Vietnam Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This research produces a mixed Gaussian membership function (GMF) fuzzy cerebellar model articulation controller (CMAC) for a three-link robot. A mixed GMF is created using the current and the previous GMFs on each layer of CMAC to detect errors efficiently, so a mixed GMF fuzzy CMAC (MGMFFC) is able to train parameters efficiently and constructs the MGMFFC structure automatically. A Lyapunov cost function and the gradient descent techniques are utilized to get the adaptive with guaranteed system's stable. Simulation studies for a three-link robot show that the MGMFFC attains favorable tracking performance.

关键词： Two dimensional displays Robots Hypercubes Robustness Fuzzy logic Nonlinear systems Electronic mail

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image Dehazing Network Based on Dilated Convolution Feature Extraction 12

Image Dehazing Network Based on Dilated Convolution Feature ...

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12th International Congress on image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Feng, Ting Li, Zuoyong Wang, Chuansheng Chen, Xinwei Wu, Jiawei College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781728148526

Hazy weather brings a lot of inconvenience to peoples lives, such as transportation. Therefore, image dehazing is still an important focus point. To achieve image dehazing, we proposed a Deep Dilated Residual Haze Network (DDRHNet) based on self-encoder and self-decoder. The proposed mode is an end-to-end supervised method, which directly estimates image dehazing result instead of estimating the atmospheric light and the transmission in the unsupervised and classical atmospheric scattering model. The DDRHNet method obtains image dehazed result by finding the difference between the input hazy image and the output of haze-free image. Experimental results on an open image dehazing dataset called SOTS demonstrate the superiority of the proposed DDRHNet network. © 2019 IEEE.

关键词： Demulsification

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FDT-MAC: A new multi-channel MAC protocol based on fuzzy decision tree for wireless sensor network 5th

FDT-MAC: A new multi-channel MAC protocol based on fuzzy dec...

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5th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2018

作者： Yang, Hui Ci, Linlin Zhang, Fuquan Yang, Minghua Mao, Yu Niu, Ke School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Rocket Military Equipment Research Institute Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783030037659

The current popular multi-channel Medium Access control (MAC) layer protocol adopts a strategy of separating control and data channels, in order to improve channel utilization and packet forwarding success rate. However, the randomness of the data access and the high mobility of the nodes will lead to switch channels too frequently, which will still increase the packet transmission latency. Therefore, this paper proposes a fuzzy decision tree for MAC (FDT-MAC) protocol, which constructs the fuzzy decision tree of the nodes sub-tree. FDT-MAC utilizes fuzzy reasoning method to construct the membership function of the node collision factor (NCF) and node relative direction (NRD) factor. So that the node can quickly select the appropriate channel according to the real-time status (Output value) of each channel, reduce the collision probability of the packet, and improve the delivery probability (90%) and throughput of the channel (750 bps). © Springer Nature Switzerland AG 2019.

关键词： Membership functions

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Predefined-time terminal sliding mode control of robot manipulators

arXiv

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arXiv 2020年

作者： Liang, Chang-Duo Ge, Ming-Feng Liu, Zhi-Wei Wang, Yan-Wu Karimi, Hamid Reza School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy

In this paper, we present a new terminal sliding mode control to achieve predefined-time stability of robot manipulators. The proposed control is developed based on a novel predefined-time terminal sliding mode (PTSM) surface, on which the states are forced to reach the origin in a predefined time, i.e., the settling time is independent to the initial condition and can be explicitly user-defined via adjusting some specific parameters called the predefined-time parameters. It is also demonstrated that the proposed control can provide satisfactory steady-state performance in the case of both external disturbances and parametric uncertainties. Besides, we present a formal systemic analysis method to derive the sufficient conditions for guaranteeing the predefined-time convergence of the closed-loop system. Finally, the effectiveness and performance of the presented control scheme are illustrated through both theoretical comparisons and numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sliding mode control

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Level attraction and PT symmetry in indirectly coupled microresonators

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Physical Review A 2020年第4期102卷 043527-043527页

作者： Zhao-Hui Peng Chun-Xia Jia Yu-Qing Zhang Ji-Bing Yuan Le-Man Kuang Institute of Modern Physics and Department of Physics College of Physics and Electronic Science Hunan University of Science and Technology Xiangtan 411201 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China College of Physics and Electronic Engineering and Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421008 China

