近年来室内燃气事故多发,而燃气用户风险意识淡薄、户内安全检查难度大。针对现行室内燃气安全管理技术多为静态主观评估的局限性,构建了基于模糊Petri网(Fuzzy Petri Net,FPN)的风险计算规则,提出了结合粒子群优化算法(Particles Swarm...近年来室内燃气事故多发,而燃气用户风险意识淡薄、户内安全检查难度大。针对现行室内燃气安全管理技术多为静态主观评估的局限性,构建了基于模糊Petri网(Fuzzy Petri Net,FPN)的风险计算规则,提出了结合粒子群优化算法(Particles Swarm Optimization,PSO)和FPN的室内燃气泄漏动态风险评估模型。首先,应用Petri网的直观图像描述和异步并发处理能力建立室内燃气泄漏事故风险演化的拓扑结构模型,借助FPN的模糊推理能力处理风险传播的不确定性;然后,根据燃气运维数据,融合PSO动态更新初始参数,提高风险评估的准确性。结果表明,基于PSO-FPN的室内风险评估方法可弱化燃气公司安检人员分析的主观不确定性,更为准确地量化风险因子演化过程,实现室内燃气泄漏风险的动态分析,有效支持户内燃气泄漏风险管控。展开更多
在分析模糊Petri网推理机制的基础上,将优化算法ACA(Ant Colony Algorithm)引入至FPN(Fuzzy Petri Net)的学习能力问题中。针对一知识库系统的具体实例,探讨该算法在FPN学习能力问题中的具体实现,并结合传统优化算法对比分析了它们各自...在分析模糊Petri网推理机制的基础上,将优化算法ACA(Ant Colony Algorithm)引入至FPN(Fuzzy Petri Net)的学习能力问题中。针对一知识库系统的具体实例,探讨该算法在FPN学习能力问题中的具体实现,并结合传统优化算法对比分析了它们各自的特点和性能优劣。仿真实验表明,ACA算法整体性能最佳,训练出的参数正确率较高,且所得的模糊Petri网具有很强的泛化能力和自适应功能。展开更多
In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecis...In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.展开更多
文摘近年来室内燃气事故多发,而燃气用户风险意识淡薄、户内安全检查难度大。针对现行室内燃气安全管理技术多为静态主观评估的局限性,构建了基于模糊Petri网(Fuzzy Petri Net,FPN)的风险计算规则,提出了结合粒子群优化算法(Particles Swarm Optimization,PSO)和FPN的室内燃气泄漏动态风险评估模型。首先,应用Petri网的直观图像描述和异步并发处理能力建立室内燃气泄漏事故风险演化的拓扑结构模型,借助FPN的模糊推理能力处理风险传播的不确定性;然后,根据燃气运维数据,融合PSO动态更新初始参数,提高风险评估的准确性。结果表明,基于PSO-FPN的室内风险评估方法可弱化燃气公司安检人员分析的主观不确定性,更为准确地量化风险因子演化过程,实现室内燃气泄漏风险的动态分析,有效支持户内燃气泄漏风险管控。
文摘在分析模糊Petri网推理机制的基础上,将优化算法ACA(Ant Colony Algorithm)引入至FPN(Fuzzy Petri Net)的学习能力问题中。针对一知识库系统的具体实例,探讨该算法在FPN学习能力问题中的具体实现,并结合传统优化算法对比分析了它们各自的特点和性能优劣。仿真实验表明,ACA算法整体性能最佳,训练出的参数正确率较高,且所得的模糊Petri网具有很强的泛化能力和自适应功能。
基金supported by Department of Computer Science Project of University of Jamia Millia Islamia, India (No. 39151-A)
文摘In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.
