Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6357939
Ding Qiang, Chen Hong, Chunlin Wang, Aipeng Jiang, Weiwei Lin
To solve chemical problems with variable and nonrigid constraints, a method based on particle swarm optimization (PSO) algorithm was presented. By mathematical analysis and transform, the variable constraints were regard as an item to be optimized. Then the item multiplied by penalty and combined with the primary objective function. So the primary problem was transferred to the multi-objective function, and can be solved by multi-objective PSO algorithm. With problems solved by multi-objective PSO and analysis of the solutions related with variable constraints, reasonable solution and optimal scheme can be obtained. The proposed method was used to optimize a chemical design problem and a parameter estimation problem. The results demonstrate that the proposed method is effective.
{"title":"Research of PSO algorithm with variable constraints in process system","authors":"Ding Qiang, Chen Hong, Chunlin Wang, Aipeng Jiang, Weiwei Lin","doi":"10.1109/WCICA.2012.6357939","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6357939","url":null,"abstract":"To solve chemical problems with variable and nonrigid constraints, a method based on particle swarm optimization (PSO) algorithm was presented. By mathematical analysis and transform, the variable constraints were regard as an item to be optimized. Then the item multiplied by penalty and combined with the primary objective function. So the primary problem was transferred to the multi-objective function, and can be solved by multi-objective PSO algorithm. With problems solved by multi-objective PSO and analysis of the solutions related with variable constraints, reasonable solution and optimal scheme can be obtained. The proposed method was used to optimize a chemical design problem and a parameter estimation problem. The results demonstrate that the proposed method is effective.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6359079
Xuemin Sun, Shuhua Liu, Jing Xia, Xue Yang
A new method of map building is presented for mobile robots in unknown indoor environments. It combined Internal Spiral Coverage (ISC) algorithm, A* algorithm and wildfire algorithm to build the map in unknown indoor environments. The rasterization of sensor detection zone can improve the accuracy of map building which is affected by the error of the sensor data. Once an obstacle is explored, the robot will immediately go around it to identify. Simulation results show that the proposed method of map building is very effective in different indoor environments.
{"title":"A method of map building for robots in unknown indoor environments","authors":"Xuemin Sun, Shuhua Liu, Jing Xia, Xue Yang","doi":"10.1109/WCICA.2012.6359079","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6359079","url":null,"abstract":"A new method of map building is presented for mobile robots in unknown indoor environments. It combined Internal Spiral Coverage (ISC) algorithm, A* algorithm and wildfire algorithm to build the map in unknown indoor environments. The rasterization of sensor detection zone can improve the accuracy of map building which is affected by the error of the sensor data. Once an obstacle is explored, the robot will immediately go around it to identify. Simulation results show that the proposed method of map building is very effective in different indoor environments.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125729154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6358425
Yahui Wang, Yifeng Huo
Ellipsoidal basis function(EBF) can make the partition and limitary of input space. Compared with the Guassian function of radial basis function(RBF) neural network, the EBF can make the partition of input space more specific, which has the higher capability of pattern recognition. However, the neural network has a common problem of training the weight and threshold. The evolution of genetic algorithm(GA) can maximumly optimize the training time of neural network. In this paper, a new method based on GA-EBF neural network was proposed. The simulation experiment shows that the proposed method has a higher rate of fault diagnosis than that of RBF neural network.1
{"title":"Fault diagnosis based on genetic algorithm for optimization of EBF neural network","authors":"Yahui Wang, Yifeng Huo","doi":"10.1109/WCICA.2012.6358425","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6358425","url":null,"abstract":"Ellipsoidal basis function(EBF) can make the partition and limitary of input space. Compared with the Guassian function of radial basis function(RBF) neural network, the EBF can make the partition of input space more specific, which has the higher capability of pattern recognition. However, the neural network has a common problem of training the weight and threshold. The evolution of genetic algorithm(GA) can maximumly optimize the training time of neural network. In this paper, a new method based on GA-EBF neural network was proposed. The simulation experiment shows that the proposed method has a higher rate of fault diagnosis than that of RBF neural network.1","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125994582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6359423
Qitao Jin, Jiang Wang, Bin Deng, Xile Wei, Feng Dong, Huiyan Li, Y. Che
Fast axonal conduction of action potentials in mammals relies on myelin insulation. Demyelination can cause slowed, blocked, desynchronized, or paradoxically excessive spiking that underlies the symptoms observed in demyelination diseases. Feedback control via functional electrical stimulation (FES) seems to be a promising treatment modality in such diseases. However, there are challenges to implementing such method for neurons: high nonlinearity, biological tissue constrains and unobservable ion channel states. To address this problem, we propose an estimating and tracking control strategy for systems based on Kalman filter, in order to enhance the action potential propagation reliability of demyelinated neuron via FES. Our method could promote the design of new closed-loop electrical stimulation systems for patients suffering from different nerve system dysfunctions.
