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IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

作者： Lijun Wang Qing Li Chaonan Tong Yixin Yin Jiangyun Li Siyang Song School of Automation & Electrical Engineering Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing(USTB) Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781612844923

To satisfy high accuracy and speed requirement, a kind of Active Disturbance Rejection control (ADRC) solution is proposed according to the complexity of Monitor Finishing Automatic Width control (FAWC) system with large time-delay for hot strip mill. In order to improve the versatility of ADRC parameters, a unit gain was added in the loop. Based on the simplified model, the presented ADRC consists of linear functions which make it easier to realize. For applying ADRC algorithms conveniently and widely, the control system is implemented in Scilab/Xcos, which is one of an Open-source Software for Scientific Computation (OSSC) and is free of charge. According to the program of data exchanging between Xocs and Scilab, Xcos simulation can be carried out in batch processing in Scilab. The disturbances rejection and robustness of the proposed control method is verified by ADRC simulation toolbox in Scilab/Xcos.

关键词： Strips Monitoring Educational institutions Uncertainty Steel control systems Temperature measurement

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New method about how to construct decision tree based on association rule

New method about how to construct decision tree based on ass...

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IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

作者： Jing Gao Baoyong Zhao Information College Capital University of Economics and Business Beijing China School of Automation & Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education China

ISBN: (纸本)9781612844923

Nowadays, decision tree is widely used as one of the most powerful tools in data mining. However, to construct an optimization decision tree is a complete NP problem. So a new method about how to construct decision tree, which is based on association rule mining, is proposed in this paper. Firstly, approximate exact rule with high reliability is defined. Secondly new attributes are generated from the approximate exact rule. And then its evaluation method is discussed in detail. Thirdly, the decision tree is constructed with both the new generated attributes and its original data. Finally, after comprehensive analysis, experimental results show that this new method has higher accuracy than any other old method.

关键词： Association rules Decision trees Approximation algorithms Classification algorithms Itemsets Accuracy

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Training ANFIS system with DE algorithm

Training ANFIS system with DE algorithm

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International Workshop on advanced Computational Intelligence (IWACI)

作者： Allahyar Z. Zangeneh Mohammad Mansouri Mohammad Teshnehlab Ali K. Sedigh Computer Engineering Department Science and Research Branch Islamic Azad university of Tehran Iran Intelligent System laboratory (ISLAB) Control Department K. N. Toosi University of Technology Tehran Iran Advanced Process Automation & Control laboratory (APAC) Control Department K. N. Toosi University of Technology Tehran Iran

In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.

关键词： Vectors Adaptive systems Input variables Training Mathematical model Equations Firing

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Optimization research of genetic neural network based on Scilab

Optimization research of genetic neural network based on Sci...

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IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

作者： Baoyong Zhao Yingjian Qi Xing Tao School of Automation & Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education China Science School Communication University of China Beijing China Beijing Research Center of Urban System Engineering Beijing China

ISBN: (纸本)9781612844923

Radial basis function (RBF) network is one of the significant neural networks. It has been used successfully in various fields. But in RBF network approximation algorithm, the initial value of the network weights, Gauss function center vector and broad-based vector is not easy to determine, and when these parameter choice is undeserved, RBF network approximation precision will decline and even the serious consequences of network spread will be produced. By using genetic algorithm in this paper, which can better realize RBF network parameter optimization, thereby increasing the accuracy of approximation. Scilab is open source software and has good simulation capabilities. Experiments using Scilab shows that the optimization method of genetic neural network is feasible and results are satisfied.

关键词： Genetic algorithms Radial basis function networks Optimization Approximation methods Vectors Biological cells Genetics

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A novel type of trigonometric neural network trained by Extended Kalman Filter

A novel type of trigonometric neural network trained by Exte...

