Estimation of a Unique Pair of Nash Model Parameters: An Optimization Approach

2010 ◽  
Vol 24 (12) ◽  
pp. 2971-2989 ◽  
Author(s):  
Muhammad Masood Ahmad ◽  
Abdul Razzaq Ghumman ◽  
Sajjad Ahmad ◽  
Hashim Nisar Hashmi
Keyword(s):  
Model Parameters ◽  
Optimization Approach ◽  
Nash Model ◽  
Unique Pair

scholarly journals A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 126 ◽  
Author(s):  
Lina Aboulmouna ◽  
Shakti Gupta ◽  
Mano Maurya ◽  
Frank DeVilbiss ◽  
Shankar Subramaniam ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Metabolic Regulation ◽  
Metabolic Flux ◽  
Optimal Parameter ◽  
Gene Knockout ◽  
Model Parameters ◽  
Mouse Bone Marrow ◽  
Optimization Approach ◽  
Substrate Uptake ◽  
Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

scholarly journals Robust Optimization Approach Using Scenario Concepts for Artillery Firing Scheduling Under Uncertainty

2019 ◽  
Vol 9 (14) ◽  
pp. 2811
Author(s):  
Choi ◽  
Yun ◽  
Kim ◽  
Jin ◽  
Kim
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Numerical Experiments ◽  
Master Problem ◽  
Model Parameters ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Robust Solution ◽  
Reasonable Time ◽  
Scheduling Under Uncertainty

Real wars involve a considerable number of uncertainties when determining firing scheduling. This study proposes a robust optimization model that considers uncertainties in wars. In this model, parameters that are affected by enemy’s behavior and will, i.e., threats from enemy targets and threat time from enemy targets, are assumed as uncertain parameters. The robust optimization model considering these parameters is an intractable model with semi-infinite constraints. Thus, this study proposes an approach to obtain a solution by reformulating this model into a tractable problem; the approach involves developing a robust optimization model using the scenario concept and finding a solution in that model. Here, the combinations that express uncertain parameters are assumed by scenarios. This approach divides problems into master and subproblems to find a robust solution. A genetic algorithm is utilized in the master problem to overcome the complexity of global searches, thereby obtaining a solution within a reasonable time. In the subproblem, the worst scenarios for any solution are searched to find the robust solution even in cases where all scenarios have been expressed. Numerical experiments are conducted to compare robust and nominal solutions for various uncertainty levels to verify the superiority of the robust solution.

Using Reinforcement Learning to Estimate Human Joint Moments via EMG Signals or Joint Kinematics: An Alternative Solution to Musculoskeletal-Based Biomechanics

2020 ◽  
Author(s):  
Wen Wu ◽  
Kate Saul ◽  
He (Helen) Huang
Keyword(s):  
Reinforcement Learning ◽  
Correlation Coefficients ◽  
Metacarpophalangeal Joint ◽  
Joint Moment ◽  
Biomechanical Analysis ◽  
Model Parameters ◽  
Optimization Approach ◽  
Joint Moments ◽  
Moment Estimation ◽  
Policy Optimization

Abstract Reinforcement learning (RL) has potential to provide innovative solutions to existing challenges in estimating joint moments in motion analysis, such as kinematic or electromyography (EMG) noise and unknown model parameters. Here we explore feasibility of RL to assist joint moment estimation for biomechanical applications. Forearm and hand kinematics and forearm EMGs from 4 muscles during free finger and wrist movement were collected from six healthy subjects. Using the Proximal Policy Optimization approach, we trained and tested two types of RL agents that estimated joint moment based on measured kinematics or measured EMGs, respectively. To quantify the performance of RL agents, the estimated joint moment was used to drive a forward dynamic model for estimating kinematics, which were then compared with measured kinematics. The results demonstrated that both RL agents can accurately reproduce wrist and metacarpophalangeal joint motion. The correlation coefficients between estimated and measured kinematics, derived from the kinematics-driven agent and subject-specific EMG-driven agents, were 0.98±0.01 and 0.94±0.03 for the wrist, respectively, and were 0.95±0.02 and 0.84±0.06 for the metacarpophalangeal joint, respectively. In addition, a biomechanically reasonable joint moment-angle-EMG relationship (i.e. dependence of joint moment on joint angle and EMG) was predicted using only 15 seconds of collected data. In conclusion, this study serves as a proof of concept that an RL approach can assist in biomechanical analysis and human-machine interface applications by deriving joint moments from kinematic or EMG data.

scholarly journals Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

2019 ◽  
Vol 5 (1) ◽  
pp. eaat7854 ◽  
Author(s):  
Peng Wang ◽  
Ru Kong ◽  
Xiaolu Kong ◽  
Raphaël Liégeois ◽  
Csaba Orban ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Circuit Model ◽  
Model Parameters ◽  
Optimization Approach ◽  
Functional Magnetic Resonance ◽  
Cortical Hierarchy ◽  
Out Of Sample ◽  
External Stimuli ◽  
Recurrent Connections

