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International Conference on Information Science and Technology (ICIST)

作者： Dongsheng Guo Yaqiong Ding Xiaodong Li Senbo Fu Yunong Zhang School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Shunde China

In this paper, a special method called Zhang neuronet (ZN) is proposed and investigated for online solution of complex-valued time-varying linear inequalities (CVTVLI). Instead of employing a norm-based energy function in traditional gradient neuronet (GN) and related methods, the given ZN model is designed using a vector-valued error function and takes advantage of the first-order time-derivative information of time-varying coefficients involved in CVTVLI. Through adjusting the value of design parameter γ appropriately, superior convergence performance is achieved for the proposed ZN model for dealing with such a time-varying problem. Then, theoretical and simulative results are given to illustrate and substantiate the convergence property of the proposed ZN model. Besides, a GN model is developed and exploited to for the same CVTVLI solving. The comparison on transient behaviors of these two models further shows the efficacy and superiority of the proposed ZN model.

关键词： Process control Neural networks

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Use of WASD neuronet in projecting the population of Oceania based on 1000-year historical data

Use of WASD neuronet in projecting the population of Oceania...

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Chinese Automation Congress (CAC)

作者： Yunong Zhang Yaqiong Ding Jingyao Ren Xiaodong Li Hongzhou Tan Ministry of Education Key Laboratory of Autonomous Systems and Networked Control Guangzhou China SYSU-CMU Shunde International Joint Research Institute Shunde China School of Information Science and Technology Sun Yat-sen University Guangzhou Guangdong China

ISBN: (纸本)9781467371902

With growing economic and political influence in the world, the important role played by Oceania in population issues should not be neglected. So it is very important and urgent to find an effective way to make a proper population projections. Nonetheless, the traditional methods focused on fertility and mortality may lead to the lack of all-sidedness in projection results. We realize that the historical data contain the internal mechanism of the population development, and the neuronet performs well with nonlinear data and the multifactor system. Therefore, in this report, we construct a 3-layer feed-forward neuronet equipped with a weights-and-structure-determination (WASD) algorithm to learn the historical data and project the population. With the neuronet well trained by over 1000-year historical data, we successfully project that the future Oceania population will keep a steady increasing trend in the coming fifteen years.

关键词： Sociology Statistics Neurons Training Testing Market research Prediction algorithms

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Near future prediction of European population through Chebyshev-activation WASD neuronet

Near future prediction of European population through Chebys...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yunong Zhang Jinjin Wang Qingkai Zeng Heng Qiu Hongzhou Tan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (纸本)9781479917181

With the world population increasing rapidly, the conflicts between the population and limited resources have become more and more severe. Population growth is a root cause of many environmental and social problems. Therefore, it is of vital importance to make population predictions. However, predictions based on standard cohort-component method fails to consider all relevant impact factors and may neglect some important uncertainty factors. To overcome the inherent limitations, in this article, we present a Chebyshev-activation WASD neuronet approach for the population prediction. This neuronet method is applied to predicting European population, with numerous numerical experiments conducted as a research basis to guarantee the feasibility and validity of our approach. It is predicted with the most possibility that European population will decrease in the near future.

关键词： Sociology Statistics Europe Market research Neurons Chebyshev approximation Prediction algorithms

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WASD neuronet prediction for China's population

WASD neuronet prediction for China's population

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International Conference on Information and Automation (ICIA)

作者： Yunong Zhang Jianxi Liu Xiaogang Yan Binbin Qiu Tianjian Qiao Ministry of Education Key Laboratory of Autonomous Systems and Networked Control Guangzhou China SYSU-CMU Shunde International Joint Research Institute Shunde China School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (纸本)9781467391054

As one of the most important factors influencing the development potential of the country, China's population attracts considerable attention. Some official organizations regularly publish the predictions of China's population every calendar year. However, most of the predictions use the standard cohort-component method, which does not allow for all relevant impact factors and may lose sight of some important uncertainty factors. To overcome the aforementioned limitations, in this paper, we present a 3-layer feedforward neuronet (i.e., neural network) approach equipped with a weights-and-structuredetermination (WASD) algorithm for the population prediction of China. Numerical experiments further substantiate the feasibility of such an approach and indicate that there are three kinds of possibilities for the progress of China's population in the next decade. With the highest possibility, China's population will keep continual increase with a gentle growth rate.

