Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022595
A. Ioannidou, S. Manenti, L. Gini, F. Groppi
131I and 137Cs and 134Cs fallout isotopes were measured in the Milano region (45°N), Italy over one month after the nuclear accident in Fukushima, Japan. Daily monitoring of the airborne activity levels carried out with a high volume air sampler, gave increased atmospheric radioactivity on air filter taken on 30 March 2011, while the maximum activity of 467 µBq m−3, occurred at April 3–4, 2011. Radionuclides from Fukushima fallout were first detected at Milano region in a rain water sample, at 27–28 March, 2011 with the concentrations of 131I and 137Cs isotopes in the rainwater to be equal to 0.89 Bq L−1 and 0.12 Bq L−1, respectively. During the same days a snowfall sample was collected from Monte Rosa mountain at a height of 3000 m, with the concentrations of 131I and 137Cs in snowfall to be lower than that in rainwater sample. A sample of dry deposition that was collected 9 days after the first rainfall event of 27-28 March, 2011 showed that the dry deposition of 131I and 137Cs was 0.40 Bq m−2 and 0.24 Bq m−2 respectively. The concentration of 131I in goat and cow milk samples collected on 9 April, 2011 from a farm at a village in Anzasca valley near Macugnaga (Monte Rosa mountain), were 0.30 Bq L−1 and 0.37 Bq L−1 respectively.
在日本福岛核事故发生一个多月后，在意大利米兰地区(45°N)测量了131I、137Cs和134Cs的放射性沉降同位素。使用大容量空气采样器进行的每日空气活动水平监测显示，2011年3月30日空气过滤器上的大气放射性增加，而2011年4月3日至4日的最大活动为467µBq m−3。2011年3月27日至28日，在米兰地区的雨水样本中首次检测到福岛沉降物中的放射性核素，雨水中的131I和137Cs同位素浓度分别为0.89 Bq L - 1和0.12 Bq L - 1。同一天，在海拔3000 m的Monte Rosa山采集了降雪样品，降雪中131I和137Cs的浓度低于雨水样品。2011年3月27日至28日首次降雨后第9天采集的干沉降样品表明，131I和137Cs的干沉降量分别为0.40 Bq m−2和0.24 Bq m−2。2011年4月9日从靠近Macugnaga (Monte Rosa山)的Anzasca山谷一个村庄的一个农场采集的羊奶和牛奶样品中的131 - i浓度分别为0.30 Bq L - 1和0.37 Bq L - 1。
{"title":"Fukushima fallout of 131I, 137Cs, 134Cs at Milano, Italy","authors":"A. Ioannidou, S. Manenti, L. Gini, F. Groppi","doi":"10.1109/ICNC.2011.6022595","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022595","url":null,"abstract":"<sup>131</sup>I and <sup>137</sup>Cs and <sup>134</sup>Cs fallout isotopes were measured in the Milano region (45°N), Italy over one month after the nuclear accident in Fukushima, Japan. Daily monitoring of the airborne activity levels carried out with a high volume air sampler, gave increased atmospheric radioactivity on air filter taken on 30 March 2011, while the maximum activity of 467 µBq m<sup>−3</sup>, occurred at April 3–4, 2011. Radionuclides from Fukushima fallout were first detected at Milano region in a rain water sample, at 27–28 March, 2011 with the concentrations of <sup>131</sup>I and <sup>137</sup>Cs isotopes in the rainwater to be equal to 0.89 Bq L<sup>−1</sup> and 0.12 Bq L<sup>−1</sup>, respectively. During the same days a snowfall sample was collected from Monte Rosa mountain at a height of 3000 m, with the concentrations of <sup>131</sup>I and <sup>137</sup>Cs in snowfall to be lower than that in rainwater sample. A sample of dry deposition that was collected 9 days after the first rainfall event of 27-28 March, 2011 showed that the dry deposition of <sup>131</sup>I and <sup>137</sup>Cs was 0.40 Bq m<sup>−2</sup> and 0.24 Bq m<sup>−2</sup> respectively. The concentration of <sup>131</sup>I in goat and cow milk samples collected on 9 April, 2011 from a farm at a village in Anzasca valley near Macugnaga (Monte Rosa mountain), were 0.30 Bq L<sup>−1</sup> and 0.37 Bq L<sup>−1</sup> respectively.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115303603","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022551
Long Chen, Wei Huo, Haitao Mi, Zhaoqing Zhang, Xiaobing Feng, Zhiyuan Li
Machine translation (MT), with its broad potential use, has gained increased attention from both researchers and software vendors. To generate high quality translations, however, MT decoders can be highly computation intensive. With significant raw computing power, multi-core microprocessors have the potential to speed up MT software on desktop machines. However, retrofitting existing MT decoders is a nontrivial issue. Race conditions and atomicity issues are among those complications making parallelization difficult. In this article, we show that, to parallelize a state-of-the-art MT decoder, it is much easier to overcome such difficulties by using a process-based parallelization method, called functional task parallelism, than using conventional thread-based methods. We achieve a 7.60 times speed up on an 8-core desktop machine while making significantly less changes to the original sequential code than required by using multiple threads.