Benefiting from the tunability of their phase delay and gain, we show the possibility of realizing level attraction, parity-time (PT)-, and anti-PT-symmetric photonic structures with indirectly coupled active and passive microresonators mediated by drop-filter waveguides. We find that level attraction originates from two mechanisms, i.e., exceptional point and resonance, and it may emerge not only for dissipative coupling but also coherent coupling and hybrid coupling after considering the gain and loss of microresonators, which confirms the universality of level attraction in the coupled photonic system. On the other hand, the transition from PT to anti-PT symmetry or vice versa may be realizable by tuning their phase delay and gain; thus the indirectly coupled microresonators may provide an excellent platform for exhibiting the unique features of PT and anti-PT symmetry. Furthermore, we also reveal the intrinsic relationship of the line shape and phase behavior of the reflection spectrum with PT symmetry, anti-PT symmetry, and level attraction.

关键词： Nanophotonics Optics & lasers Quantum optics Optical microcavities Waveguides Quantum theory

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Optimize TSK fuzzy systems for big data classification problems: Bag of tricks

arXiv

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arXiv 2019年

作者： Cui, Yuqi Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily applicable to big data problems, especially when the size and the dimensionality of the data are both large. This paper proposes a mini-batch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy systems for big data classification problems. It integrates three novel techniques: 1) uniform regularization (UR), which is a regularization term added to the loss function to make sure the rules have similar average firing levels, and hence better generalization performance;2) random percentile initialization (RPI), which initializes the membership function parameters efficiently and reliably;and, 3) batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy systems to speedup the convergence and improve generalization. Experiments on nine datasets from various application domains, with varying size and feature dimensionality, demonstrated that each of UR, RPI and BN has its own unique advantages, and integrating all three together can achieve the best classification performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

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Tongue body localization based on image clustering and gray projection 2nd

Tongue body localization based on image clustering and gray ...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Liu, Weixia Straits Institute Minjiang University Fuzhou China Internet Innovation Research Center The Fujian College’s Research Base of Humanities and Social Science Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783030045845

Tongue diagnosis is often used in Traditional Chinese Medicine (TCM). Tongue image segmentation, which extracts tongue body, is a key step when manufacturing an automated system of tongue diagnosis. Localization of tongue body depicted by a rectangle is a useful preprocessing step in tongue image segmentation, which can eliminate the adverse effect of strong edges from neighboring tissues such as face and lip when extracting tongue body contour. After exploring the existing tongue body localization method based on gray projection, we propose an upgraded method combining image clustering with gray projection. Specifically, our proposed method first conducts the clustering on the image hue component in HSI (i.e., hue, saturation, and intensity) space to determine three thresholds. Then, image thresholding and morphological operations are sequentially performed to generate a binary image, and its largest object region is taken as the initial localization result. Finally, the localization result is refined by performing gray projection on the image red component. Experiments on a variety of tongue images showed that our proposed method significantly improves the accuracy of tongue body localization in comparison with the existing gray projection method. © Springer Nature Switzerland AG 2019.

关键词： Binary images

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A Model Reference Adaptive controller With Feedforward Decoupling for Active Magnetic Bearing Rotor System

A Model Reference Adaptive Controller With Feedforward Decou...

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IEEE Advanced information Technology, Electronic and Automation control Conference (IAEAC)

作者： Jian Yi Gui-Ping Ren Hai-Tao Zhang Yue Wu School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan P.R. China

Active magnetic bearing (AMB) rotor system is widely applied in the industry for its remarkable advantages. However, it is a typical open-loop unstable mechatronics system suffering from nonlinear and couple characteristic. In this paper, the feedforward decoupling method is used to deal with the couple relationship of different axes. And the model reference adaptive control (MRAC) method is applied to improve the control performance and robustness and overcome the uncertainties. This control method makes the system output reach to the reference model by the designed adaptive law. Simulation results illustrate the effectiveness of the proposed scheme.

关键词： Rotors Couplings Adaptation models Magnetic levitation Feedforward systems Adaptive systems Uncertainty

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Canonical correlation analysis (CCA) based multi-view classification: An overview

arXiv

引用

arXiv 2019年

作者： Guo, Chenfeng Wu, Dongrui The Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets. Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation. Traditional CCA can only be used to calculate the linear correlation of two views. Besides, it is unsupervised and the label information is wasted. Many nonlinear, supervised, or generalized extensions have been proposed to overcome these limitations. However, to our knowledge, there is no overview for these approaches. This paper provides an overview of many representative CCA-based MVL approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Correlation methods

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