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Adaptive over-sampling method for classification with application to imbalanced datasets in aluminum electrolysis

NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 7183-7199页

作者： Huang, Zhaoke Yang, Chunhua Chen, Xiaofang Huang, Keke Xie, Yongfang Cent South Univ Sch Automat Changsha 410083 Peoples R China

The class imbalance problem often appears in practical applications, where one class has numerous instances and the other has only a few instances. Synthetic Minority Over-sampling TEchnique (SMOTE) is the most popular and commonly used sampling method to solve this problem. It has two important parameters: over-sampling rate N and number of nearest neighbors k. However, the two parameters that are arbitrarily chosen by users are not optimal in practical applications. In addition, the imbalance ratios of these datasets are absolutely different, which makes parameter selection in SMOTE more difficult. To overcome the problem, an adaptive over-sampling method is proposed in this study based on SMOTE. It transforms the parameter selection problem in SMOTE to a multi-objective optimization problem. Then, a new selection strategy named absolute dominance-based selection is proposed to obtain the current optimal solution. Finally, the state transition algorithm is used to search the best parameter values of SMOTE to achieve the optimal objectives. Four imbalanced benchmark datasets and four class-imbalanced aluminum electrolysis datasets are used to verify the validity of the proposed method. In comparison with other methods, the proposed method has the advantage of good classification performance. Numerical results also show that the proposed method can successfully solve the class imbalance problem in aluminum electrolysis.

关键词： Class imbalance problem Multi-objective optimization state transition algorithm SMOTE Aluminum electrolysis

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Defect detection of nickel plated punched steel strip based on improved least square method

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OPTIK 2020年 206卷 164331-000页

作者： Cao, Binfang Li, Jianqi Liu, Chengfa Qin, Lingjie Hunan Univ Arts & Sci Hunan Prov Cooperat Innovat Ctr Construct & Dev D Changde Peoples R China Hunan Univ Arts & Sci Coll Math & Phys Sci Changde Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha Peoples R China

In order to solve the problems of false detection and missed detection on punched steel strip brought by manual inspection, a machine vision detection system for nickel plated punched steel strip is built, from which a defect detection method of nickel-plated punched steel strip based on improved least square method is presented. At first, the image is extracted and preprocessed in order to obtain clear edge images. Then state transition algorithm and iterative algorithm are used to improve least square method. The iterative algorithm is used to obtain the data set of the center coordinates and the radius of the fitted circle. While the state transition algorithm is used to perform the optimization of the results after multiple iterations to obtain the optimal solution of the center and radius parameters. Finally, the parameters such as hole diameter, transverse hole distance and longitudinal hole distance are calculated and used to realize the defect detection. The experimental results show that the method proposed can realize the parameters calculation with an absolute error of less than 10um. It also can realize defect detection of the blind hole and connecting hole for nickel plated punched steel strip.

关键词： Nickel plated punched steel strip Defect detection Circle fitting Least square method (LSM) state transition algorithm

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Power scheduling optimization under single-valued neutrosophic uncertainty

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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

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state transition algorithm for traveling salesman problem

State transition algorithm for traveling salesman problem

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31st Chinese Control Conference

作者： Yang Chunhua Tang Xiaolin Zhou Xiaojun Gui Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Ballarat Sch Sci Informat Technol & Engn Ballarat Vic 3353 Australia

ISBN: (纸本)9789881563811

Discrete version of state transition algorithm is proposed in order to solve the traveling salesman problem. Three special operators for discrete optimization problem named swap, shift and symmetry transformations are presented. Convergence analysis and time complexity of the algorithm are also considered. To make the algorithm simple and efficient, no parameter adjusting is suggested in current version. Experiments are carried out to test the performance of the strategy, and comparisons with simulated annealing and ant colony optimization have demonstrated the effectiveness of the proposed algorithm. The results also show that the discrete state transition algorithm consumes much less time and has better search ability than its counterparts, which indicates that state transition algorithm is with strong adaptability.

关键词： state transition algorithm Traveling Salesman Problem Swap Shift Symmetry

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Hierarchical parameter optimization based support vector regression for power load forecasting

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SUSTAINABLE CITIES AND SOCIETY 2021年 71卷 102937-102937页

作者： Wang, Zeyu Zhou, Xiaojun Tian, Jituo Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar Peng Cheng Lab Shenzhen 518000 Peoples R China

Power load forecasting is an important task of smart grid, which is of great significance to the sustainable development of society. In this paper, a hybrid support vector regression (HSVR) is raised for the medium and long term load forecasting. To further improve prediction accuracy, the coupling and interdependent relationship between hyperparameters and model parameters in the optimization process is focused. A hierarchical optimization method based on nested strategy and state transition algorithm (STA) is proposed to find optimal parameters. The effectiveness of the proposed hierarchical optimization method is confirmed on several benchmarks, and the resulting hierarchical optimization method based SVR is also successfully applied to a real industrial power load forecasting problem in China.