{"title":"UKF-based state feedback control of abnormal neural oscillations in demyelination symptom","authors":"Qitao Jin, Jiang Wang, Bin Deng, Xile Wei, Feng Dong, Huiyan Li, Y. Che","doi":"10.1109/WCICA.2012.6359423","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6359423","url":null,"abstract":"Fast axonal conduction of action potentials in mammals relies on myelin insulation. Demyelination can cause slowed, blocked, desynchronized, or paradoxically excessive spiking that underlies the symptoms observed in demyelination diseases. Feedback control via functional electrical stimulation (FES) seems to be a promising treatment modality in such diseases. However, there are challenges to implementing such method for neurons: high nonlinearity, biological tissue constrains and unobservable ion channel states. To address this problem, we propose an estimating and tracking control strategy for systems based on Kalman filter, in order to enhance the action potential propagation reliability of demyelinated neuron via FES. Our method could promote the design of new closed-loop electrical stimulation systems for patients suffering from different nerve system dysfunctions.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132412328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6358092
J. Naiborhu, F. Firman, M. L. Sitanggang
Iterative learning control (ILC) refers to a class of self-tuning controllers where the system performance of a specified task is gradually improved or perfected based on the previous performance of identical tasks. In this paper, based on the modified steepest descent control we proposed the iterative learning control algorithm for nonlinear nonminimum phase system. By applying the modified steepest descent control we have the extended system with relative degree greater one than original systems. By extending result of Gosh, cs [1], the convergence of algorithm is guaranteed.
{"title":"Iterative learning control based on modified steepest descent control for output tracking of nonlinear non-minimum phase systems","authors":"J. Naiborhu, F. Firman, M. L. Sitanggang","doi":"10.1109/WCICA.2012.6358092","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6358092","url":null,"abstract":"Iterative learning control (ILC) refers to a class of self-tuning controllers where the system performance of a specified task is gradually improved or perfected based on the previous performance of identical tasks. In this paper, based on the modified steepest descent control we proposed the iterative learning control algorithm for nonlinear nonminimum phase system. By applying the modified steepest descent control we have the extended system with relative degree greater one than original systems. By extending result of Gosh, cs [1], the convergence of algorithm is guaranteed.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6359036
Jiang Aipeng, Li Weiwei, Ding Qiang, Wang Jian, Jiang Zhou-shu, H. Guohui
It is the most effective resource use practices for slurry and slime to be used as fuel for Fluidized bed boiler. The dry desulfurization of sludge Fluidized bed boiler is a large time delay system, and load disturbance of this system changes frequently. In order to achieve stable control of SO2 emission, and meet environmental requirements, a fuzzy control technology combined with the optimal feed-forward was designed. Combined with field experience, fuzzy controller was designed by fuzzy control technology, and then the integral process was added to achieve non-error track. Based on the objective to minimize disturbance impact, and in order to coordinate the desulfurization control and steam load control, a nonlinear programming problem for solving the optimal feed-forward parameters was established, from which the most excellent feed-forward form can be obtained. Results of 440T/H fluidized bed boiler show that the proposed method has satisfactory control effect. SO2concentration can fully meet environmental emissions requirements, and its fluctuation is relatively small.
{"title":"Research on control method combined with load coordinate for dry desulfurization of slurry fluidized bed boiler","authors":"Jiang Aipeng, Li Weiwei, Ding Qiang, Wang Jian, Jiang Zhou-shu, H. Guohui","doi":"10.1109/WCICA.2012.6359036","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6359036","url":null,"abstract":"It is the most effective resource use practices for slurry and slime to be used as fuel for Fluidized bed boiler. The dry desulfurization of sludge Fluidized bed boiler is a large time delay system, and load disturbance of this system changes frequently. In order to achieve stable control of SO2 emission, and meet environmental requirements, a fuzzy control technology combined with the optimal feed-forward was designed. Combined with field experience, fuzzy controller was designed by fuzzy control technology, and then the integral process was added to achieve non-error track. Based on the objective to minimize disturbance impact, and in order to coordinate the desulfurization control and steam load control, a nonlinear programming problem for solving the optimal feed-forward parameters was established, from which the most excellent feed-forward form can be obtained. Results of 440T/H fluidized bed boiler show that the proposed method has satisfactory control effect. SO2concentration can fully meet environmental emissions requirements, and its fluctuation is relatively small.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121584038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6357959
Shengyao Wang, Ling Wang, Ye Xu
According to the characteristics of the hybrid flow-shop scheduling problem (HFSP), the permutation based encoding and decoding schemes are designed and a probability model for describing the distribution of the solution space is built to propose a compact estimation of distribution algorithm (cEDA) in this paper. The algorithm uses only two individuals by sampling based on the probability model and updates the parameters of the probability model with the selected individual. The cEDA is efficient and easy to implement due to its low complexity and comparatively few parameters. Simulation results based on some widely-used instances and comparisons with some existing algorithms demonstrate the effectiveness and efficiency of the proposed compact estimation of distribution algorithm. The influence of the key parameter on the performance is investigated as well.
