This paper proposes a sensitivity analysis method for engineering parameters using interval analyses.This method substantially extends the application of interval analysis method.In this scheme,parameter intervals and...This paper proposes a sensitivity analysis method for engineering parameters using interval analyses.This method substantially extends the application of interval analysis method.In this scheme,parameter intervals and decision-making target intervals are determined using the interval analysis method.As an example,an inverse analysis method for uncertainty is presented.The intervals of unknown parameters can be obtained by sampling measured data.Even for limited measured data,robust results can also be obtained with the inverse analysis method,which can be intuitively evaluated by the uncertainty expressed in terms of an interval.For complex nonlinear problems,an iteratively optimized inverse analysis model is proposed.In a given set of loose parameter intervals,all the unknown parameter intervals that satisfy the measured information can be obtained by an iteratively optimized inverse analysis model.The influences of measured precisions and the number of parameters on the results of the inverse analysis are evaluated.Finally,the uniqueness of the interval inverse analysis method is discussed.展开更多
This paper establishes a method for identifying and locating dynamic loads in time-varying systems.The proposed method linearizes time-varying parameters within small time units and uses the Wilson-θ inverse analysis...This paper establishes a method for identifying and locating dynamic loads in time-varying systems.The proposed method linearizes time-varying parameters within small time units and uses the Wilson-θ inverse analysis method to solve modal loads of each order at each time step.It then uses an exhaustive method to determine the load position.Finally,it calculates the time history of the load.Simulation examples demonstrate how the number of measuring points and step size affect load identi-fication accuracy,verifying that this algorithm achieves good identification accuracy for loads under resonance conditions.Additionally,it explores how noise affects load position and recognition accuracy,while providing a solution.Simulation examples and experimental results demonstrate that the proposed method can identify both the time history and position of loads simultaneously with high identification accuracy.展开更多
In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous co...In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.展开更多
基金Supported by the National Natural Science Foundation of China(50978083)the Fundamental Research Funds for the Central Universities(2010B02814)
文摘This paper proposes a sensitivity analysis method for engineering parameters using interval analyses.This method substantially extends the application of interval analysis method.In this scheme,parameter intervals and decision-making target intervals are determined using the interval analysis method.As an example,an inverse analysis method for uncertainty is presented.The intervals of unknown parameters can be obtained by sampling measured data.Even for limited measured data,robust results can also be obtained with the inverse analysis method,which can be intuitively evaluated by the uncertainty expressed in terms of an interval.For complex nonlinear problems,an iteratively optimized inverse analysis model is proposed.In a given set of loose parameter intervals,all the unknown parameter intervals that satisfy the measured information can be obtained by an iteratively optimized inverse analysis model.The influences of measured precisions and the number of parameters on the results of the inverse analysis are evaluated.Finally,the uniqueness of the interval inverse analysis method is discussed.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘This paper establishes a method for identifying and locating dynamic loads in time-varying systems.The proposed method linearizes time-varying parameters within small time units and uses the Wilson-θ inverse analysis method to solve modal loads of each order at each time step.It then uses an exhaustive method to determine the load position.Finally,it calculates the time history of the load.Simulation examples demonstrate how the number of measuring points and step size affect load identi-fication accuracy,verifying that this algorithm achieves good identification accuracy for loads under resonance conditions.Additionally,it explores how noise affects load position and recognition accuracy,while providing a solution.Simulation examples and experimental results demonstrate that the proposed method can identify both the time history and position of loads simultaneously with high identification accuracy.
基金This project is supported by the National Natural Science Foundation of China
文摘In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.
