The Experts below are selected from a list of 21981 Experts worldwide ranked by ideXlab platform
D. E. Stewart - One of the best experts on this subject based on the ideXlab platform.
-
AN EFFICIENT PROCEDURE FOR COMPUTING QUASI- STATIONARY DISTRIBUTIONS OF MARKOV CHAINS WITH SPARSE TRANSITION STRUCTURE
Advances in Applied Probability, 1994Co-Authors: Philip K. Pollett, D. E. StewartAbstract:We describe a computational procedure for evaluating the quasi-stationary distributions of a continuous-time Markov chain. Our method, which is an 'Iterative Version' of Arnoldi's algorithm, is appropriate for dealing with cases where the matrix of transition rates is large and sparse, but does not exhibit a banded structure which might otherwise be usefully exploited. We illustrate the method with reference to an epidemic model and we compare the computed quasi-stationary distribution with an appropriate diffusion approximation.
-
An Efficient Procedure for Computing Quasi-Stationary Distributions of Markov Chains by Sparse Transition Structure
Advances in Applied Probability, 1994Co-Authors: Philip K. Pollett, D. E. StewartAbstract:We describe a computational procedure for evaluating the quasi-stationary distributions of a continuous-time Markov chain. Our method, which is an ‘Iterative Version' of Arnoldi's algorithm, is appropriate for dealing with cases where the matrix of transition rates is large and sparse, but does not exhibit a banded structure which might otherwise be usefully exploited. We illustrate the method with reference to an epidemic model and we compare the computed quasi-stationary distribution with an appropriate diffusion approximation.
Alexandre Mota - One of the best experts on this subject based on the ideXlab platform.
-
a sliding window solution for the on line implementation of the levenberg marquardt algorithm
Engineering Applications of Artificial Intelligence, 2006Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:The Levenberg-Marquardt algorithm is considered as the most effective one for training artificial neural networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true Iterative Version to use in on-line training. The algorithm is frequently used for off-line training in batch Versions although some attempts have been made to implement Iterative Versions. To overcome the difficulties in implementing the Iterative Version, a batch-sliding window with Early Stopping, which uses a hybrid Direct/Specialized evaluation procedure, is proposed and tested with a real system. m.
-
ICANN (2) - Using the Levenberg-marquardt for on-line training of a variant system
2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:This paper presents an application of the Levenberg-Marquardt algorithm to on-line modelling of a variant system. Because there is no Iterative Version of the Levenberg-Marquardt algorithm, a batch Version is used with a double sliding window and Early Stopping to produce models of a system whose poles change during operation. The models are used in a Internal Model Controller to control the system which is held functioning in the initial phase by a PI controller.
-
on line training of neural networks a sliding window approach for the levenberg marquardt algorithm
International Work-Conference on the Interplay Between Natural and Artificial Computation, 2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:In the Neural Network universe, the Backpropagation and the Levenberg-Marquardt are the most used algorithms, being almost consensual that the latter is the most effective one. Unfortunately for this algorithm it has not been possible to develop a true Iterative Version for on-line use due to the necessity to implement the Hessian matrix and compute the trust region. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping is proposed, which uses a hybrid Direct/Specialized evaluation procedure. The final solution is tested with a real system.
-
IWINAC (2) - On-line training of neural networks: a sliding window approach for the levenberg-marquardt algorithm
Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, 2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:In the Neural Network universe, the Backpropagation and the Levenberg-Marquardt are the most used algorithms, being almost consensual that the latter is the most effective one. Unfortunately for this algorithm it has not been possible to develop a true Iterative Version for on-line use due to the necessity to implement the Hessian matrix and compute the trust region. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping is proposed, which uses a hybrid Direct/Specialized evaluation procedure. The final solution is tested with a real system.
-
implementing the levenberg marquardt algorithm on line a sliding window approach with early stopping
IFAC Proceedings Volumes, 2004Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:Abstract The Levenberg-Marquardt algorithm is considered as the most effective one for training Artificial Neural Networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true Iterative Version to use in on-line training. The algorithm is frequently used for off-line training in batch Versions although some attempts have been made to implement Iterative Versions. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping Version, which uses a hybrid Direct/Specialized evaluation procedure is proposed and tested with a real system.
Philip K. Pollett - One of the best experts on this subject based on the ideXlab platform.
-
AN EFFICIENT PROCEDURE FOR COMPUTING QUASI- STATIONARY DISTRIBUTIONS OF MARKOV CHAINS WITH SPARSE TRANSITION STRUCTURE
Advances in Applied Probability, 1994Co-Authors: Philip K. Pollett, D. E. StewartAbstract:We describe a computational procedure for evaluating the quasi-stationary distributions of a continuous-time Markov chain. Our method, which is an 'Iterative Version' of Arnoldi's algorithm, is appropriate for dealing with cases where the matrix of transition rates is large and sparse, but does not exhibit a banded structure which might otherwise be usefully exploited. We illustrate the method with reference to an epidemic model and we compare the computed quasi-stationary distribution with an appropriate diffusion approximation.
-
An Efficient Procedure for Computing Quasi-Stationary Distributions of Markov Chains by Sparse Transition Structure
Advances in Applied Probability, 1994Co-Authors: Philip K. Pollett, D. E. StewartAbstract:We describe a computational procedure for evaluating the quasi-stationary distributions of a continuous-time Markov chain. Our method, which is an ‘Iterative Version' of Arnoldi's algorithm, is appropriate for dealing with cases where the matrix of transition rates is large and sparse, but does not exhibit a banded structure which might otherwise be usefully exploited. We illustrate the method with reference to an epidemic model and we compare the computed quasi-stationary distribution with an appropriate diffusion approximation.
