基于聚类和支持向量机的话务量预测模型

针对利用单因素时问序列模型进行话务量预测的不足,建立基于模糊C均值(FCM)聚类和支持向量机(SVM)的多元回归话务量预测模型.模型使用FCM算法对话务量的原始样本集聚类,选择与待预测样本特征最相似的样本子集作为训练集.使用SVM训练样本,通过决策回归函数预测话务量.实际话务量数据验证表明,该方法较周期时间序列和神经网络预测方法具有更高的预测精度和泛化能力.     

永磁同步电机的自适应反演滑模变结构控制

针对永磁同步电机提出一种基于反演的PMSM自适应滑模控制方案.设计基于反演的滑模变结构位置控制器,通过RBF神经网络实现系统参数变化和外部负载扰动等引起的不确定上界值的在线辨识,减小滑模控制器的控制量,并引入饱和函数来减弱系统的"抖动"现象.理论分析和仿真结果对比表明,基于RBF神经网络的自适应反演滑模控制对参数变化和外部负载扰动具有很好的鲁棒性,永磁同步电动机获得了很好的跟踪效果.     

Robust output feedback learning control for induction motor servo drives

This paper addresses the output feedback tracking control problem for induction motor servo drives with mechanical uncertainties: rotor angle, rotor speed and stator currents are assumed to be available for feedback. A robust adaptive learning control is designed under the assumption that the reference profile for the rotor angle is periodic with known period: it ‘learns’ the periodic disturbance signal by identifying the Fourier coefficients of any truncated approximation; ??₂ and ??_∞ transient performances are guaranteed in the ‘learning phase’. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: (i) the rotor position and flux modulus tracking errors exponentially converge to residual sets, which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series; (ii) when the unknown periodic disturbance can be represented by a finite Fourier series, the rotor position and flux modulus tracking errors exponentially converge to zero. As in field oriented‐control, the control algorithm generates references for the magnetizing flux component and for the torque component of the stator current leading to significant simplifications for current‐fed motors. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive control design for a class of nonsmooth nonlinear systems with matched and linearly parameterized uncertainty

This paper presents an adaptive control design for a class of nonsmooth nonlinear systems with matched uncertainty, which is linearly parameterized with a known discontinuous function. The design framework is based on the concept of the Filippov solution as the classical Lyapunov theory for smooth systems cannot be applied to establish the stability of the adaptive control system due to the presence of the discontinuity. It is proved that as an adaptive control system, the global Lyapunov stability with the convergence of the state of the controlled system to the origin can be achieved by evaluating the monotonicity of the Lyapunov function in the state space, particularly on the discontinuous surface, while the uniqueness of the solution of the closed‐loop system is not necessarily guaranteed. Some interesting numerical examples are demonstrated with simulation results. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive integral method combined with the loose GMRES algorithm for planar structures analysis

In this article, the adaptive integral method (AIM) is used to analyze large‐scale planar structures. Discretization of the corresponding integral equations by method of moment (MoM) with Rao‐Wilton‐Glisson (RWG) basis functions can model arbitrarily shaped planar structures, but usually leads to a fully populated matrix. AIM could map these basis functions onto a rectangular grid, where the Toeplitz property of the Green's function would be utilized, which enables the calculation of the matrix‐vector multiplication by use of the fast Fourier transform (FFT) technique. It reduces the memory requirement from O(N²) to O(N) and the operation complexity from O(N²) to O(N log N), where N is the number of unknowns. The resultant equations are then solved by the loose generalized minimal residual method (LGMRES) to accelerate iteration, which converges much faster than the conventional conjugate gradient method (CG). Furthermore, several preconditioning techniques are employed to enhance the computational efficiency of the LGMRES. Some typical microstrip circuits and microstrip antenna array are analyzed and numerical results show that the preconditioned LGMRES can converge much faster than conventional LGMRES. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

A Randomized Algorithm for Online Unit Clustering

In this paper, we consider the online version of the following problem: partition a set of input points into subsets, each enclosable by a unit ball, so as to minimize the number of subsets used. In the one-dimensional case, we show that surprisingly the naïve upper bound of 2 on the competitive ratio can be beaten: we present a new randomized 15/8-competitive online algorithm. We also provide some lower bounds and an extension to higher dimensions.     

Adaptive sliding mode-like fuzzy logic control for nonlinear systems

Sliding mode-like fuzzy logic control （SMFC） algorithm for nonlinear systems is presented in this paper. Firstly dead zone parameters of sliding mode control （SMC） are selftuned by proper adaptive laws and then combined into fuzzy logic system （FLS） to compose the opportune fuzzy logic control （FLC）, which is equivalent to the predesigned SMC controller with self-tuning parameters. Robustness and invariance to the uncertainties of the closed-loop systems are improved and chattering of the SMC is eliminated. Finally simulation results of numerical examples show that the proposed control algorithm is efficient and feasible.     

An improved clustering analyzing algorithm for image index

This paper provides a survey of current clustering analysis in image indexing at first, then the problems are solved and the new directions in the future in this domain are pointed out. In the end, it proposes a new clustering algorithm with the combination of k-means clustering algorithm, genetic algorithm and chaotic optimization, a new idea of dimensionality reduction.     

基于数据挖掘算法的网络入侵检测系统研究

回顾了当前入侵检测技术和数据挖掘技术,分析了Snort网络入侵检测系统存在的问题,重点研究了数据挖掘中的关联算法Apriori算法和聚类算法K一均值算法;在Snort入侵检测系统的基础上,增加了正常行为挖掘模块、异常检测模块和新规则生成模块,构建了基于数据挖掘技术的网络入侵检测系统模型。新模型能够有效地检测新的入侵行为,而且提高了系统的检测效率。     

基于ART2神经网络的车辆感应波形识别的方法

在智能交通系统中,无论是在交通监控领域还是在不停车收费系统方面,对车辆进行自动分类都十分重要;环形线圈车辆检测器以其良好的适应性、稳定性和高效性在车辆监测方面得到了广泛的应用,同时利用同类型或同种车辆经过环形线圈产生轮廓相似的电磁感应波形这一特点也可以进行车型识别;对波形进行预处理,以波形轮廓的抽样、量化值作为特征向量,将特征向量作为ART2神经网络的输入向量,经ART2神经网络的自动聚类最终实现车辆感应波形的聚类与识别。