The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the...The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the fuzzy sets theory. While the fuzzy theory is widely used, the completely certain membership function that has no any fuzziness at all has been the bottleneck of the applications of this theory. Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. It can represent the fuzziness and randomness and their relations of uncertain concepts. Also cloud models can show the concept granularity in multi-scale spaces by the digital characteristic Entropy (En). The normal cloud model not only broadens the form conditions of the normal distribution but also makes the normal membership function be the expectation of the random membership degree. In this paper, the universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing. It would be more applicable and universal in the representation of uncertain notions.展开更多
Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by nois...Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by noises and intensity non-uniformity with a huge amount of data needs to be processed regularly,so it is hard to detect convective clouds in satellite image using traditional SVM.To deal with this problem,a novel method for detection of convective clouds was proposed based on fast fuzzy support vector machine(FFSVM).FFSVM was constructed by eliminating feeble samples and designing new membership function as two aspects.Firstly,according to the distribution characteristics of fuzzy inseparable sample set and the fact that the classification hyper-plane is only determined by support vectors,this paper uses SVDD,Gaussian model and border vector extraction model comprehensively to design a sample selection method in three steps,which can eliminate most of redundant samples and keep possible support vectors.Then,by defining adaptive parameters related to attenuation rate and critical membership on the basis of the distribution characteristics of training set,an adaptive membership function is designed.Finally,the FFSVM is trained by the remaining samples using adaptive membership function to detect convective clouds.The experiments on FY-2D satellite images show that the proposed method,compared with traditional FSVM,not only remarkably reduces training time,but also further improves the accuracy of convective clouds detection.展开更多
车辆跟驰模型中的驾驶行为是一个复杂的推理、决策、执行过程,在知识表达和推理机制上存在模糊性和随机性.针对当前车辆跟驰模型研究存在的不足,引入云模型理论,确定了跟驰模型中的决策变量及概念判断子集,设计了模型参数标定的实验方法...车辆跟驰模型中的驾驶行为是一个复杂的推理、决策、执行过程,在知识表达和推理机制上存在模糊性和随机性.针对当前车辆跟驰模型研究存在的不足,引入云模型理论,确定了跟驰模型中的决策变量及概念判断子集,设计了模型参数标定的实验方法,在实验数据的基础上,计算了相关参数的隶属云特征值,总结了基于行为规则的车辆跟驰云推理机制,构造了车辆跟驰云模型CFCM(Car Following Cloud Model).展开更多
文摘The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the fuzzy sets theory. While the fuzzy theory is widely used, the completely certain membership function that has no any fuzziness at all has been the bottleneck of the applications of this theory. Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. It can represent the fuzziness and randomness and their relations of uncertain concepts. Also cloud models can show the concept granularity in multi-scale spaces by the digital characteristic Entropy (En). The normal cloud model not only broadens the form conditions of the normal distribution but also makes the normal membership function be the expectation of the random membership degree. In this paper, the universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing. It would be more applicable and universal in the representation of uncertain notions.
基金supported in part by the National Natural Science Foundation of China under Grants (61471212)Natural Science Foundation of Zhejiang Province under Grants (LY16F010001)+1 种基金Science and Technology Program of Zhejiang Meteorological Bureau under Grants (2016YB01)Natural Science Foundation of Ningbo under Grants(2016A610091,2017A610297)
文摘Support vector machine(SVM)is easily affected by noises and outliers,and its training time dramatically increases with the growing in number of training samples.Satellite cloud image may easily be deteriorated by noises and intensity non-uniformity with a huge amount of data needs to be processed regularly,so it is hard to detect convective clouds in satellite image using traditional SVM.To deal with this problem,a novel method for detection of convective clouds was proposed based on fast fuzzy support vector machine(FFSVM).FFSVM was constructed by eliminating feeble samples and designing new membership function as two aspects.Firstly,according to the distribution characteristics of fuzzy inseparable sample set and the fact that the classification hyper-plane is only determined by support vectors,this paper uses SVDD,Gaussian model and border vector extraction model comprehensively to design a sample selection method in three steps,which can eliminate most of redundant samples and keep possible support vectors.Then,by defining adaptive parameters related to attenuation rate and critical membership on the basis of the distribution characteristics of training set,an adaptive membership function is designed.Finally,the FFSVM is trained by the remaining samples using adaptive membership function to detect convective clouds.The experiments on FY-2D satellite images show that the proposed method,compared with traditional FSVM,not only remarkably reduces training time,but also further improves the accuracy of convective clouds detection.
文摘车辆跟驰模型中的驾驶行为是一个复杂的推理、决策、执行过程,在知识表达和推理机制上存在模糊性和随机性.针对当前车辆跟驰模型研究存在的不足,引入云模型理论,确定了跟驰模型中的决策变量及概念判断子集,设计了模型参数标定的实验方法,在实验数据的基础上,计算了相关参数的隶属云特征值,总结了基于行为规则的车辆跟驰云推理机制,构造了车辆跟驰云模型CFCM(Car Following Cloud Model).
