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Improved differential evolution algorithm used for load distribution of hot strip rolling

Improved differential evolution algorithm used for load dist...

引用

International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Feng, Yao Weidong, Yang School of Information Engineering 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 100083 China

ISBN: (纸本)9781424464388

To solve the problem that standard differential evolution algorithm is easy to premature convergence, here gives a new variant form-self-disturbance variation, and takes some improvement to it. New mutation could maintain the diversity of population, and the improvement could increase convergence speed. The improved method achieves performance improvement, and does not increase the time complexity of the original algorithm. Then, it is applied to the calculation of hot rolling load distribution. © 2010 IEEE.

关键词： Hot rolling

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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...

引用

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...

引用

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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3D virtual face modeling technology and application

3D virtual face modeling technology and application

引用

IEEE International Conference on Broadband Network & Multimedia Technology (IC-BNMT)

作者： Fenhua Wang Ningjuan Shi Xuegang Gao Zhiliang Wang Iron and Steel Flow Advanced Control Ministry of Education Key Laboratory University of Science and Technology Beijing China School of Automation Nanjing University of Science and Technology of PLA Nanjing China

3D face modeling in computer graphics and virtual reality technology is a hot of research. In this paper, we get the original three-dimensional face data through optical scanners. Due to a variety constraints of conditions (light, face of the rough parts), there are many empty parts of the face. In this paper, we use Hermite cubic spline interpolation algorithm to repair three-dimensional facial model hollow parts, and use the shortest path algorithm to draw three-dimensional face of the grid. Established by the three-dimensional face model can be applied to the digital home, the emotional human facial expressions, medical plastic surgery and other fields.

关键词： Face

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Research on BP Neural Network Optimal Method Based on Improved Ant Colony Algorithm

Research on BP Neural Network Optimal Method Based on Improv...

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Li Wang Dongqing Wang Ning Ding Key Laboratory for Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781424460793

A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for Back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This algorithm has many attractive features, such as adaptive learning, self-organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But, BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments suggest the method proposed has resolved those problems efficiently.

关键词： Neural networks Artificial neural networks Neurons Backpropagation algorithms Convergence Distributed computing Laboratories iron steel control engineering education

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A New Model Based on Improved ACA and BP to Predict Silicon Content in Hot Metal

A New Model Based on Improved ACA and BP to Predict Silicon ...

引用

International Conference on Computer Modeling and Simulation, ICCMS

作者： Li Wang Dong-qing Wang Jia-jun Zhu Xiang Zhao Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education School of Information Engineering University of Science and Technology Beijing China

A new model based on improved ant colony algorithm (ACA) and backpropagation (BP) is proposed to predict Silicon content of hot metal in blast furnace. BP algorithm has been widely used in training artificial neural network (ANN), which is an outstanding model to predict Silicon content. BP algorithm has many attractive features, such as adaptive learning, self- organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, the improved ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments show the model proposed has good performance in predicting Silicon content of hot metal.

关键词： Predictive models Silicon Backpropagation algorithms Artificial neural networks Convergence Blast furnaces Organisms Fault tolerance Traveling salesman problems Distributed computing

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Vector control of induction motor based on online identification and ant colony optimization

Vector control of induction motor based on online identifica...

引用

International Conference on Industrial and Information Systems, IIS

作者： Chun-shan Lai Kai-xiang Peng Gui-shui Cao Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing P.R.C

During operation, due to changes of temperature, magnetic field, frequency and other factors, electrical and mechanical parameters of induction motor will change. It will affect controlled plant model accordingly bring badly control accuracy directly. In order to obtain better dynamic performance and steady state accuracy, this paper proposes a combination method of online identification and online optimization of the controller parameters. It is designed that a vector control system of induction motor based on online identification and ant colony optimization. The simulation results have proved the effectiveness of this method. It can not only meet the real time induction motor vector control requirements, and also can greatly improve the induction motor dynamic performance and steady state characteristics, showing strong self-adaptability and robustness.

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A new model based on improved PSO and BP to predict silicon content in hot metal

A new model based on improved PSO and BP to predict silicon ...

引用

International Conference on Computer and Automation Engineering, ICCAE

作者： Li Wang Dong-qing Wang Ning Ding Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) School of Information Engineering University of Science and Technology Beijing Beijing China

In the smelting process of blast furnace, maintaining the temperature at an acceptable level is the key to ensure the smelting at a good level. The hot metal Silicon content is not only the indication of the blast furnace thermal state and its changes, but also the significant indicator for assessing the blast furnace's stability and the quality of iron. Therefore, as the core content of automatic control of blast furnace, it is crucial to create a model to predict Silicon content of hot metal. Using the online data of a steel company's blast furnace, a new model based on improved Particle Swarm Optimization (PSO) and Back-propagation (BP) is proposed to predict Silicon content of hot metal in blast furnace. As a new bionic algorithm, the improved PSO has gained very good performance in some classical optimization problems. Its properties such as fast searching, global searching have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Compared with pure BP algorithm and basic PSO, experimental results show the model proposed has good performance in predicting Silicon content of hot metal.

关键词： Predictive models Silicon Blast furnaces Smelting Temperature Thermal stability iron Automatic control steel Particle swarm optimization

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

引用

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

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

引用

International Conference on Intelligent control and Information Processing (ICICIP)

作者： Xiang Zhou Xiaoli Li Dexin Liu Yixin Yin Xianzhong Chen Qingwen Hou Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education University of Science and Technology Beijing Beijing China University of Science and Technology Beijing Beijing Beijing CN

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 guidance for the future research of burden surface based on the data of phased-array radar.

关键词： Turning Blast furnaces Radar imaging Feature extraction Surface treatment

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