The Experts below are selected from a list of 78441 Experts worldwide ranked by ideXlab platform
Arvid Naess - One of the best experts on this subject based on the ideXlab platform.
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response Probability Density Functions of strongly non linear systems by the path integration method
International Journal of Non-linear Mechanics, 2006Co-Authors: D V Iourtchenko, Arvid NaessAbstract:Abstract The paper discusses challenges in numerical analysis and numerical/analytical results for strongly non-linear systems—systems with “signum”-type non-linearities. Such non-linearities are implemented for instantaneous variations of the systems’ parameters, to reduce their mean energy response when subjected to random excitations. Numerical results for displacement and velocity response Probability Density Functions (PDFs), energy response PDFs and various order moments are obtained by the path integration technique. Attention is also given to evaluation of mean upcrossing rate, related to the system's half period, via Rice's formula informally applied to discontinuous response PDFs.
R. Fitzgerald - One of the best experts on this subject based on the ideXlab platform.
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charged particle thermonuclear reaction rates ii tables and graphs of reaction rates and Probability Density Functions
Nuclear Physics, 2010Co-Authors: C. Iliadis, R. Longland, A. Coc, Arthur E Champagne, R. FitzgeraldAbstract:Abstract Numerical values of charged-particle thermonuclear reaction rates for nuclei in the A = 14 to 40 region are tabulated. The results are obtained using a method, based on Monte Carlo techniques, that has been described in the preceding paper of this issue (Paper I). We present a low rate, median rate and high rate which correspond to the 0.16, 0.50 and 0.84 quantiles, respectively, of the cumulative reaction rate distribution. The meaning of these quantities is in general different from the commonly reported, but statistically meaningless expressions, “lower limit”, “nominal value” and “upper limit” of the total reaction rate. In addition, we approximate the Monte Carlo Probability Density function of the total reaction rate by a lognormal distribution and tabulate the lognormal parameters μ and σ at each temperature. We also provide a quantitative measure (Anderson–Darling test statistic) for the reliability of the lognormal approximation. The user can implement the approximate lognormal reaction rate Probability Density Functions directly in a stellar model code for studies of stellar energy generation and nucleosynthesis. For each reaction, the Monte Carlo reaction rate Probability Density Functions, together with their lognormal approximations, are displayed graphically for selected temperatures in order to provide a visual impression. Our new reaction rates are appropriate for bare nuclei in the laboratory. The nuclear physics input used to derive our reaction rates is presented in the subsequent paper of this issue (Paper III). In the fourth paper of this issue (Paper IV) we compare our new reaction rates to previous results.
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Charged-Particle Thermonuclear Reaction Rates: II. Tables and Graphs of Reaction Rates and Probability Density Functions
Nuclear Physics A, 2010Co-Authors: C. Iliadis, R. Longland, A. Champagne, A. Coc, R. FitzgeraldAbstract:Numerical values of charged-particle thermonuclear reaction rates for nuclei in the A=14 to 40 region are tabulated. The results are obtained using a method, based on Monte Carlo techniques, that has been described in the preceding paper of this series (Paper I). We present a low rate, median rate and high rate which correspond to the 0.16, 0.50 and 0.84 quantiles, respectively, of the cumulative reaction rate distribution. The meaning of these quantities is in general different from the commonly reported, but statistically meaningless expressions, "lower limit", "nominal value" and "upper limit" of the total reaction rate. In addition, we approximate the Monte Carlo Probability Density function of the total reaction rate by a lognormal distribution and tabulate the lognormal parameters {\mu} and {\sigma} at each temperature. We also provide a quantitative measure (Anderson-Darling test statistic) for the reliability of the lognormal approximation. The user can implement the approximate lognormal reaction rate Probability Density Functions directly in a stellar model code for studies of stellar energy generation and nucleosynthesis. For each reaction, the Monte Carlo reaction rate Probability Density Functions, together with their lognormal approximations, are displayed graphically for selected temperatures in order to provide a visual impression. Our new reaction rates are appropriate for bare nuclei in the laboratory. The nuclear physics input used to derive our reaction rates is presented in the subsequent paper of this series (Paper III). In the fourth paper of this series (Paper IV) we compare our new reaction rates to previous results.
C. Iliadis - One of the best experts on this subject based on the ideXlab platform.
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charged particle thermonuclear reaction rates ii tables and graphs of reaction rates and Probability Density Functions
Nuclear Physics, 2010Co-Authors: C. Iliadis, R. Longland, A. Coc, Arthur E Champagne, R. FitzgeraldAbstract:Abstract Numerical values of charged-particle thermonuclear reaction rates for nuclei in the A = 14 to 40 region are tabulated. The results are obtained using a method, based on Monte Carlo techniques, that has been described in the preceding paper of this issue (Paper I). We present a low rate, median rate and high rate which correspond to the 0.16, 0.50 and 0.84 quantiles, respectively, of the cumulative reaction rate distribution. The meaning of these quantities is in general different from the commonly reported, but statistically meaningless expressions, “lower limit”, “nominal value” and “upper limit” of the total reaction rate. In addition, we approximate the Monte Carlo Probability Density function of the total reaction rate by a lognormal distribution and tabulate the lognormal parameters μ and σ at each temperature. We also provide a quantitative measure (Anderson–Darling test statistic) for the reliability of the lognormal approximation. The user can implement the approximate lognormal reaction rate Probability Density Functions directly in a stellar model code for studies of stellar energy generation and nucleosynthesis. For each reaction, the Monte Carlo reaction rate Probability Density Functions, together with their lognormal approximations, are displayed graphically for selected temperatures in order to provide a visual impression. Our new reaction rates are appropriate for bare nuclei in the laboratory. The nuclear physics input used to derive our reaction rates is presented in the subsequent paper of this issue (Paper III). In the fourth paper of this issue (Paper IV) we compare our new reaction rates to previous results.
