期刊文献+
共找到5篇文章
< 1 >
每页显示 20 50 100
Exponential distance distribution of connected neurons in simulations of two-dimensional in vitro neural network development
1
作者 Zhi-Song Lv Chen-Ping Zhu +4 位作者 Pei Nie Jing Zhao Hui-Jie Yang Yan-Jun Wang Chin-Kun Hu 《Frontiers of physics》 SCIE CSCD 2017年第3期133-138,共6页
The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a po... The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two- dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distri-bution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels. 展开更多
关键词 distance distribution connected neurons DEVELOPMENT EXPONENTIAL POWER-LAW NEURALNETWORKS complex systems
原文传递
A plotless density estimator with a Norton-Rice distribution for ordered distances
2
作者 Steen Magnussen 《Journal of Forestry Research》 SCIE CAS CSCD 2021年第6期2385-2401,共17页
A Norton-Rice distribution(NRD)is a versatile,flexible distribution for k ordered distances from a random location to the k nearest objects.In a context of plotless density estimation(PDE)with n randomly chosen sample... A Norton-Rice distribution(NRD)is a versatile,flexible distribution for k ordered distances from a random location to the k nearest objects.In a context of plotless density estimation(PDE)with n randomly chosen sample locations,and distances measured to the k=6 nearest objects,the NRD provided a good fit to distance data from seven populations with a census of forest tree stem locations.More importantly,the three parameters of a NRD followed a simple trend with the order(1,…,6)of observed distances.The trend is quantified and exploited in a proposed new PDE through a joint maximum likelihood estimation of the NRD parameters expressed as a functions of distance order.In simulated probability sampling from the seven populations,the proposed PDE had the lowest overall bias with a good performance potential when compared to three alternative PDEs.However,absolute bias increased by 0.8 percentage points when sample size decreased from 20 to 10.In terms of root mean squared error(RMSE),the new proposed estimator was at par with an estimator published in Ecology when this study was wrapping up,but otherwise superior to the remaining two investigated PDEs.Coverage of nominal 95%confidence intervals averaged 0.94 for the new proposed estimators and 0.90,0.96,and 0.90 for the comparison PDEs.Despite tangible improvements in PDEs over the last decades,a globally least biased PDE remains elusive. 展开更多
关键词 Fixed-count sampling Spatial point pattern distance distributions Forest inventory Joint maximum likelihood estimation BIAS Root mean squared error COVERAGE
下载PDF
Distribution of Miss Distance for Pursuit-Evasion Problem
3
作者 Shengwen Xiang Hongqi Fan Qiang Fu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第4期1161-1168,共8页
Miss distance is a critical parameter of assessing the performance for highly maneuvering targets interception(HMTI). In a realistic terminal guidance system, the control of pursuer depends on the estimate of unknown ... Miss distance is a critical parameter of assessing the performance for highly maneuvering targets interception(HMTI). In a realistic terminal guidance system, the control of pursuer depends on the estimate of unknown state, thus the miss distance becomes a random variable with a prior unknown distribution. Currently, such a distribution is mainly evaluated by the method of Monte Carlo simulation. In this paper, by integrating the estimation error model of zero-effort miss distance(ZEM) obtained by our previous work, an analytic method for solving the distribution of miss distance is proposed, in which the system is presumed to use a bang-bang control strategy. By comparing with the results of Monte Carlo simulations under four different types of disturbances(maneuvers), the correctness of the proposed method is validated. Results of this paper provide a powerful tool for the design, analysis and performance evaluation of guidance system. 展开更多
关键词 Analytical method bang-bang strategy miss distance distribution pursuit-evasion problem
下载PDF
On the ErdÖs Distance Conjecture in Geometry
4
作者 Amir Jafari Amin Najafi Amin 《Open Journal of Discrete Mathematics》 2016年第3期109-160,共52页
Erd&#214;s asks if it is possible to have n points in general position in the plane (no three on a line or four on a circle) such that for every i (1≤i≤n-1 ) there is a distance determined by the points that occ... Erd&#214;s asks if it is possible to have n points in general position in the plane (no three on a line or four on a circle) such that for every i (1≤i≤n-1 ) there is a distance determined by the points that occur exactly i times. So far some examples have been discovered for 2≤n≤8 [1] [2]. A solution for the 8 point is provided by I. Palasti [3]. Here two other possible solutions for the 8 point case as well as all possible answers to 4 - 7 point cases are provided and finally a brief discussion on the generalization of the problem to higher dimensions is given. 展开更多
关键词 ErdÖs distance Problem General Position Graph Pattern distance distribution
下载PDF
EXTENDED CLUSTERING COEFFICIENTS:GENERALIZATION OF CLUSTERING COEFFICIENTS IN SMALL-WORLD NETWORKS
5
作者 Wenjun XIAO Wenhong WEI +2 位作者 Weidong CHEN Yong QIN Behrooz PARHAMI 《Journal of Systems Science and Systems Engineering》 SCIE EI CSCD 2007年第3期370-382,共13页
The clustering coefficient C of a network, which is a measure of direct connectivity between neighbors of the various nodes, ranges from 0 (for no connectivity) to 1 (for full connectivity). We define extended clu... The clustering coefficient C of a network, which is a measure of direct connectivity between neighbors of the various nodes, ranges from 0 (for no connectivity) to 1 (for full connectivity). We define extended clustering coefficients C(h) of a small-world network based on nodes that are at distance h from a source node, thus generalizing distance-1 neighborhoods employed in computing the ordinary clustering coefficient C = C(1). Based on known results about the distance distribution Pδ(h) in a network, that is, the probability that a randomly chosen pair of vertices have distance h, we derive and experimentally validate the law Pδ(h)C(h) ≤ c log N / N, where c is a small constant that seldom exceeds 1. This result is significant because it shows that the product Pδ(h)C(h) is upper-bounded by a value that is considerably smaller than the product of maximum values for Pδ(h) and C(h). Extended clustering coefficients and laws that govern them offer new insights into the structure of small-world networks and open up avenues for further exploration of their properties. 展开更多
关键词 Clustering coefficient SMALL-WORLD extended clustering coefficient distance distribution
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部