A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

Dynamic analysis of the NPP pressure sensor system

The aim of the work was to develop on-line methods of control and diagnostics of pressure sensors at a NPP. The analysis was carried out along two lines:

1. i) The detection system including the sensor itself was modeled theoretically to obtain and study its transfer function, and to establish correspondence between the spectral characteristics of the pressure signal.

2. ii) The numerical processing of the signal using the AR technique to reconstruct the transfer function and evaluate the system's response, to a step impulse, as well as the spectral analysis. The hydraulic model presented indicate that the spectral lines observed at 10 Hz are due to longitu-dinal oscillations of water in the system branches, while the response time of the detection system is effectively the time of signal retardation in the porous ceramic diaphragms of the δ-cell of the sensor itself.

     

Bivariate Scientific Function Finding in a Sampled,Real-Data Testbed

This article reports the results of a study of domain-independent function finding based on a collection of several hundred real scientific data sets. Prior studies have introduced the controversial idea of discovering functional relationships of interest to scientists directly from the data they collect. The evidence presented here supports the view that this is sometimes possible, but it also suggests how often purely data-driven discovery is not possible and how much more difficult it may be than has often been assumed. Experience with sampled examples of real scientific data suggests as well that emphasis on search in prior studies may have been misplaced. For the function-finding problems studied here, scientists typically propose only a handful of different functional relationships. The difficulty is not in searching through a large space of relationships but in evaluating a few common ones to determine if they are likely to be of scientific interest.     

智能化红外焊缝检测仪的实时信号自相关分析

详细介绍了在改进红外焊缝检测仪的过程中实时信号的一种抗干扰方法－－自相关方法。重点离自相关方法的数学模型，软件框图及设计思想，并给出了实验结果。     

Stability analysis and antiwindup design of uncertain discrete-time switched linear systems subject to actuator saturation

This paper investigates the stability analysis and antiwindup design problem for a class of discrete-time switched linear systems with time-varying norm-bounded uncertainties and saturating actuators by using the switched Lyapunov function approach.Supposing that a set of linear dynamic output controllers have been designed to stabilize the switched system without considering its input saturation,we design antiwindup compensation gains in order to enlarge the domain of attraction of the closed-loop system in the presence of saturation.Then,in terms of a sector condition,the antiwindup compensation gains which aim to maximize the estimation of domain of attraction of the closed-loop system are presented by solving a convex optimization problem with linear matrix inequality(LMI)constraints.A numerical example is given to demonstrate the effectiveness of the proposed design method.     

一种改进的灰度预测模型

灰色预测模型通常是GM模型,但预测精度有时不令人满意。在对模型GM做了进一步研究的基础上,提出了一个预测精度较高的新灰色预测模型,并从理论上证明了这种模型可以有效提高建模数据序列的光滑度。最后把此方法应用于东部某镇GDP的建模中,试验结果表明该方法是可行且有效的,所建模型的精度优于传统GM（1,1）模型的精度。     

全局优化的一类新的F-C函数

针对求解全局优化问题,有很多种求解方法。文中提出了一种快速求解一般无约束最优化问题的辅助函数方法。即F-C函数方法。该方法与填充函数法和跨越函数法相比较,既有相同点又有不同点。F-C函数法最大的优点就是在极小化F-C函数阶段中只需要进行一次局部极小化算法就能得到比当前极小值更低的目标函数局部极小点。文中在无Lipschitz连续的条件下,给出了一类新的求解全局优化问题的F-C函数。文中讨论了该F-C函数的优良性质并对该函数设计了相应的算法。最后,通过数值试验表明该F-C函数方法具有有效性和可行性。     

裂纹对石墨烯拉伸力学性能影响的数值仿真

为研究裂纹对石墨烯力学性能的影响,以锯齿型石墨烯为研究对象,基于分子动力学方法,采用Tersoff势函数分析裂纹长度对石墨烯拉伸力学性能的影响以及含有裂纹的石墨烯在不同应变率下的拉伸变形破坏过程.结果表明,裂纹长度的增加大大减小石墨烯的抗拉强度和抗拉应变,对弹性模量有一定影响;裂纹降低石墨烯的抗拉应变对应变率的敏感性,但对于含有裂纹的石墨烯,仍可以增大加载速率来提高石墨烯的抗拉性能.     

P2P网络中的超级节点选取算法研究

P2P系统在构建覆盖网络时未考虑到与物理网络的结合,导致逻辑拓扑结构与物理拓扑结构严重不匹配问题,增加P2P相邻节点间的延迟。本文提出一种基于区域划分的超级节点选取机制,将P2P网络中的节点按照物理位置划分成若干区域,保证区域内节点在物理位置上是相近的。利用MATLAB进行仿真实验,仿真实验表明使用这种机制能降低半分布式P2P网络的信息检索延迟,有效地提高检索的效率,并且具有较好的可扩展性。     

一种基于相关函数法铁磁谐振波形研究

对铁磁谐振波形进行了系统的分类分级,提出了一种基于Matlab的相关函数方法,并且利用大量数据进行对比,已经得到了效果验证,经过桂林市电力电容器责任有限公司认证,具有良好的实际效果。