The Experts below are selected from a list of 97218 Experts worldwide ranked by ideXlab platform
C. Philip L. Chen - One of the best experts on this subject based on the ideXlab platform.
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Term Selection for a Class of Separable Nonlinear Models
IEEE Transactions on Neural Networks and Learning Systems, 2020Co-Authors: Guang-yong Chen, Long Chen, C. Philip L. ChenAbstract:In this paper, we consider the term selection problem for a class of separable Nonlinear Models. The strategy is a two-step process in which the Nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods.
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Modified Gram–Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models
IEEE Transactions on Neural Networks and Learning Systems, 2019Co-Authors: Guang-yong Chen, Feng Ding, C. Philip L. ChenAbstract:Separable Nonlinear Models are very common in various research fields, such as machine learning and system identification. The variable projection (VP) approach is efficient for the optimization of such Models. In this paper, we study various VP algorithms based on different matrix decompositions. Compared with the previous method, we use the analytical expression of the Jacobian matrix instead of finite differences. This improves the efficiency of the VP algorithms. In particular, based on the modified Gram-Schmidt (MGS) method, a more robust implementation of the VP algorithm is introduced for separable Nonlinear least-squares problems. In numerical experiments, we compare the performance of five different implementations of the VP algorithm. Numerical results show the efficiency and robustness of the proposed MGS method-based VP algorithm.
Guang-yong Chen - One of the best experts on this subject based on the ideXlab platform.
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Term Selection for a Class of Separable Nonlinear Models
IEEE Transactions on Neural Networks and Learning Systems, 2020Co-Authors: Guang-yong Chen, Long Chen, C. Philip L. ChenAbstract:In this paper, we consider the term selection problem for a class of separable Nonlinear Models. The strategy is a two-step process in which the Nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods.
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Modified Gram–Schmidt Method-Based Variable Projection Algorithm for Separable Nonlinear Models
IEEE Transactions on Neural Networks and Learning Systems, 2019Co-Authors: Guang-yong Chen, Feng Ding, C. Philip L. ChenAbstract:Separable Nonlinear Models are very common in various research fields, such as machine learning and system identification. The variable projection (VP) approach is efficient for the optimization of such Models. In this paper, we study various VP algorithms based on different matrix decompositions. Compared with the previous method, we use the analytical expression of the Jacobian matrix instead of finite differences. This improves the efficiency of the VP algorithms. In particular, based on the modified Gram-Schmidt (MGS) method, a more robust implementation of the VP algorithm is introduced for separable Nonlinear least-squares problems. In numerical experiments, we compare the performance of five different implementations of the VP algorithm. Numerical results show the efficiency and robustness of the proposed MGS method-based VP algorithm.
Gaetano Valenza - One of the best experts on this subject based on the ideXlab platform.
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Predicting seizures in untreated temporal lobe epilepsy using point-process Nonlinear Models of heartbeat dynamics
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, A. Romigi, F. Placidi, F. Izzi, M. Albanese, M. G. Marciani, A. Duggento, M. GuerrisiAbstract:Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, Nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process Nonlinear Models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, Nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.
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Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics
IEEE Transactions on Signal Processing, 2013Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo BarbieriAbstract:In the last decades, mathematical modeling and signal processing techniques have played an important role in the study of cardiovascular control physiology and heartbeat Nonlinear dynamics. In particular, Nonlinear Models have been devised for the assessment of the cardiovascular system by accounting for short-memory second-order Nonlinearities. In this paper, we introduce a novel inverse Gaussian point process model with Laguerre expansion of the Nonlinear Volterra kernels. Within the model, the second-order Nonlinearities also account for the long-term information given by the past events of the nonstationary non-Gaussian time series. In addition, the mathematical link to an equivalent cubic input-output Wiener-Volterra model allows for a novel instantaneous estimation of the dynamic spectrum, bispectrum and trispectrum of the considered inter-event intervals. The proposed framework is tested with synthetic simulations and two experimental heartbeat interval datasets. Applications on further heterogeneous datasets such as milling inserts, neural spikes, gait from short walks, and geyser geologic events are also reported. Results show that our model improves on previously developed Models and, at the same time, it is able to provide a novel instantaneous characterization and tracking of the inherent Nonlinearity of heartbeat dynamics.
Riccardo Barbieri - One of the best experts on this subject based on the ideXlab platform.
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Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics
IEEE Transactions on Signal Processing, 2013Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo BarbieriAbstract:In the last decades, mathematical modeling and signal processing techniques have played an important role in the study of cardiovascular control physiology and heartbeat Nonlinear dynamics. In particular, Nonlinear Models have been devised for the assessment of the cardiovascular system by accounting for short-memory second-order Nonlinearities. In this paper, we introduce a novel inverse Gaussian point process model with Laguerre expansion of the Nonlinear Volterra kernels. Within the model, the second-order Nonlinearities also account for the long-term information given by the past events of the nonstationary non-Gaussian time series. In addition, the mathematical link to an equivalent cubic input-output Wiener-Volterra model allows for a novel instantaneous estimation of the dynamic spectrum, bispectrum and trispectrum of the considered inter-event intervals. The proposed framework is tested with synthetic simulations and two experimental heartbeat interval datasets. Applications on further heterogeneous datasets such as milling inserts, neural spikes, gait from short walks, and geyser geologic events are also reported. Results show that our model improves on previously developed Models and, at the same time, it is able to provide a novel instantaneous characterization and tracking of the inherent Nonlinearity of heartbeat dynamics.
Andrea Saltelli - One of the best experts on this subject based on the ideXlab platform.
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importance measures in global sensitivity analysis of Nonlinear Models
Reliability Engineering & System Safety, 1996Co-Authors: Toshimitsu Homma, Andrea SaltelliAbstract:Abstract The present paper deals with a new method of global sensitivity analysis of Nonlinear Models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol'. Given that Sobol' treatment of the measure of importance is the most general, his formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novelty of this study is the introduction of the ‘total effect’ parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer Models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost.