关键词： Sociology Statistics Market research Predictive models Neurons Testing Computational modeling

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New DTZNN model for future minimization with cube steady-state error pattern using Taylor finite-difference formula

New DTZNN model for future minimization with cube steady-sta...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yunong Zhang Ying Fang Bolin Liao Tianjian Qiao Hongzhou Tan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China

In this paper, a discrete-time Zhang neural network (DTZNN) model, discretized from continuous-time Zhang neural network, is proposed and investigated for performing the online future minimization (OFM). In order to approximate more accurately the 1st-order derivative in computation and discretize more effectively the continuous-time Zhang neural network, a new Taylor-type numerical differentiation formula, together with the optimal sampling-gap rule, is presented and utilized to obtain the Taylor-type DTZNN model. For comparison, Euler-type DTZNN model and Newton iteration, with an interesting link being found, are also presented. Moreover, theoretical results of stability and convergence are presented, which show that the steady-state residual errors of the presented Taylor-type DTZNN model, Euler-type DTZNN model and Newton iteration have a pattern of 0(t3), 0(t2) and 0(t), respectively, with t denoting the sampling gap. Numerical experimental results further substantiate the effectiveness and advantages of the Taylor-type DTZNN model for solving the OFM problem.

关键词： Numerical models Minimization Computational modeling Mathematical model Neural networks Convergence Steady-state

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Fast, finite, accurate and optimal WASD neuronet versus slow, infinite, inaccurate and rough BP neuronet illustrated via russia population prediction

Fast, finite, accurate and optimal WASD neuronet versus slow...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Jianxi Liu Yunong Zhang Zhengli Xiao Tianjian Qiao Hongzhou Tan Ministry of Education Key Laboratory of Autonoumous Systems and Networked Control Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China School of Information Science and Technology Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (纸本)9781479917181

Russia population problem attracts great concerns to the future trend of population and the development of the nation. Conventional researches on Russia population prediction are usually based on the standard cohort-component method. Such a method only allows for several factors (fertility, mortality and migration rates), and then leads to the lack of all-sidedness in the prediction results. With outstanding generalization ability, the feedforward neuronet is considered to be a more appropriate substitute. Besides, the back-propagation (BP) is of the most widely-used feedforward neuronet. As the conventional back-propagation neuronet has some inherent weaknesses, in this paper, two types of improved feedforward neuronet are constructed for the Russia population prediction. More specifically, a type of 3-layer power-activated neuronet (PAN) equipped with the BP algorithm (BP-PAN) and a type of 3-layer PAN equipped with the weights-and-structure-determination (WASD) algorithm (WASD-PAN) are built on the basis of 2013-year (from 1AD to 2013AD) historical population data for the Russia population prediction. By a lot of numerical experiments, the future declining trend of Russia population in the next decade is predicted with the highest possibility. In addition, via the Russia population prediction, the comparisons on the performance between the WASD neuronet and BP neuronet are conducted and summarized.

关键词： Sociology Statistics Neurons Prediction algorithms Feedforward neural networks Data models Training

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Mechatronic Design and Implementation of a Novel Gliding Robotic Dolphin

Mechatronic Design and Implementation of a Novel Gliding Rob...

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IEEE International Conference on robotics and Biomimetics

作者： Zhengxing Wu Junzhi Yu Jun Yuan Min Tan Jianwei Zhang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department Informatics University of Hamburg Hamburg 22527 Germany

ISBN: (纸本)9781467396769

This paper provides an innovative design for a gliding robotic dolphin. In order to realize high maneuverability and long endurance, the robotic dolphin combines the advantages of both robotic dolphins and underwater gliders. It can not only realize fast and flexible dolphin-like swimming depending on the powerful propulsive posterior body and fluke, but also implement gently and durable gliding motion due to the buoyancy-driven system. More importantly, the controllable pectoral fins and horizontal fluke can effectively complete the attitude adjustment, so traditional internal movable masses could be removed for saving a considerable space. Besides, the hydrodynamic analysis in the steady gliding motion is executed and hydrodynamic coefficients including lift, drag, and pitching moment are also obtained through Computational Fluid Dynamics (CFD) method. Finally, extensive experiments including dolphin-like swimming, spiraling motion and gently gliding motion illustrate the great locomotion ability of the developed gliding robotic dolphin.