{"title":"A compact estimation of distribution algorithm for solving hybrid flow-shop scheduling problem","authors":"Shengyao Wang, Ling Wang, Ye Xu","doi":"10.1109/WCICA.2012.6357959","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6357959","url":null,"abstract":"According to the characteristics of the hybrid flow-shop scheduling problem (HFSP), the permutation based encoding and decoding schemes are designed and a probability model for describing the distribution of the solution space is built to propose a compact estimation of distribution algorithm (cEDA) in this paper. The algorithm uses only two individuals by sampling based on the probability model and updates the parameters of the probability model with the selected individual. The cEDA is efficient and easy to implement due to its low complexity and comparatively few parameters. Simulation results based on some widely-used instances and comparisons with some existing algorithms demonstrate the effectiveness and efficiency of the proposed compact estimation of distribution algorithm. The influence of the key parameter on the performance is investigated as well.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116984187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6359366
Zhiyuan Ming, Jin He, Chao Huang, Yu Lei
Support Vector Machine (SVM) is a machine learning theory based on statistical learning algorithms, SVM based on kernel function has lots of unique advantages on solving the small sample, nonlinear and high dimensional pattern recognition. This article al so uses BP neural networks, Support Vector Machine based on PSO algorithm and so on to be compared to identify propolis in Yunnan. Compared with traditional algorithms, it can solve the small sample, nonlinear and other issues. The experiments show the performance is good when using SVM kernel function on solving the herbs recognition.
{"title":"Recognition of crude drugs based on SVM","authors":"Zhiyuan Ming, Jin He, Chao Huang, Yu Lei","doi":"10.1109/WCICA.2012.6359366","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6359366","url":null,"abstract":"Support Vector Machine (SVM) is a machine learning theory based on statistical learning algorithms, SVM based on kernel function has lots of unique advantages on solving the small sample, nonlinear and high dimensional pattern recognition. This article al so uses BP neural networks, Support Vector Machine based on PSO algorithm and so on to be compared to identify propolis in Yunnan. Compared with traditional algorithms, it can solve the small sample, nonlinear and other issues. The experiments show the performance is good when using SVM kernel function on solving the herbs recognition.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-26DOI: 10.1109/WCICA.2012.6358112
Lequn Zhang, Jun‐e Feng
The model-input-state matrix of a Switched Boolean Control Network (SBCN) is introduced for the first time. This matrix contains all information of the model-input-state mapping. A necessary and sufficient condition for the controllability of SBCN is obtained. The corresponding control and switching law which drive a point to a given reachable point is designed. One sufficient condition for the observability of a SBCN is obtained. Under the assumption of controllability, one necessary and sufficient condition is derived for the observability.
{"title":"Model-input-state matrix of Switched Boolean Control Networks and its applications","authors":"Lequn Zhang, Jun‐e Feng","doi":"10.1109/WCICA.2012.6358112","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6358112","url":null,"abstract":"The model-input-state matrix of a Switched Boolean Control Network (SBCN) is introduced for the first time. This matrix contains all information of the model-input-state mapping. A necessary and sufficient condition for the controllability of SBCN is obtained. The corresponding control and switching law which drive a point to a given reachable point is designed. One sufficient condition for the observability of a SBCN is obtained. Under the assumption of controllability, one necessary and sufficient condition is derived for the observability.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122224798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-07-06DOI: 10.1109/WCICA.2012.6358301
Xiao-long Zhou, Shubin Wang, Xionglin Luo
The constraints of input variables and output variables commonly exit in the actual industrial production process. Due to the interference and different constraints between conflicting, the constraint conditions can not be all satisfied, appearing to look for less feasible solutions and global optimal solution and then bringing negative effects on the actual production. Based on Polyhedral pole, the constrained model predictive control feasibility and the soft constraints adjustment algorithm when infeasibility are discussed in this paper. The method in this article considers the feasibility analysis and the reasonable soft constraints adjustment before the rolling optimization in each step, which makes the whole control process meet the requirements of constraint conditions without changing the basic structure of MPC. Through the simulation results of the constrained CSTR system, the validity and feasibility of the algorithm are verified.
{"title":"Intersection analysis of input and output constraints in model predictive control and on-line adjustment of soft constraints","authors":"Xiao-long Zhou, Shubin Wang, Xionglin Luo","doi":"10.1109/WCICA.2012.6358301","DOIUrl":"https://doi.org/10.1109/WCICA.2012.6358301","url":null,"abstract":"The constraints of input variables and output variables commonly exit in the actual industrial production process. Due to the interference and different constraints between conflicting, the constraint conditions can not be all satisfied, appearing to look for less feasible solutions and global optimal solution and then bringing negative effects on the actual production. Based on Polyhedral pole, the constrained model predictive control feasibility and the soft constraints adjustment algorithm when infeasibility are discussed in this paper. The method in this article considers the feasibility analysis and the reasonable soft constraints adjustment before the rolling optimization in each step, which makes the whole control process meet the requirements of constraint conditions without changing the basic structure of MPC. Through the simulation results of the constrained CSTR system, the validity and feasibility of the algorithm are verified.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115292881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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