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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Diao, Yiya Li, Changhe Ma, Yebin Wang, Junchen Zhou, Xingang School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China Communication Security Group of Eastern Theater Command Nanjing China

ISBN: (纸本)9783319687582

Ant colony optimization (ACO) has been shown effectiveness for solving combinatorial optimization problems. Inspired by the basic principles of ACO, this paper proposes a novel evolutionary algorithm, called probabilistic learning (PL). In the algorithm, a probability matrix is created based on weighted information of the population and a novel random search operator is proposed to adapt the PL to dynamic environments. The algorithm is tested on the shortest path problem (SPP) in both static and dynamic environments. Experimental results on a set of carefully designed 3D-problems show that the PL algorithm is effective for solve SPPs and outperforms several popular ACO variants. © Springer International Publishing AG 2017.

关键词： Ant colony optimization

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Development of A Customizable Real-time Monitoring System for Networked control systems

Development of A Customizable Real-time Monitoring System fo...

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Chinese control Conference

作者： Haoran Tan Zhiwu Huang Min Wu School of Information Science and Engineering Central South University Changsha P. R. China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan P. R. China

The design and implementation of a monitoring system for networked control systems (NCSs) are introduced in this paper. The monitoring system is developed in the .NET framework using C# according to the C/S architecture which allows the user to monitor network nodes without geographical limitations. The widgets used to display come from the National Instruments (NI) suit and are integrated in the system by reconstructing it. The user is able to design a monitoring interface freely by creating and customizing widgets dynamically. During the monitoring, the display value of the widget is synchronized with parameters and signals of NCSs through receiving data asynchronously from network notes. In particular, all elements of the matrix widely used in NCSs are supported for monitoring. The functionality and feasibility of the designed monitoring system are demonstrated via a networked control experiment. In addition, the saved data can be used in data driven control and model identification.

关键词： Monitoring Graphical user interfaces control systems Communication channels Mice Protocols Real-time systems

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Stability Analysis of Discrete T-S Fuzzy systems with Time-Varying Delay Using an Extended Reciprocally Convex Matrix Inequality

Stability Analysis of Discrete T-S Fuzzy Systems with Time-V...

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Chinese control Conference (CCC)

作者： Bi-Ying Chen Fei Long Chuan-Ke Zhang Yong He Hui-Qin Xiao Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China College of Electrical and Information Engineering Hunan University of Technology Zhuzhou P. R. China

This paper concerns the stability analysis of discrete Takagi-Sugeno fuzzy systems with time-varying delays. A suitable Lyapunov-Krasovskii functional is constructed, and an extended reciprocally convex matrix inequality is used to estimate the forward difference of the functional. As a result, a less conservative stability criterion is obtained. Finally, a numerical example is given to demonstrate the advantage of the proposed method.

关键词： Delays Linear matrix inequalities Fuzzy systems Time-varying systems Stability criteria Symmetric matrices

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Qualitative Elemental Analysis of Minerals Based on Energy Dispersive X - ray Fluorescence Spectrometry

Qualitative Elemental Analysis of Minerals Based on Energy D...

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Chinese control Conference (CCC)

作者： JunLei Song ZhiHeng Yang Jing Cai XiaoYong Ni DianHong Wang Xing Jin WenQin Mo KaiFeng Dong Fang Jin Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Mechanical and Electronic Information china University of Geosciences Wuhan P. R. China

The research of miniaturized XRF/XRD analyzers are carried out in this paper, especially the qualitative analysis based on EDXRF, a miniaturized energy dispersive X-ray fluorescence spectrometry platform is designed and a qualitative elemental analysis software based on Android operating system is developed. The elemental qualitative analysis software can preprocess the X-ray feature spectrum by background eliminationsmoothing and energy coefficient calibration, after these pretreatments, the main elements of the sample can be analyzed. The experimental results show that the qualitative elemental analysis platform proposed in this paper can measure the elements of Al to Au in the periodic table, besides, the detection accuracy of multiple samples is higher than 97%. The mineral qualitative elemental analysis software proposed in this paper can be applied to any portable analysis platform based on Android operating system, moreover, it will spur advances in the development of the miniaturization and intelligent X-ray fluorescence spectrometer.

关键词： Minerals Software Calibration Spectroscopy Instruments Detectors Smoothing methods

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Robust Disturbance-Rejection for a Class of Nonlinear systems

Robust Disturbance-Rejection for a Class of Nonlinear System...

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第37届中国控制会议

作者： Pan Yu Kang-Zhi Liu Min Wu Jinhua She Department of Electrical and Electronic Engineering Chiba University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Information Science and Engineering Central South University School of Automation China University of Geosciences School of Engineering Tokyo University of Technology

This paper presents a robust disturbance-rejection method for a system with a time-variant nonlinearity and a disturbance. The nonlinear dynamics which satisfies Lipschitz condition is unknown. The disturbance is also unknown but *** advantage of stable inversion, an improved EID(IEID) is utilized to deal with the total effect of the nonlinearity and disturbance. To reduce the conservativeness of system design, a feedback control gain is obtained in prior to the control gains of the observer and the IEID estimator. Then a Lyapunov functional considering the information of the nonlinearity is constructed and based on that, a linear-matrix-inequality(LMI)-based stability condition is derived to acquire the gains of the observer and the IEID estimator. Two tuning parameters are introduced in the LMI to enable the adjustment of disturbance-rejection performance. Finally, simulations for a flight control system show that the developed method has good robustness. Comparisons with disturbance-observer-based control(DOBC), composite DOBC and H control, and conventional EID-based control methods demonstrate the effectiveness of the developed method and its advantages over others.

