Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment of Cardiac Control Complexity Superior to the Linear Model-Based One?

2017 ◽  
Vol 64 (6) ◽  
pp. 1287-1296 ◽  
Author(s):  
Alberto Porta ◽  
Beatrice De Maria ◽  
Vlasta Bari ◽  
Andrea Marchi ◽  
Luca Faes
Keyword(s):  
Linear Model ◽  
Nonlinear Model ◽  
Conditional Entropy ◽  
Model Based ◽  
Model Free ◽  
Cardiac Control

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

Nonlinear Model Reduction for Dynamic and Automotive System Descriptions

2002 ◽  
Vol 124 (4) ◽  
pp. 637-647 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free ◽  
Vehicle Suspension System

The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which may offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Thus, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined. The controllability gramian represents the energy needed to transport the system between states, while the observability gramian denotes the output energy transmitted. These gramians are then balanced and select system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point, and then a balanced realization linear reduction strategy applied to reduce the linearized model. To demonstrate the functionality of each model reduction strategy, a vehicle suspension system and exhaust gas recirculation valve are investigated, and respective transient performances are compared.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Model-Based and Model-Free Control of DC-DC Converters with High-Order Dynamics and Limited Measurements

2020 ◽  
pp. 1-1
Author(s):  
Javier Loranca ◽  
Jonathan Carlos Mayo Maldonado ◽  
Gerardo Escobar ◽  
Carlos Villarreal-Hernandez ◽  
Thabiso Maupong ◽  
...  
Keyword(s):  
High Order ◽  
Model Based ◽  
Model Free ◽  
Model Free Control
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