检索结果-内蒙古大学图书馆

Optimal Setting and Control Strategy for Industrial Process Based on Discrete-Time Fractional-Order PI^λD^μ

IEEE ACCESS 2019年 7卷 47747-47761页

作者： Zhang, Fengxue Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent South Univ Sch Automat Changsha 41183 Hunan Peoples R China

Generally, the main task of the optimal operation in the industrial process is to satisfy the process technical requirements, and the PID controller is a well-known controller in industrial control applications. Nevertheless, due to the complicated mechanisms and the dynamic characteristics of a complex industrial process, the conventional PID controller fails to provide effective control to such systems. In this paper, we develop a novel discrete-time fractional order PID (DFOPID) control strategy to achieve the technical requirements of the complex industrial process. The proposed work is conducted through a combination of three novel interdependent efforts. First, on the basis of the widely used Tustin operator and its Taylor series, a digital structure of the DFOPID is proposed. Second, in order to solve the stability problem of the complex industrial process, an optimal setting of the approximation function's order (N) and five parameters (lambda, mu, K-p, K-i, K-d) is necessary. Hence, an integral time absolute error (ITAE) criterion is applied to convert the optimal setting problem to a nonconvex optimization problem. Finally, a novel intelligent optimization search algorithm called state transition algorithm is employed to carry out the aforementioned design procedure. Furthermore, the performance of the DFOPID control strategy in some practical industrial control systems, including the copper removal process and the electrochemical process of zinc are also investigated.

关键词： Discrete-time system (PID mu)-D-lambda control state transition algorithm discretization method complex industrial process

来源：评论

学校读者我要写书评

暂无评论

Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy

引用

HYDROMETALLURGY 2017年 173卷 134-148页

作者： Han, Jie Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The additions of oxygen and zinc oxide for the goethite process determine the cost and efficiency of the iron precipitation process. As the two production targets (cost and efficiency) are conflicting and the chemical reaction is a continuous process that changes over time, the amounts of additive need to be dynamically optimized to satisfy the requirement of industrial application. In this paper, a discretization method based on control variables and control intervals is proposed to transform the dynamic optimization problem to a nonlinear mathematical programming problem. Then, a multi-objective optimization approach based on the state transition algorithm and constrained nondominated sorting is proposed to find the Pareto optimal solutions. Finally, an evaluation mechanism is proposed to obtain the best solution for industrial applications. The results from a series of simulation experiments show the effectiveness of the proposed approach, e.g. the daily average additions of oxygen and zinc oxide are decreased by 778.0854 m(3) and 4.9013 t, respectively.

关键词： Dynamic multi-objective optimization Discretization method state transition algorithm Iron precipitation

来源：评论

学校读者我要写书评

暂无评论

Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm

引用

NEUROCOMPUTING 2021年 462卷 169-184页

作者： Dong, Yingchao Zhang, Hongli Wang, Cong Zhou, Xiaojun Xinjiang Univ Sch Elect Engn Urumqi 830047 Xinjiang Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Wind power forecasting has high application value in power systems. However, due to the intermittence and fluctuation of wind power, it is difficult to predict wind power effectively using a single forecasting model. Therefore, to improve the accuracy and stability of wind power forecasting, an ensemble learning model based on stacking framework is proposed in this paper. First, several decomposition techniques are used to pre-process the original wind power data and an optimal decomposition method is selected through experiments. Then, a quadratic interpolation based on state transition algorithm is proposed to optimize the parameters of the Bernstein polynomial model and the weights of the Hermite neural net-work (HNN) to obtain two base learners. Finally, the Spearman correlation coefficient is used to analyze the correlation of several base learners. The base learners with low correlation and strong prediction abil-ity are selected as the first-layer forecasting model of the stacking model, and the HNN is used as the second-layer prediction model to obtain the stacking ensemble model. To verify the effectiveness of the proposed model, a large number of comprehensive experiments are carried out with wind power data from a wind farm in Xinjiang, China. Experimental results show that the proposed model has higher pre-diction accuracy and stability than other single forecasting models. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Wind power forecasting Decomposition Bernstein polynomial Hermite polynomial state transition algorithm Stacking ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Brain-inspired STA for parameter estimation of fractional-order memristor-based chaotic systems

引用

APPLIED INTELLIGENCE 2023年第15期53卷 18653-18665页

作者： Huang, Zhaoke Yang, Chunhua Zhou, Xiaojun Gui, Weihua Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China Peng Cheng Lab Shenzhen 518000 Guangdong Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

