为降低风险因素的不确定性和模糊性对易流态化货物海上运输风险评估准确性的影响,通过引入可信度概念,提出基于模糊Petri网(fuzzy Petri nets,FPN)的风险评估方法。对其风险因素进行分析,以易流态化货物海上运输事故为顶事件,建立层次...为降低风险因素的不确定性和模糊性对易流态化货物海上运输风险评估准确性的影响,通过引入可信度概念,提出基于模糊Petri网(fuzzy Petri nets,FPN)的风险评估方法。对其风险因素进行分析,以易流态化货物海上运输事故为顶事件,建立层次化评估指标体系;基于FPN理论,构建风险评估的FPN模型;运用模糊推理算法对其进行风险评估。实例分析表明:基于FPN模型的风险评估方法适用于对易流态化货物海上运输风险进行评估,且评估结果比用传统的风险评估方法得到的结果更加客观、准确,能为易流态化货物海上运输的风险预判和航线规划提供参考。展开更多
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustab...A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.展开更多
The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.展开更多
文摘为降低风险因素的不确定性和模糊性对易流态化货物海上运输风险评估准确性的影响,通过引入可信度概念,提出基于模糊Petri网(fuzzy Petri nets,FPN)的风险评估方法。对其风险因素进行分析,以易流态化货物海上运输事故为顶事件,建立层次化评估指标体系;基于FPN理论,构建风险评估的FPN模型;运用模糊推理算法对其进行风险评估。实例分析表明:基于FPN模型的风险评估方法适用于对易流态化货物海上运输风险进行评估,且评估结果比用传统的风险评估方法得到的结果更加客观、准确,能为易流态化货物海上运输的风险预判和航线规划提供参考。
文摘A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
基金Projects(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
