An adaptive blind support vector machine equalizer（ABSVME） is presented in this paper.The method is based upon least square support vector machine（LSSVM）,and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.
针对在有限样本条件下恒模算法无法保证代价函数的经验风险与期望风险收敛到一致的问题,提出了一种基于结构风险最小化(SRM)准则的恒模盲均衡器(Structural risk minumum-contant model blind equalier,SRM-CMBE)。该方法利用信号的恒模特性,在高维特征空间中以SRM为准则构造代价函数,采用核方法实现计算,并结合Kumar快速算法和静态迭代学习算法在线跟踪信道,能够在小样本条件下有效保证代价函数的经验风险收敛到期望风险。通过仿真实验,并与标准恒模盲均衡器(Constant model blind equalizer,CMBE)和修正的恒模盲均衡器(Modified-constant model blind equalizer,M-CMBE)进行比较,结果证明该方法的非线性均衡性能最佳。
The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache （DSm） model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance pro- portionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary （half-） open intervals, （half-） closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.
