The Experts below are selected from a list of 246039 Experts worldwide ranked by ideXlab platform
Anoop K. Mathur - One of the best experts on this subject based on the ideXlab platform.
-
Parameter Estimation for Process Control with Neural Networks
International Journal of Approximate Reasoning, 1992Co-Authors: Tariq Samad, Anoop K. MathurAbstract:Abstract Neural networks are applied to the problem of Parameter Estimation for process systems. Neural network Parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated by using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of Parameter Estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results are detailed. Some results of other Parameter Estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.
-
Parameter Estimation for process control with neural networks
1991Co-Authors: Tariq Samad, Anoop K. MathurAbstract:An application of neural networks to the problem of Parameter Estimation for process systems is described. Neural network Parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of Parameter Estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results detailed. Some results of other Parameter Estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.
G. Padmanabhan - One of the best experts on this subject based on the ideXlab platform.
-
Parameter Estimation of Linear and Nonlinear Muskingum Models
Journal of Water Resources Planning and Management, 1993Co-Authors: Jaewan Yoon, G. PadmanabhanAbstract:The conventional method of Parameter Estimation of Muskingum flood‐routing models is based on a graphical bivariate curve‐fitting procedure. The subjectivity of this procedure may lead to problems of reproducibility. A software, called MUPERS (Muskingum Parameter Estimation and flood routing system) was developed for estimating the Parameters of a linear or nonlinear Muskingum model of choice and for routing the flood through the river reach. Although 11 methods of Parameter Estimation‐eight for the linear, two for the piecewise linear, and one for the nonlinear model‐are included in the software, only six of them are discussed in this note. A linearity check of data is used to choose between linear and nonlinear forms of Muskingum models. An example is illustrated using MUPERS, which is menu‐driven and user‐friendly, with graphics capabilities. A comparison of the performance of the different Parameter‐Estimation methods is also discussed.
K. Shah - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear Parameter Estimation of excitation systems
IEEE Transactions on Power Systems, 2000Co-Authors: R. Bhaskar, Mariesa L. Crow, E. Ludwig, K.t. Erickson, K. ShahAbstract:This paper details the nonlinear Parameter Estimation process of an IEEE AC1A type exciter using time-domain system identification techniques. This paper discusses nonlinear Parameter Estimation techniques, systematic and random noise mitigation strategies, and system validation. This study establishes a strong basis for excitation system Parameter Estimation.
Wei Cui - One of the best experts on this subject based on the ideXlab platform.
-
Multi-objective optimization in quantum Parameter Estimation
Science China Physics Mechanics & Astronomy, 2018Co-Authors: Beili Gong, Wei CuiAbstract:We investigate quantum Parameter Estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown Parameter. We show that while the precision of Parameter Estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the Parameter Estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified $\varepsilon$-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum Parameter Estimation.
-
Multi-objective Optimization in Quantum Parameter Estimation
arXiv: Quantum Physics, 2017Co-Authors: Beili Gong, Wei CuiAbstract:We study the quantum Parameter Estimation based on the linear and Kerr-type nonlinear controls in the Hamiltonian of the system. We show that when we enhance the Parameter Estimation precision, it usually induces significant backaction on the system itself. Moreover, we propose a multi-objective model which aims at optimizing the two conflicting objectives: (1) maximizing the Fisher information that improves the Parameter Estimation precision; (2) minimizing the backaction on the system that maintains the fidelity. Finally, simulations of a simplified $\varepsilon$-constrained model demonstrate the feasibility of the Hamiltonian control in the quantum Parameter Estimation.
Tariq Samad - One of the best experts on this subject based on the ideXlab platform.
-
Parameter Estimation for Process Control with Neural Networks
International Journal of Approximate Reasoning, 1992Co-Authors: Tariq Samad, Anoop K. MathurAbstract:Abstract Neural networks are applied to the problem of Parameter Estimation for process systems. Neural network Parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated by using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of Parameter Estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results are detailed. Some results of other Parameter Estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.
-
Parameter Estimation for process control with neural networks
1991Co-Authors: Tariq Samad, Anoop K. MathurAbstract:An application of neural networks to the problem of Parameter Estimation for process systems is described. Neural network Parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of Parameter Estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results detailed. Some results of other Parameter Estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.