{"title":"Parallelizing a machine translation decoder for multicore computer","authors":"Long Chen, Wei Huo, Haitao Mi, Zhaoqing Zhang, Xiaobing Feng, Zhiyuan Li","doi":"10.1109/ICNC.2011.6022551","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022551","url":null,"abstract":"Machine translation (MT), with its broad potential use, has gained increased attention from both researchers and software vendors. To generate high quality translations, however, MT decoders can be highly computation intensive. With significant raw computing power, multi-core microprocessors have the potential to speed up MT software on desktop machines. However, retrofitting existing MT decoders is a nontrivial issue. Race conditions and atomicity issues are among those complications making parallelization difficult. In this article, we show that, to parallelize a state-of-the-art MT decoder, it is much easier to overcome such difficulties by using a process-based parallelization method, called functional task parallelism, than using conventional thread-based methods. We achieve a 7.60 times speed up on an 8-core desktop machine while making significantly less changes to the original sequential code than required by using multiple threads.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375660","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022054
Wang Honghui, Zhang Hao, Hong Liang
Design and implement a transparent access to heterogeneous database platforms, successfully solved the transparency of data access. Using JNDI data source to realize the nodes in the dynamic management of heterogeneous databases; the study used an advanced model of the Struts MVC model, made the system has high maintainability and scalability.
{"title":"Notice of RetractionData transparent access to heterogeneous database based on XML technology","authors":"Wang Honghui, Zhang Hao, Hong Liang","doi":"10.1109/ICNC.2011.6022054","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022054","url":null,"abstract":"Design and implement a transparent access to heterogeneous database platforms, successfully solved the transparency of data access. Using JNDI data source to realize the nodes in the dynamic management of heterogeneous databases; the study used an advanced model of the Struts MVC model, made the system has high maintainability and scalability.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"444 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124257624","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022115
Junying Chen, Zhe Li
In this paper, weighted mean subtractive clustering algorithms are proposed to find cluster centers of the dataset. Then the found cluster centers act as the centers of radial basis functions. In weighted mean subtractive clustering algorithms, subtractive clustering is used to find center prototypes and then weighted mean methods are used to create new centers. Three weighted mean methods are tried to create more effective centers. Comparative experiments were executed between subtractive clustering and three weighted mean subtractive clustering algorithms on five benchmark datasets. Next, the performance of RBF neural networks set with the proposed algorithms was studied. The experimental results suggest that all three weighted mean subtractive clustering algorithms can find more accurate centers and can be successfully applied to design RBF neural networks. The RBF neural networks determined by weighted mean subtractive clustering algorithms have rather simpler network architecture but with slightly lower classification accuracy than ones determined by subtractive clustering algorithm.
{"title":"Designing RBF neural networks with weighted mean subtractive clustering algorithms","authors":"Junying Chen, Zhe Li","doi":"10.1109/ICNC.2011.6022115","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022115","url":null,"abstract":"In this paper, weighted mean subtractive clustering algorithms are proposed to find cluster centers of the dataset. Then the found cluster centers act as the centers of radial basis functions. In weighted mean subtractive clustering algorithms, subtractive clustering is used to find center prototypes and then weighted mean methods are used to create new centers. Three weighted mean methods are tried to create more effective centers. Comparative experiments were executed between subtractive clustering and three weighted mean subtractive clustering algorithms on five benchmark datasets. Next, the performance of RBF neural networks set with the proposed algorithms was studied. The experimental results suggest that all three weighted mean subtractive clustering algorithms can find more accurate centers and can be successfully applied to design RBF neural networks. The RBF neural networks determined by weighted mean subtractive clustering algorithms have rather simpler network architecture but with slightly lower classification accuracy than ones determined by subtractive clustering algorithm.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"45 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120853992","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022056
Y. Li, Xun Cai, M. Li
On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.