It is very important to estimate the unknown parameters of the fractional-order memristor-based chaotic systems (FOMCSs). In this study, a brain-inspired state transition algorithm (BISTA) is proposed to estimate the parameters of the FOMCSs. In order to generate a better initial population, a novel initialization approach based on opposition-based learning is presented. To balance the global search and local search, and accelerate the convergence speed, the mutual learning and selective learning are proposed in the optimization process. The performance of the proposed algorithm is comprehensively evaluated on two typical FOMCSs. The simulation results and statistical analysis have demonstrated the effectiveness of the proposed algorithm. For the fractional-order memristor-based Lorenz system, the proposed method can increase the estimated value of parameters by at least one order of magnitude compared with the other methods.

关键词： Parameter estimation Fractional-order chaotic systems Memristor state transition algorithm

来源：评论

学校读者我要写书评

暂无评论

A method for classifying citrus surface defects based on machine vision

引用

JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021年第3期15卷 2877-2888页

作者： Zhang, Wenzhuo Tan, Aijiao Zhou, Guoxiong Chen, Aibin Li, Mingxuan Chen, Xiao He, Mingfang Hu, Yahui Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha Peoples R China Cent South Univ Forestry & Technol Hunan Prov Key Lab Urban Forest Ecol Changsha Peoples R China Hunan Acad Agr Sci Inst Plant Protect Changsha Hunan Peoples R China

When detecting citrus surface defects, the performance of machine vision system is affected by different aspects such as the size, shape and environment. Therefore, a method for classifying citrus surface defects based on machine vision was proposed in this paper. First, the Fuzzy C-Means algorithm optimized by the Gray Wolf Optimizer algorithm was used to preprocess the citrus image. The citrus in the image was separated from the background;Then, the improved convolutional neural network combined with the state Transfer algorithm (STA) was used to identify the citrus surface defects. We selected 2000 Tribute Citrus, 1000 ones with and without the defects separately, to carry on the experiment. The identification accuracy of the trained model on the dataset was 99.1%. In order to verify the effectiveness of the model in complex background, the convolutional neural network in combination with a STA was compared with SVM, AlexNet, VGG16 and other methods. The experimental results show that the citrus surface defect classification method based on machine vision is effective.

关键词： Citrus surface defects Convolutional neural network Machine vision FCM algorithm GWO algorithm state transition algorithm

来源：评论

学校读者我要写书评

暂无评论

Power scheduling optimization under single-valued neutrosophic uncertainty

引用

NEUROCOMPUTING 2020年第0期382卷 12-20页

作者： Han, Jie Yang, Chunhua Lim, Cheng-Chew Zhou, Xiaojun Shi, Peng Gui, Weihua Cent S Univ Sch Automat Changsha 410083 Peoples R China Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

Optimization problems with improper expression of constraints exist widely in practical engineering. In order to achieve a reasonable degree of constraints satisfaction, this paper investigates a single-valued neutrosophic optimization method to deal with system uncertainty. Firstly, an equivalent model based on single-valued neutrosophic entropy is proposed to transform the original problem into a crisp multi-objective optimization problem. The Pareto-front of the optimization problem is then obtained by a multi-objective state transition algorithm. Finally, the best solution is determined by a multi-criteria decision making method. A practical example of a zinc electrowinning process is used to illustrate the effectiveness and advantage of the developed new optimization approach, which provides a more cost-effective solution to decrease the electricity utility charge and satisfy the daily output production requirements. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Power scheduling optimization Fuzzy uncertainty Single-valued neutrosophic set state transition algorithm Decision making method

来源：评论

学校读者我要写书评

暂无评论

Set-Point Tracking and Multi-Objective Optimization-Based PID Control for the Goethite Process

引用

IEEE ACCESS 2018年 6卷 36683-36698页

作者： Zhou, Xiaojun Zhou, Jiajia Yang, Chunhua Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The goethite process is complicated since its chemical reactions interact with each other, making its control and optimization in industrial production difficult. The goal of the goethite process is to make the outlet ion concentration satisfy the technical requirements with minimal process consumption. To simplify the difficulties of optimization in the goethite process, an optimization method based on a set-point tracking strategy is proposed. The set-point tracking strategy is used to transform the complex state constraints into an additional objective. Therefore, the single optimization control problem for the goethite process is transformed into a bi-objective optimization control problem. Furthermore, PID controllers are adopted to control the addition amounts of zinc oxide and oxygen in the goethite process. The optimal parameters of the PID controllers are obtained via a multi-objective state transition algorithm (MOSTA). The performance of MOSTA is verified by several benchmark test functions with performance matrices. The control performance reveals that the proposed method is an effective way to control the process and can not only reduce the zinc oxide and oxygen addition amounts compared with manual operation and traditional PID control but also reject disturbances. The proposed method can satisfy the industrial requirements with less energy consumption.

