基于支持向量机的传感器非线性误差校正

提出了一种用支持向量机校正传感器非线性误差的原理和方法。该算法只依据样本就可以正确辩识传感器逆模型特征,而不需关于逆模型函数形式的任何先验知识,并将原问题转化为一个凸二次优化问题,能够保证找到的极值解就是全局最优解,具有较好的泛化能力。通过对电容式湿敏传感器误差校正的应用表明:该算法可取得较好的效果。     

一类非线性奇摄动方程的周期

探讨了方程εy' f(t,y,y')y=0的周期性,给出了周期解存在的一些条件,并进行了实例分析.     

An iterative Monte Carlo method for nonconjugate Bayesian analysis

The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However, the predominance of experience to date resides in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored approximate rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.     

极限情形非线性椭圆方程解的某些性质

考虑非线性椭圆方程当右端项f（x）属于适当的Lorentz空间，研究了方程的弱解u∈Wl,p（G）的可积性属性。     

非线性微分-积分方程组解的存在惟一性

利用最大值定理、Picard存在惟一性定理和Leray-Schauder不动点定理，证明了一个几何模型为平面保面积曲率流的非线性耦合微分一积分方程组的解的存在惟一性。     

分析非线性电路的广义变频矩阵法

根据非线性电路的频域延拓交调波平衡原理和大信号/小信号分析原理,提出了广义变频矩阵法,可有效地分析激励为 n 频大信号外加单频小信号的稳态非线性电路。     

中国高耗能行业能源消费的贝叶斯非对称影响效应研究

构建双机制的非线性FAVAR模型,从贝叶斯角度出发,研究中国高耗能行业能源消费对宏观经济以及环境污染因素的影响效应。利用中国高耗能行业的能源消费以及废气排放数据,结合贝叶斯方法进行非线性FAVAR模型的参数估计,结果发现中国高耗能行业的能源消费增加和减少对国内生产总值、外商直接投资等宏观经济因素以及主要环境污染因素都具有非对称影响效应。但是,高耗能能源消费增加或减少的冲击一般都是短期效应,不具有长期影响效应。     

多变量仿射非线性系统的可逆性秩判据

系统的可逆性判别是非线性控制的逆系统方法的关键,为探索可逆性分析的新途径,该文将系统可逆的秩检验法引入到多变量仿射非线性系统中,其实质是将系统的可逆性判定转化为对系统的输出函数及其导数所构成的雅可比矩阵的秩条件分析。文中给出了仿射非线性系统可逆的秩判据定理与证明过程,提出了一种具体的求逆算法,最后,举例对算法进行了验证,通过与微分几何法和逆系统方法的比较说明了秩判据法的有效性。     

Bayesian Measures of the Minimum Detectable Concentration of an Immunoassay

The minimum detectable concentration (MDC) is one of the most important properties of an assay. It is a statement about the smallest physical quantity an assay can reliably measure, and is used in assay design and quality control assessments. A plethora of measures of the MDC have been reported in a widely scattered literature. Many of these were developed at a time when accuracy and relevance had to be sacrificed for computational feasibility. This paper identifies limitations of existing measures and demonstrates how Bayesian inference may be used to overcome these limitations. Several new measures of the MDC are developed. These are conceptually simpler than existing measures, and are free of analytical approximations. The recent advances in Bayesian computation make them efficient to evaluate. A procedure developed in this paper measures the difference in the quality of two assays and shows that the new Bayesian measures perform better than existing measures.     

An R2criterion based on optimal predictors

The predictor that minimizes mean-squared prediction error is used to derive a goodness-of-fit measure that offers an asymptotically valid model selection criterion for a wide variety of regression models. In particular, a new goodness-of-fit criterion (cr²) is proposed for censored or otherwise limited dependent variables. The new goodness-of-fit measure is then applied to the analysis of duration.