{"title":"Notice of RetractionA new neural network algorithm based on conjugate gradient and output weight optimization","authors":"Y. Li, Xun Cai, M. Li","doi":"10.1109/ICNC.2011.6022056","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022056","url":null,"abstract":"On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"2 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120993613","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022101
Hong Hu, Zhongzhi Shi
In order to probe the secret of our brain, it is necessary to design large scale dynamical neural circuits( more than 106 neurons) to simulate complex process of our brain. But such kind task is not easy to achieve only based on the analysis of partial equations especially for complex neural models, e.g. Rose-Hindmarsh (RH) model. So we should develop a novel approach which combines logic and machine learning in the designation or analysis of large scale neural circuits, and this new approach should be able to greatly simplify the designation of large scale dynamical neural circuits which is really very important both for cognition science and neural science. For this purpose, we introduce the concept of fuzzy logical framework of a neural circuit, and we proved that if the behave of a neural circuit can be described by first order partial differential equations, then such kind neural circuit can be simulated with arbitrary small errors by a Hopfield neural circuit which has a uniform structure or a fuzzy logical dynamical system; for more, a novel learning approach for large scale layered neural circuits based on PSVM and back propagation is developed for training Hopfield neural circuits.
{"title":"Notice of RetractionThe eqviualence of fuzzy logical dynamics and the neural circuits' dynamics","authors":"Hong Hu, Zhongzhi Shi","doi":"10.1109/ICNC.2011.6022101","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022101","url":null,"abstract":"In order to probe the secret of our brain, it is necessary to design large scale dynamical neural circuits( more than 106 neurons) to simulate complex process of our brain. But such kind task is not easy to achieve only based on the analysis of partial equations especially for complex neural models, e.g. Rose-Hindmarsh (RH) model. So we should develop a novel approach which combines logic and machine learning in the designation or analysis of large scale neural circuits, and this new approach should be able to greatly simplify the designation of large scale dynamical neural circuits which is really very important both for cognition science and neural science. For this purpose, we introduce the concept of fuzzy logical framework of a neural circuit, and we proved that if the behave of a neural circuit can be described by first order partial differential equations, then such kind neural circuit can be simulated with arbitrary small errors by a Hopfield neural circuit which has a uniform structure or a fuzzy logical dynamical system; for more, a novel learning approach for large scale layered neural circuits based on PSVM and back propagation is developed for training Hopfield neural circuits.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127297739","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022203
Li Ge, Bo Cui
For the multivariate forecast of Gross Domestic Product (GDP), the common features of traditional forecast methods are difficult to express the time cumulative effects in real forecast, and on the other hand, the factors influencing GDP have very typical timing characteristics. Therefore, in consideration of increasing GDP forecast accuracy, process neural network (PNN) was used into the GDP forecast. Making use of the feature of time-varying input function in PNN, the time and space cumulative effect of GDP influence factors was adequately considered into the forecast, and penalty factor was introduced to PNN training to improve BP algorithm. The GDP forecast model of Heilongjiang Province was established based on the above improved algorithm and it was compared and analyzed with the traditional method. The result shows that the PNN model has higher accuracy.
{"title":"Research on forecast of GDP based on process neural network","authors":"Li Ge, Bo Cui","doi":"10.1109/ICNC.2011.6022203","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022203","url":null,"abstract":"For the multivariate forecast of Gross Domestic Product (GDP), the common features of traditional forecast methods are difficult to express the time cumulative effects in real forecast, and on the other hand, the factors influencing GDP have very typical timing characteristics. Therefore, in consideration of increasing GDP forecast accuracy, process neural network (PNN) was used into the GDP forecast. Making use of the feature of time-varying input function in PNN, the time and space cumulative effect of GDP influence factors was adequately considered into the forecast, and penalty factor was introduced to PNN training to improve BP algorithm. The GDP forecast model of Heilongjiang Province was established based on the above improved algorithm and it was compared and analyzed with the traditional method. The result shows that the PNN model has higher accuracy.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125311270","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022158
J. Jhang
This research proposes an economic and effective experimental design method of multiple characteristics to deal with the parameter design problem with many continuous parameters and levels. It uses TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ANN (Artificial Neural Network) to train the optimal function framework of parameter design. It combines SC (Soft Computing) of SA (Simulated Anneal) and GA (Genetic Algorithm) to search the optimal parameters combination for the optimal parameter of aerospace aluminum alloy weldment. To improve previous experimental methods for multiple characteristics, this research method employs SA to search the optimal parameter such that the potential parameter can be evaluated more completely and objectively. Additionally, the model can learn the relationship between the welding parameters and the quality responses of different aluminum alloy materials to facilitate the future applications in the decision-making of parameter settings for automatic welding equipment. The research results can be presented to the industries as a reference, and improve the product quality and welding efficiency to relevant welding industries.