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International Workshop on advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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Iterative learning control of nonlinear system with state and control time delay

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2011年第SUPPL. 1期35卷 191-195页

作者： Ding, Bao Tao, Hong-Feng Yang, Hui-Zhong Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 China Shanghai Key Laboratory of Power Station Automation Technology Shanghai 200072 China

For nonlinear systems with state and control time delays in actual industrial processes, which have the initial deviation and disturbances of output errors, a PID-type iterative learning control algorithm for repetitive nonlinear time-varying systems with any desired outputs and bounded disturbances is investigated. By using the memories of the desired trajectories, control and tracking error expectations in the processes of iterative learning, the learning controller is designed with the variable batches of forgetting factors. Based on the λ norm theory and the Bellman-Gronwall inequality, the necessary and sufficient conditions for the existence of the learning gain are discussed, and the convergence of the control algorithm is analyzed to ensure that the batch error of the closed-loop tracking system is bounded. The robustness and dynamic performances of the system are improved. The simulation of the motion control of the single-joint robot arm illustrates the effectiveness of the proposed algorithm.

关键词： Time delay

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Analysis and pattern recognition of blast furnace burden surface based on multi-radar data

Analysis and pattern recognition of blast furnace burden sur...

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2010 International Conference on Intelligent control and Information processing, ICICIP 2010

作者： Zhou, Xiang Li, Xiaoli Liu, Dexin Yin, Yixin Chen, Xianzhong Hou, Qingwen Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing Beijing 10083 China

ISBN: (纸本)9781424470488

Iron making is the first stage and also an important part in steel making process, which will bring the problem of energy efficiency and economic benefits. In this process, the distribution of the blast burden impacts greatly on the production of BF (blast furnace). Therefore, the prediction of furnace burden distribution in furnace throat will play an important role in the control strategy of furnace burden. Based on the data from the multi-radar, the blast burden curve can be formed. Feature extraction and classification based on different curves (i.e. different pattern) can be made by using BP neural networks. The work proposed in this paper will be a guidence for the future research of burden surface based on the data of phased-array radar. © 2010 IEEE.

关键词： Blast furnaces

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Application of fuzzy pattern recognition in burden surface identification

Application of fuzzy pattern recognition in burden surface i...

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2010 International Conference on Intelligent control and Information processing, ICICIP 2010

作者： Liu, Dexin Li, Xiaoli Zhou, Xiang Yin, Yixin Chen, Xianzhong Hou, Qingwen Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing Beijing 10083 China

ISBN: (纸本)9781424470488

A kind of pattern recognition method with Fuzzy clustering is applied to identify the burden surface of blast furnace. According to the information of burden surface distribution, the target clustering analysis is used to set up a standard model database. Based on the character of distribution, each target burden surface is matched into different standard model database by using fuzzy pattern recognition method. Maximal minimal similarity method is adapted in the course of fuzzy pattern recognition. The simulation results show the effectiveness of the proposed method. © 2010 IEEE.

关键词： Blast furnaces

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Improving transient response of adaptive control systems using multiple neural network models

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7th International Symposium on Neural Networks, ISNN 2010

作者： Li, Xiao-Li Yu, Xiang Zhang, Yan Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing Beijing 10083 China Graduate School of Education University of Pennsylvania 3451 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9783642129896

Multiple Radial Basis Function (RBF) neural network models are used to approximate a kind of discrete time nonlinear system described by a combination of nonlinear and linear part. Based on these models, Multiple model adaptive control strategy is given to improve the transient response of closed-loop system. Simulations according to different uncertain changing parameters in the model of the system are studied to show the efficiency of the proposed method. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Transient analysis

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Multiresoloution wavelet analysis based ischemic beat detection using fuzzy reasoning

Multiresoloution wavelet analysis based ischemic beat detect...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shiva Khoshnoud Mohammad Teshnehlab Mahdi Aliyari Shoorehdeli Electrical and Computer Engineering Department Khaje Nasir Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department Advanced Process Automation and Control (APAC) Khaje Nasir Toosi University of Technology Tehran Iran

In this study a new fuzzy expert system for ischemic beat detection based on interpretability is proposed. First, efficacious morphologic features which are useful in ischemia detection are extracted by wavelet analysis. Then, subtractive clustering is performed for clustering of normal patterns. Finally after creation of fuzzy system, rule reduction and optimization is employed in respect with accuracy and interpretability in order to achieve more optimized system. The fuzzy expert system is evaluated on the European Society of Cardiology (ESC) ST-T database. The results proof the medical rules that are common in detection of ischemia by physicians. Also some other rules those are not common for physicians gain medical interpretation ability is proposed.

关键词： Electrocardiography Libraries Cardiology

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