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Zhang neuronet solving complex-valued time-varying linear inequalities

Zhang neuronet solving complex-valued time-varying linear in...

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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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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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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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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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Design and implementation of an adaptive cruise control system based on supervised actor-critic learning

Design and implementation of an adaptive cruise control syst...

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International Conference on information Science and Technology (ICIST)

作者： Bin Wang Dongbin Zhao Chengdong Li Yujie Dai The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Information and Electrical Engineering Shandong Jianzhu University Jinan China Transportation and Economics Institute China Academy of Railway Sciences Beijing China

A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinforcement learning approach is presented to obtain the desired acceleration controller. In the lower level, throttle and brake controllers calculate the required throttle and/or brake signals based on the desired longitudinal acceleration. Feed-forward neural networks are used to implement the actor and critic components of the SAC learning algorithm. An online learning mechanism is introduced to implement the SAC training process. dSPACE simulator is used to verify the effectiveness of the ACC system. Typical emergency braking scenario is simulated to test the adaptability of the ACC system. Road condition change (e.g. wintry or wet conditions) simulation is first investigated to evaluate the robustness of the ACC system. Performance of the proposed ACC system is proved to be very practical.

关键词： Vehicles Torque Acceleration Roads Engines Training Sensors

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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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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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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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A multi-step robust model predictive control scheme for polytopic uncertain multi-input systems 19

A multi-step robust model predictive control scheme for poly...

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Zhang, Langwen Wang, Jingcheng Wang, Bohui Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9783902823625

Model predictive control (MPC) has attracted wide attention in process industries with its ability to handle constrained multivariable processes. Computational complexity can become a limiting factor when MPC is applied to large-scale systems with fast sampling times. In this paper, a control scheme known as multi-step robust MPC is presented for polytopic uncertain multi-input systems. Only one or several state feedback laws are optimized at each time interval to reduce computational complexity. A set invariance condition for polytopic uncertain systems is identified and the invariant set is determined by solving a linear matrix inequality (LMI) optimization problem. Based on the set invariance condition, a min-max multi-step robust MPC scheme is proposed. Numerical simulations show the effectiveness of the proposed scheme. © IFAC.

关键词： Model predictive control

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Opinion dynamics of modified Hegselmann-Krause model with group-based bounded confidence 19

Opinion dynamics of modified Hegselmann-Krause model with gr...

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Fu, Guiyuan Zhang, Weidong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9783902823625

The continuous opinion dynamics with group-based heterogeneous bounded confidences is considered in this paper. Firstly, a slightly modified Hegselmann-Krause model is proposed, and the agents are divided into open-minded-, moderate-minded-, and close-minded-subgroups according to the corresponding confidence intervals. Then numerical simulations are carried out to analyze the influence of the close-minded and open-minded agents, as well as the population size, on the opinion dynamics. It is observed that (1) for the fixed population size, the larger proportion of close-minded agents, the more opinion clusters;(2) open-minded agents cannot contribute to forging different opinions, instead, the existence of them maybe diversify final opinions;also interestingly the relative size of the largest cluster varies along concave-parabola-like curve as the proportion of open-minded agents increases;(3) for the same proportion of the three subgroups, as population size increases, the number of final opinion clusters will increase at the beginning and then reach a stable level, which is quite different from the previous studies. © IFAC.

关键词： Population statistics

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