本研究提出了一种经济有效的多特征试验设计方法，以解决具有多连续参数和水平的参数设计问题。利用TOPSIS (Order Preference Technique of Similarity to Ideal Solution)和ANN (Artificial Neural Network)训练参数设计的最优函数框架。将模拟退火软计算与遗传算法相结合，搜索航空铝合金焊件最优参数组合。为了改进以往的多特性实验方法，本研究方法采用SA来搜索最优参数，从而更全面、客观地评价潜在参数。此外，该模型还可以学习到不同铝合金材料的焊接参数与质量响应之间的关系，便于今后在自动焊接设备参数设置决策中的应用。研究成果可供相关行业参考，提高相关焊接行业的产品质量和焊接效率。
{"title":"The optimal parameter design of aerospace aluminum alloy weldment via soft computing","authors":"J. Jhang","doi":"10.1109/ICNC.2011.6022158","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022158","url":null,"abstract":"This research proposes an economic and effective experimental design method of multiple characteristics to deal with the parameter design problem with many continuous parameters and levels. It uses TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ANN (Artificial Neural Network) to train the optimal function framework of parameter design. It combines SC (Soft Computing) of SA (Simulated Anneal) and GA (Genetic Algorithm) to search the optimal parameters combination for the optimal parameter of aerospace aluminum alloy weldment. To improve previous experimental methods for multiple characteristics, this research method employs SA to search the optimal parameter such that the potential parameter can be evaluated more completely and objectively. Additionally, the model can learn the relationship between the welding parameters and the quality responses of different aluminum alloy materials to facilitate the future applications in the decision-making of parameter settings for automatic welding equipment. The research results can be presented to the industries as a reference, and improve the product quality and welding efficiency to relevant welding industries.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"23 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126649146","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6022401
Q. Luo, Jianfeng Wu, Yun Yang
This paper develops an improved fast harmony search (IFHS) algorithm for solving optimization problems. The proposed IFHS algorithm employs novel methods for generating new solution vectors and expanding the scale of new solution vectors to enhance accuracy and convergence rate of harmony search (HS) algorithm. Moreover, the IFHS algorithm combined with MODFLOW is successfully used to solve the problem of hydrogeological parameters identification. The results show that the proposed algorithm, compared with other heuristic methods, has more powerful ability of global searching and faster convergence rate for complex parameter identification problems of groundwater systems.
{"title":"An improved fast harmony search algorithm for identification of hydrogeological parameters","authors":"Q. Luo, Jianfeng Wu, Yun Yang","doi":"10.1109/ICNC.2011.6022401","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022401","url":null,"abstract":"This paper develops an improved fast harmony search (IFHS) algorithm for solving optimization problems. The proposed IFHS algorithm employs novel methods for generating new solution vectors and expanding the scale of new solution vectors to enhance accuracy and convergence rate of harmony search (HS) algorithm. Moreover, the IFHS algorithm combined with MODFLOW is successfully used to solve the problem of hydrogeological parameters identification. The results show that the proposed algorithm, compared with other heuristic methods, has more powerful ability of global searching and faster convergence rate for complex parameter identification problems of groundwater systems.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184969","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 : 2011-07-26DOI: 10.1109/ICNC.2011.6021902
Yuxiang Wu, S. Chen
In this paper, the Adaptive motion/force control problems of robot manipulators with uncertainties and end-effector constraints are addressed. A RBF neural networks and a linear observer are employed to construct the controller for constrained robot manipulators with only position measurement. The proposed controller guarantees that all the signals of the closed-loop system are bounded. The stability of the closed-loop system and the boundedness of tracking error are proved using Lyapunov stability synthesis. Finally, simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.
{"title":"Adaptive neural motion/force control of constrained robot manipulators by position measurement","authors":"Yuxiang Wu, S. Chen","doi":"10.1109/ICNC.2011.6021902","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6021902","url":null,"abstract":"In this paper, the Adaptive motion/force control problems of robot manipulators with uncertainties and end-effector constraints are addressed. A RBF neural networks and a linear observer are employed to construct the controller for constrained robot manipulators with only position measurement. The proposed controller guarantees that all the signals of the closed-loop system are bounded. The stability of the closed-loop system and the boundedness of tracking error are proved using Lyapunov stability synthesis. Finally, simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115543018","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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