关键词： Disturbance-rejection improved equivalent-input-disturbance（EID） linear matrix inequality（LMI） nonlinear system robustness

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ADP-Based Model Reference Adaptive control Design for Unknown Discrete-Time Nonlinear systems

ADP-Based Model Reference Adaptive Control Design for Unknow...

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Chinese control Conference (CCC)

作者： Wei Wang Xin Chen Fang Wang Hao Fu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Electrical and Information Engineering Zhengzhou University of Light Industry Wuhan P. R. China

In engineering applications, it is very useful to make a nonlinear system behave like a linear stable system. However, how to design a model reference adaptive control (MRAC) as the nonlinear dynamics is unknown is the key problem. In this study, an MRAC with adaptive dynamic programming (ADP) algorithm is presented for discrete-time nonlinear unknown dynamic systems, in which a multi-layer neural network (NN) model is utilized to describe the nonlinear system's dynamics. Then based on this approximate model, a feedforward neuro-controller is developed as the desired control corresponding to the reference input. Third, the iterative ADP algorithm, in the form of actor-critic framework, employs two NNs to estimate the action value function, and generates the feedback control which, together with the feedforward neuro-controller, makes the state of the system track the reference model. Finally the feasibility of the new approach is verified by two numerical experiments.

关键词： Artificial neural networks Nonlinear systems Feedforward systems Optimal control Adaptation models Feedback control Mathematical model

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High-accuracy carrier frequency acquisition algorithm of GNSS signal based on numerical approximation method 5

High-accuracy carrier frequency acquisition algorithm of GNS...

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5th International Workshop on advanced Computational Intelligence and intelligent Informatics, IWACIII 2017

作者： Wang, Guangjun Deng, Xuening Wu, Min School of Automation China University of Geosciences Wuhan430074 China China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

The bandwidth of the Doppler frequency shift under high dynamic environment that the GNSS receivers need to search is 5-10 times than the conventional receivers, a large frequency step is generally chosen to improve the satellite signal acquisition speed. Aiming at the problem that acquisition carrier frequency deviation caused by the large step search cannot meet the requirement of the tracking loop design, a fine frequency estimation method based on numerical approximation is proposed in this paper. The proposed method applied the preferred frequency slot and approximated the capture correlation peak combined with the curve fitting method, which can reduce the frequency deviation, and improve the accuracy of acquisition, then simplify the complexity of tracking loop design. Simulation results show that it has higher accuracy than the other two algorithm at different SNR (Signal to Noise Ratio).

关键词： Frequency estimation

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Privacy Preservation for Distributed Nonsmooth Constrained Optimization Based on Pseudo-Subgradient

Privacy Preservation for Distributed Nonsmooth Constrained O...

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第37届中国控制会议

作者： Xianlin Zeng Shu Liang Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Advanced Innovation Center for Intelligent Robots and Systems (Beijing Institute of Technology) Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education

In this paper, we investigate a privacy preservation design in the distributed nonsmooth convex optimization with set constraints. To solve the distributed optimization problem while preserving the privacy, we use pseudo-subgradients involved with（non-integrable） set-valued functions. Based on pseudo-subgradients, we propose distributed nonsmooth optimization algorithms with keeping subgradient information confidential. Then we prove the correctness and convergence of the distributed privacy preservation optimization algorithms to achieve the exact solution of the original optimization problem.

关键词： Distributed nonsmooth convex optimization privacy preservation set constraints pseudo-subgradients

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A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder

A Novel Deep Learning Approach: Stacked Evolutionary Auto-en...

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International Joint Conference on Neural Networks

作者： Yaoming Cai Zhihua Cai Meng Zeng Xiaobo Liu Jia Wu Guangjun Wang School of Computer Science China University of Geosciences Wuhan China Qinzhou University Guangxi China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Computing Macquarie University Sydney NSW Australia

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective Optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification problems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.

关键词： Training Optimization Decoding Neural networks Linear programming Feature extraction Stacking

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An Improved-WM Method Based on Optimization of Centers of Output Fuzzy Subsets for Fuzzy Rules

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IFAC-PapersOnLine 2017年第1期50卷 7369-7374页

作者： Jin Y. Cao W. Wu M. She J. Yuan Y. School of Automation China University of Geosciences Wuhan 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

The generation of fuzzy rules from samples is significant for fuzzy modelling. To improve the robustness of Wang-Mendel (WM) method, an improved WM method to extract fuzzy rules from all the regularized sample data was proposed. However, the accuracy of the model with this method is degraded for the conflicting rules with small difference between support degrees. And the output subsets can only be chosen from the pre-defined ones. To solve these problems, we develop an improved-WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (COiWM). This method adopts the fuzzy c-means (FCM) clustering algorithm to divide the input and output spaces, and the improved WM method which replaces the original data by regularized data is used to calculate the support degrees. Then the support degrees are used as weights to optimize the centers of output fuzzy subsets with a method of weighted averages, so as to enhance the accuracy of a fuzzy model. Experimental results of a case study on short term daily maximum electric load forecasting prove that our proposed method enhances the accuracy of a fuzzy model. © 2017

关键词： Accuracy Conflicting rules Fuzzy c-means clustering algorithm Fuzzy rules Wang-Mendel method

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