Due to their complex structure,2-D models are challenging to work with;additionally,simulation,analysis,design,and control get increasingly difficult as the order of the model grows.Moreover,in particular time interva...Due to their complex structure,2-D models are challenging to work with;additionally,simulation,analysis,design,and control get increasingly difficult as the order of the model grows.Moreover,in particular time intervals,Gawronski and Juang’s time-limited model reduction schemes produce an unstable reduced-order model for the 2-D and 1-D models.Researchers revealed some stability preservation solutions to address this key flaw which ensure the stability of 1-D reduced-order systems;nevertheless,these strategies result in large approximation errors.However,to the best of the authors’knowledge,there is no literature available for the stability preserving time-limited-interval Gramian-based model reduction framework for the 2-D discrete-time systems.In this article,2-D models are decomposed into two separate sub-models(i.e.,two cascaded 1-D models)using the condition of minimal rank-decomposition.Model reduction procedures are conducted on these obtained two 1-D sub-models using limited-time Gramian.The suggested methodology works for both 2-D and 1-D models.Moreover,the suggested methodology gives the stability of the reduced model as well as a priori error-bound expressions for the 2-D and 1-D models.Numerical results and comparisons between existing and suggested methodologies are provided to demonstrate the effectiveness of the suggested methodology.展开更多
The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary...The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary and sufficient condition for the equivalence. Therefore, testing for lack-of-fit in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption. A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis. Furthermore, we discuss the power of the test and the choice of the weight function involved in the test statistic.展开更多
针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法...针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。展开更多
文摘Due to their complex structure,2-D models are challenging to work with;additionally,simulation,analysis,design,and control get increasingly difficult as the order of the model grows.Moreover,in particular time intervals,Gawronski and Juang’s time-limited model reduction schemes produce an unstable reduced-order model for the 2-D and 1-D models.Researchers revealed some stability preservation solutions to address this key flaw which ensure the stability of 1-D reduced-order systems;nevertheless,these strategies result in large approximation errors.However,to the best of the authors’knowledge,there is no literature available for the stability preserving time-limited-interval Gramian-based model reduction framework for the 2-D discrete-time systems.In this article,2-D models are decomposed into two separate sub-models(i.e.,two cascaded 1-D models)using the condition of minimal rank-decomposition.Model reduction procedures are conducted on these obtained two 1-D sub-models using limited-time Gramian.The suggested methodology works for both 2-D and 1-D models.Moreover,the suggested methodology gives the stability of the reduced model as well as a priori error-bound expressions for the 2-D and 1-D models.Numerical results and comparisons between existing and suggested methodologies are provided to demonstrate the effectiveness of the suggested methodology.
文摘The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary and sufficient condition for the equivalence. Therefore, testing for lack-of-fit in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption. A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis. Furthermore, we discuss the power of the test and the choice of the weight function involved in the test statistic.
文摘针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。