Michael Ghil - One of the best experts on this subject based on the ideXlab platform.
-
Oscillatory modes of extended Nile River records (A.D. 622–1922)
Geophysical Research Letters, 2005Co-Authors: Dmitri Kondrashov, Yizhak Feliks, Michael GhilAbstract:[1] The historical records of the low- and high-water levels of the Nile River are among the longest climatic records that have near-annual resolution. There are few gaps in the first part of the records (A.D. 622–1470) and larger gaps later (A.D. 1471–1922). We apply advanced spectral methods, Singular-Spectrum Analysis (SSA) and the Multi-Taper Method (MTM), to fill the gaps and to locate interannual and interdecadal periodicities. The gap filling uses a novel, Iterative Version of SSA. Our analysis reveals several statistically significant features of the records: a nonlinear, data-adaptive trend that includes a 256-year cycle, a quasi-quadriennial (4.2-year) and a quasi-biennial (2.2-year) mode, as well as additional periodicities of 64, 19, 12, and, most strikingly, 7 years. The quasi-quadriennial and quasi-biennial modes support the long-established connection between the Nile River discharge and the El-Nino/Southern Oscillation (ENSO) phenomenon in the Indo-Pacific Ocean. The longest periods might be of astronomical origin. The 7-year periodicity, possibly related to the biblical cycle of lean and fat years, seems to be due to North Atlantic influences.
-
oscillatory modes of extended nile river records a d 622 1922
Geophysical Research Letters, 2005Co-Authors: Dmitri Kondrashov, Yizhak Feliks, Michael GhilAbstract:[1] The historical records of the low- and high-water levels of the Nile River are among the longest climatic records that have near-annual resolution. There are few gaps in the first part of the records (A.D. 622–1470) and larger gaps later (A.D. 1471–1922). We apply advanced spectral methods, Singular-Spectrum Analysis (SSA) and the Multi-Taper Method (MTM), to fill the gaps and to locate interannual and interdecadal periodicities. The gap filling uses a novel, Iterative Version of SSA. Our analysis reveals several statistically significant features of the records: a nonlinear, data-adaptive trend that includes a 256-year cycle, a quasi-quadriennial (4.2-year) and a quasi-biennial (2.2-year) mode, as well as additional periodicities of 64, 19, 12, and, most strikingly, 7 years. The quasi-quadriennial and quasi-biennial modes support the long-established connection between the Nile River discharge and the El-Nino/Southern Oscillation (ENSO) phenomenon in the Indo-Pacific Ocean. The longest periods might be of astronomical origin. The 7-year periodicity, possibly related to the biblical cycle of lean and fat years, seems to be due to North Atlantic influences.
Fernando Morgado Dias - One of the best experts on this subject based on the ideXlab platform.
-
a sliding window solution for the on line implementation of the levenberg marquardt algorithm
Engineering Applications of Artificial Intelligence, 2006Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:The Levenberg-Marquardt algorithm is considered as the most effective one for training artificial neural networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true Iterative Version to use in on-line training. The algorithm is frequently used for off-line training in batch Versions although some attempts have been made to implement Iterative Versions. To overcome the difficulties in implementing the Iterative Version, a batch-sliding window with Early Stopping, which uses a hybrid Direct/Specialized evaluation procedure, is proposed and tested with a real system. m.
-
ICANN (2) - Using the Levenberg-marquardt for on-line training of a variant system
2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:This paper presents an application of the Levenberg-Marquardt algorithm to on-line modelling of a variant system. Because there is no Iterative Version of the Levenberg-Marquardt algorithm, a batch Version is used with a double sliding window and Early Stopping to produce models of a system whose poles change during operation. The models are used in a Internal Model Controller to control the system which is held functioning in the initial phase by a PI controller.
-
on line training of neural networks a sliding window approach for the levenberg marquardt algorithm
International Work-Conference on the Interplay Between Natural and Artificial Computation, 2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:In the Neural Network universe, the Backpropagation and the Levenberg-Marquardt are the most used algorithms, being almost consensual that the latter is the most effective one. Unfortunately for this algorithm it has not been possible to develop a true Iterative Version for on-line use due to the necessity to implement the Hessian matrix and compute the trust region. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping is proposed, which uses a hybrid Direct/Specialized evaluation procedure. The final solution is tested with a real system.
-
IWINAC (2) - On-line training of neural networks: a sliding window approach for the levenberg-marquardt algorithm
Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, 2005Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:In the Neural Network universe, the Backpropagation and the Levenberg-Marquardt are the most used algorithms, being almost consensual that the latter is the most effective one. Unfortunately for this algorithm it has not been possible to develop a true Iterative Version for on-line use due to the necessity to implement the Hessian matrix and compute the trust region. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping is proposed, which uses a hybrid Direct/Specialized evaluation procedure. The final solution is tested with a real system.
-
implementing the levenberg marquardt algorithm on line a sliding window approach with early stopping
IFAC Proceedings Volumes, 2004Co-Authors: Fernando Morgado Dias, Ana Antunes, Jose Vieira, Alexandre MotaAbstract:Abstract The Levenberg-Marquardt algorithm is considered as the most effective one for training Artificial Neural Networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true Iterative Version to use in on-line training. The algorithm is frequently used for off-line training in batch Versions although some attempts have been made to implement Iterative Versions. To overcome the difficulties in implementing the Iterative Version, a batch sliding window with Early Stopping Version, which uses a hybrid Direct/Specialized evaluation procedure is proposed and tested with a real system.