关键词： maneuverability endurance CFD

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Northern American population data recovery from 1500AD to 1950AD as well as prediction using WASD neuronet with 513-year data

Northern American population data recovery from 1500AD to 19...

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Chinese Automation Congress (CAC)

作者： Yunong Zhang Wan Li Binbin Qiu Junqiao Qiu Hongzhou Tan Ministry of Education Key Laboratory of Autonomous Systems and Networked Control Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China School of Information Science and Technology Sun Yat-sen University Guangzhou Guangdong China

ISBN: (纸本)9781467371902

The recovery and prediction of Northern American population data, which are closely related to the future development of Northern America and even the whole world, have become significant subjects and captured great attention among sociologists as well as scientists. However, most of the relevant researches are just based on fertility, mortality or other individual quantifiable factors by traditional statistical models and thus lack all-sidedness in their results. As we know, the historical population data are the comprehensive reflection of the population development under the influence of all factors. In this paper, based on the past 513-year population data, a feedforward neuronet equipped with the weights-and-structure-determination (WASD) algorithm is thus constructed for the recovery and prediction of Northern American population data. Besides, the neuronet is activated by a group of Chebyshev polynomials (CP) of the first kind, which is named the CP neuronet in this paper. Moreover, the optimal normalization factor is found and utilized to improve the neuronet's performance. Due to the marvelous learning and generalization abilities of the presented CP neuronet, we recover the missing population data from 1500AD to 1950AD as well as draw up the Northern American population prediction for the next few decades.

关键词： Sociology Statistics Neurons Training Prediction algorithms Chebyshev approximation Feedforward neural networks

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Polynomial Trajectory Tracking of networked Euler-Lagrange systems 33

Polynomial Trajectory Tracking of Networked Euler-Lagrange S...

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第三十三届中国控制会议

作者： WANG Yunpeng CHENG Long HOU Zeng-Guang TAN Min BIAN Guibin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479947249

The coordinated tracking problem of networked Euler-Lagrange systems is studied in this *** to the knowledge of classical control,an integral term can eliminate the steady-state error when solving the tracking problem.A proportional-integral-derivative(PID)tracking protocol is then rst proposed to solve the tracking problem of Euler-Lagrange systems where the leader has a quadratic *** properly choosing parameters,it is proved that all followers can asymptotically track the leader’s quadratic trajectory if the communication topology graph has a spanning ***,a so-called PImD tracking protocol is derived by adding some high-order integral *** is shown that this PImD tracking protocol can solve the tracking problem of networked Euler-Lagrange systems with a leader having the higher-order polynomial ***,two simulation examples are presented to demonstrate the effectiveness of the proposed protocol.

关键词： Coordinated tracking control,Euler-Lagrange system,Multi-agent system,Integral term,Polynomial trajectory

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GROUP CONSENSUS FOR MULTIPLE networked EULER-LAGRANGE systems WITH PARAMETRIC UNCERTAINTIES

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Journal of systems Science & Complexity 2014年第4期27卷 632-649页

作者： HU Hongxiang ZHANG Zhe YU Li YU Wenwu XIE Guangming Department of Mathematics Hangzhou Dianzi University Zhejiang Provincial United Key Laboratory of Embedded Systems Department of AutomationZhejiang University of Technology Department of Mathematics Southeast University School of Electrical and Computer Engineering RMIT University Faculty of Engineering King Abdulaziz University Center for Systems and Control State Key Laboratory of Turbulence and Complex SystemsCollege of EngineeringPeking University

In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain *** the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus *** is shown that the group consensus is reachable even when the system contains the uncertain ***,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is *** results are finally provided to validate the effectiveness of the theoretical analysis.

关键词： Euler-Lagrange system group consensus multi-agent systems switching topologies.

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