关键词： Power load forecasting Hybrid support vector regression Hierarchical parameter optimization method state transition algorithm

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Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification

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APPLIED SOFT COMPUTING 2021年 106卷 107314-107314页

作者： Huang, Zhaoke Yang, Chunhua Chen, Xiaofang Zhou, Xiaojun Chen, Guo Huang, Tingwen Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar State Key Lab Synthet Automat Shenyang 110819 Peoples R China

The multivariate time series (MTS) classification is one of the major tasks of time series data mining. Many methods have been proposed to investigate the MTS classification. Among them, the method based on feature representation is the most popular and widely used one. However, there exist some shortcomings for this method, such as unsatisfactory accuracy, being sensitive to noise and not able to fully make use of time series data attributes. In order to overcome these disadvantages, we propose a new method called functional deep echo state network (FDESN) for MTS classification that utilizes two special operators: temporal aggregation and spatial aggregation. In general, the parameters of the FDESN are determined by random selection, human experience or trial and error. This may increase the complexity of the FDESN or reduce the accuracy of the FDESN. In this study, a novel bi-level optimization approach is proposed to optimize the parameters of the FDESN. The parameter selection problem in the FDESN is transformed into the bi-level optimization problem. The state transition algorithm (STA) is used to solve the bi-level optimization problem. Finally, the experimental results show that the proposed method is superior to other methods. In addition, the proposed method is successfully applied to anode condition identification in aluminum electrolysis. For the aluminum electrolysis datasets, the proposed method improved the average classification accuracy by about 3.5% compared with the other methods. For a specific aluminum electrolysis dataset ACS2504, the classification accuracy significantly increased from 77.92% to 82.69% by using the proposed method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multivariate time series classification Functional deep echo state network Bi-level optimization state transition algorithm Aluminum electrolysis

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

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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

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state transition algorithm for traveling salesman problem

State transition algorithm for traveling salesman problem

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第三十一届中国控制会议

作者： YANG Chunhua~1,TANG Xiaolin~1,ZHOU Xiaojun~(1,2),GUI Weihua~1 1.School of Information Science and Engineering,Central South University,Changsha 410083,P.R.China 2.School of Science,Information Technology and Engineering,University of Ballarat,Victoria 3353,Australia

Discrete version of state transition algorithm is proposed in order to solve the traveling salesman *** special operators for discrete optimization problem named swap,shift and symmetry transformations are *** analysis and time complexity of the algorithm are also *** make the algorithm simple and efficient,no parameter adjusting is suggested in current *** are carried out to test the performance of the strategy,and comparisons with simulated annealing and ant colony optimization have demonstrated the effectiveness of the proposed *** results also show that the discrete state transition algorithm consumes much less time and has better search ability than its counterparts, which indicates that state transition algorithm is with strong adaptability.

关键词： state transition algorithm Traveling Salesman Problem Swap Shift Symmetry

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A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

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Engineering 2019年第6期5卷 1060-1076页

作者： Weichao Yue Weihua Gui Xiaofang Chen Zhaohui Zeng Yongfang Xie School of Information Science and Engineering Central South UniversityChangsha 410083China

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

关键词： AlF3 addition Fuzzy cognitive maps Learning algorithms state transition algorithm Fuzzy decision trees

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Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy

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HYDROMETALLURGY 2018年 178卷 60-76页

作者： Zhang, Fengxue Yang, Chunhua Zhou, Xiaojun Zhu, Hongqiu Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The copper removal process is the first stage of purification in zinc hydrometallurgy. Due to its dynamic characteristics and complex reaction mechanism, a robust and effective controller to maintain high quality and stability of the outlet-ion-concentration is in great need. In this paper, a fractional order fuzzy proportional integral derivative (FOFPID) controller based on fuzzy logic is proposed to meet this challenge. The proposed work is conducted through a combination of three novel interdependent efforts. First, controller design problem is transformed into a nonconvex optimization problem. Second, a novel method named state transition algorithm (STA) is employed to solve the aforementioned optimization problem. Furthermore, in order to evaluate the performance of the proposed control strategy, the response performance of the system is analyzed. Finally, further tests are carried out to evaluate the performance of FOFPID controller, where disturbances caused by the measurement, flow rate, and inlet-ion-concentration are all taken into account. The simulation results demonstrate the superiority of the FOFPID controller in copper removal process over the competing FOPID and manual control in the same application environment.

关键词： Copper removal process Disturbance rejection Fractional order calculus Fuzzy logic control PID control state transition algorithm

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