关键词： Goethite process multi-objective optimization PID control set-point tracking state transition algorithm

来源：评论

学校读者我要写书评

暂无评论

A new wind power prediction method based on chaotic theory and Bernstein Neural Network

引用

ENERGY 2016年第Part1期117卷 259-271页

作者： Wang, Cong Zhang, Hongli Fan, Wenhui Fan, Xiaochao Xinjiang Univ Sch Elect Engn Urumqi 830047 Xinjiang Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The accuracy of wind power prediction is important for assessing the security and economy of the system operation when wind power connects to the grids. However, multiple factors cause a long delay and large errors in wind power prediction. Hence, efficient wind power forecasting approaches are still required for practical applications. In this paper, a new wind power forecasting method based on Chaos Theory and Bernstein Neural Network (BNN) is proposed. Firstly, the largest Lyapunov exponent as a judgment for wind power system's chaotic behavior is made. Secondly, Phase Space Reconstruction (PSR) is used to reconstruct the wind power series' phase space. Thirdly, the prediction model is constructed using the Bernstein polynomial and neural network. Finally, the weights and thresholds of the model are optimized by Primal Dual state transition algorithm (PDSTA). The practical hourly data of wind power generation in Xinjiang is used to test this forecaster. The proposed forecaster is compared with several current prominent research findings. Analytical results indicate that the forecasting error of PDSTA BNN is 3.893% for 24 look-ahead hours, and has lower errors obtained compared with the other forecast methods discussed in this paper. The results of all cases studying confirm the validity of the new forecast method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wind power prediction Chaotic theory Bernstein neural network Primal dual state transition algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Control for Zinc Electrowinning Process With Current Switching

引用

IEEE ACCESS 2017年 5卷 24688-24697页

作者： Yang, Chunhua Deng, Shijun Li, Yonggang Zhu, Hongqiu Li, Fanbiao Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany

This paper is concerned with the optimal control problem for the zinc electrowinning (EW) process during the current switching period. A mathematical model is developed to reveal the dynamic characteristics of the whole plant of the zinc EW process and an energy consumption model is established to set the expected set points of the concentrations of the zinc ion and the sulfuric acid under different current. Furthermore, an optimal control problem is constructed in the light of free initial time, free terminal time, and fixed system switching time during the zinc EW process. A novel time-scaling transformation based control parametrization method is introduced to transform the optimal control problem into a multiple parameters optimization selection problem, which can be effectively solved by the optimization algorithm. The applications on the EW process of a zinc hydrometallurgy plant demonstrate the validity of proposed method.

关键词： Zinc electrowinning optimal control state transition algorithm time-scaling transformation

来源：评论

学校读者我要写书评

暂无评论

An adaptive time series representation method for anode current signals in aluminium electrolysis

An adaptive time series representation method for anode curr...

引用

5th IFAC Workshop on Mining, Mineral and Metal Processing (MMM)

作者： Huang, Zhaoke Yang, Chunhua Chen, Xiaofang Zeng, Zhaohui Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

In aluminum electrolysis, anode current signals can not only provide an insight into the localized anodic dynamic behavior, but also can be used as a new way to study the process in the harsh industrial environment. This kind of the data is stored in the form of time series, which is a sequence of real numeric values. Because the data is huge and growing fast, the number of elements in anode current signals must be reduced to make further analysis much easier and faster, which is a typical time series representation problem. In this paper, an adaptive time series representation method for anode current signals is proposed for this purpose. The essence of this method is that the time series representation problem is transformed into the optimization problem. In addition, a new cognitively inspired optimization method named state transition algorithm (STA) is introduced to solve the optimization problem. The experimental results indicate that the proposed method outperforms common methods used for time series representation in aluminum electrolysis. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Anode current signals Time series representation Optimization state transition algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：