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Charged-Particle Thermonuclear Reaction Rates: II. Tables and Graphs of Reaction Rates and Probability Density Functions
Nuclear Physics A, 2010Co-Authors: C. Iliadis, R. Longland, A. Champagne, A. Coc, R. FitzgeraldAbstract:Numerical values of charged-particle thermonuclear reaction rates for nuclei in the A=14 to 40 region are tabulated. The results are obtained using a method, based on Monte Carlo techniques, that has been described in the preceding paper of this series (Paper I). We present a low rate, median rate and high rate which correspond to the 0.16, 0.50 and 0.84 quantiles, respectively, of the cumulative reaction rate distribution. The meaning of these quantities is in general different from the commonly reported, but statistically meaningless expressions, "lower limit", "nominal value" and "upper limit" of the total reaction rate. In addition, we approximate the Monte Carlo Probability Density function of the total reaction rate by a lognormal distribution and tabulate the lognormal parameters {\mu} and {\sigma} at each temperature. We also provide a quantitative measure (Anderson-Darling test statistic) for the reliability of the lognormal approximation. The user can implement the approximate lognormal reaction rate Probability Density Functions directly in a stellar model code for studies of stellar energy generation and nucleosynthesis. For each reaction, the Monte Carlo reaction rate Probability Density Functions, together with their lognormal approximations, are displayed graphically for selected temperatures in order to provide a visual impression. Our new reaction rates are appropriate for bare nuclei in the laboratory. The nuclear physics input used to derive our reaction rates is presented in the subsequent paper of this series (Paper III). In the fourth paper of this series (Paper IV) we compare our new reaction rates to previous results.
Hong Wang - One of the best experts on this subject based on the ideXlab platform.
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Estimating unknown Probability Density Functions for random parameters of stochastic ARMAX systems
IFAC Proceedings Volumes, 2003Co-Authors: Hong Wang, Yongji WangAbstract:Abstract Different from existing parameter estimation algorithms where the values of parameters are required to be estimated, this paper presents a new method to estimate the unknown Probability Density Functions of random parameters for non-Gaussian dynamic stochastic systems. The System is represted by an ARMAX model, where the parameters and the system noise term are random processes that are characterized by their unknown Probability Density Functions. Under the assumption that each random parameter and the noise term are independent and are identically distributed sequece, a simple mathematical relationship is established between the measured output Probability Density function of the system and the unknown Probability Density Functions of the random parameters and noise term. The mement generating function in Probability theory has been used to transfer the multiple convolution integration into a simple algebraic operation. An identification algorithm is then established that estimates these unknown Probability Density Functions of the parameters and the noise term by using the measured output Probability Density Functions and the system input.
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A rational spline model approximation and control of output Probability Density Functions for dynamic stochastic systems
Transactions of the Institute of Measurement and Control, 2003Co-Authors: Hong Wang, Hong YueAbstract:This paper presents a new method to model and control the shape of the output Probability Density Functions for dynamic stochastic systems subjected to arbitrary bounded random input. A new rational model is proposed to approximate the output Probability Density function of the system. This is then followed by the design of a novel nonlinear controller, which guarantees the monotonic decreasing of the functional norm of the difference between the measured Probability Density function and its target distribution. This leads to a desired tracking performance for the output Probability Density function. A simple example is utilized to demonstrate the use of the proposed modelling and control algorithm and encouraging results have been obtained.
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Robust control of the output Probability Density Functions for multivariable stochastic systems with guaranteed stability
IEEE Transactions on Automatic Control, 1999Co-Authors: Hong WangAbstract:Presents two robust solutions to the control of the output Probability Density function for general multi-input and multi-output stochastic systems. The control inputs of the system appear as a set of variables in the Probability Density Functions of the system output, and the signal available to the controller is the measured Probability Density function of the system output. A type of dynamic Probability Density model is formulated by using a B-spline neural network with all its weights dynamically related to the control input. It has been shown that the so-formed robust control algorithms can control the shape of the output Probability Density function and can guaranteed the closed-loop stability when the system is subjected to a bounded unknown input. An illustrative example is included to demonstrate the use of the developed control algorithms, and desired results have been obtained.
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Control of the Output Probability Density Functions for a Class of Nonlinear Stochastic Systems
IFAC Proceedings Volumes, 1998Co-Authors: Hong WangAbstract:Abstract Following the recent developments on the modelling and control of the output Probability Density Functions for linear stochastic systems (Wang, 1997, 1998), this paper presents an extented solution to the control of the Probability Density function for the output of a class of nonlinear stochastic systems. This is based on the fact that there exist many control systems where the requirements are set to control the shape of the Probability Density function of the system output, rather than the actual values of the system output. At first, the representation of dynamic model is discussed. This is then followed by the construction of a nonlinear control algorithm. An application to a papermaking process has been made and desired results have been obtained.
D V Iourtchenko - One of the best experts on this subject based on the ideXlab platform.
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response Probability Density Functions of strongly non linear systems by the path integration method
International Journal of Non-linear Mechanics, 2006Co-Authors: D V Iourtchenko, Arvid NaessAbstract:Abstract The paper discusses challenges in numerical analysis and numerical/analytical results for strongly non-linear systems—systems with “signum”-type non-linearities. Such non-linearities are implemented for instantaneous variations of the systems’ parameters, to reduce their mean energy response when subjected to random excitations. Numerical results for displacement and velocity response Probability Density Functions (PDFs), energy response PDFs and various order moments are obtained by the path integration technique. Attention is also given to evaluation of mean upcrossing rate, related to the system's half period, via Rice's formula informally applied to discontinuous response PDFs.