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

Game Theoretical Approach to Model Decision Making for Merging Maneuvers at Freeway On-Ramps

2017 ◽  
Vol 2623 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Kyungwon Kang ◽  
Hesham A. Rakha
Keyword(s):  
Decision Making ◽  
Theoretical Approach ◽  
Model Parameters ◽  
Decision Making Process ◽  
Optimization Approach ◽  
Data Set ◽  
Deliberation Process ◽  
Proposed Model ◽  
Game Theoretical Approach ◽  
The Subject

Drivers of merging vehicles decide when to merge by considering surrounding vehicles in adjacent lanes in their deliberation process. Conflicts between drivers of the subject vehicles (i.e., merging vehicles) in an auxiliary lane and lag vehicles in the adjacent lane are typical near freeway on-ramps. This paper models a decision-making process for merging maneuvers that uses a game theoretical approach. The proposed model is based on the noncooperative decision making of two players, that is, drivers of the subject and lag vehicles, without consideration of advanced communication technologies. In the decision-making process, the drivers of the subject vehicles elect to accept gaps, and drivers of lag vehicles either yield or block the action of the subject vehicle. Corresponding payoff functions for two players were formulated to describe their respective maneuvers. To estimate model parameters, a bi-level optimization approach was used. The next generation simulation data set was used for model calibration and validation. The data set defined the moment the game started and was modeled as a continuous sequence of games until a decision is made. The defined merging decision-making model was then validated with an independent data set. The validation results reveal that the proposed model provides considerable prediction accuracy with correct predictions 84% of the time.

Hybrid Optimization Approach for Evaluating Photovoltaic Cell Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 1261-1264
Author(s):  
Mei Ying Ye
Keyword(s):  
Cell Model ◽  
Computation Time ◽  
Photovoltaic Cell ◽  
Evaluation Procedure ◽  
Model Parameters ◽  
Optimization Approach ◽  
Problem Space ◽  
Local Optima ◽  
Cell Parameters ◽  
Intelligent Technique

A new hybrid intelligent technique is proposed to evaluate photovoltaic cell model parameters in this paper. The intelligent technique is based on a hybrid of genetic algorithm (GA) and LevenbergMarquardt algorithm (LMA). In the proposed hybrid intelligent technique, the GA firstly searches the entire problem space to get a set of roughly estimated solutions, i.e. near-optimal solutions. Then the LMA performs a local optima search in order to carry out further optimizations. An example has been used to demonstrate the evaluation procedure in order to test the performance of the proposed approach. The results show that the proposed technique has better performance than the GA approach in terms of the objective function value, the computation time and the reconstructedI-Vcurve shape.

scholarly journals Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

2006 ◽  
Vol 7 (3) ◽  
pp. 548-565 ◽  
Author(s):  
Jasper A. Vrugt ◽  
Hoshin V. Gupta ◽  
BreanndánÓ Nualláin ◽  
Willem Bouten
Keyword(s):  
Data Assimilation ◽  
Simultaneous Optimization ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimization Approach ◽  
Streamflow Forecasting ◽  
Time Data ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Using Data

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships.

Site characterization with 3D elastic full-waveform tomography

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. R389-R400 ◽  
Author(s):  
Trung Dung Nguyen ◽  
Khiem T. Tran
Keyword(s):  
Field Data ◽  
Wave Equations ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Seismic Inversion ◽  
Waveform Analysis ◽  
Staggered Grid ◽  
Model Parameters ◽  
Optimization Approach ◽  
Full Waveform

We have developed a 3D elastic full-waveform inversion (FWI) method for geotechnical site characterization. The method is based on a solution of 3D elastic-wave equations for forward modeling to simulate wave propagation and a local optimization approach based on the adjoint-state method to update the model parameters. The staggered-grid finite-difference technique is used to solve the wave equations together with implementation of the perfectly matched layer condition for boundary truncation. Seismic wavefields are acquired from geophysical testing using sensors and sources located in uniform 2D grids on the ground surface, and they are then inverted for the extraction of 3D subsurface wave velocity structures. The capability of the presented FWI method is tested on synthetic and field data sets. The inversion results from synthetic data indicate the ability of characterizing laterally variable low- and high-velocity layers. Field experimental data were collected using 96 receivers and a propelled energy generator to induce seismic wave energy. The field data result indicates that the waveform analysis was able to delineate variable subsurface soil layers. The seismic inversion results are generally consistent with invasive standard penetration test [Formula: see text]-values, including identification of a low-velocity zone.

scholarly journals Optimization of model parameters and experimental designs with the Optimal Experimental Design Toolbox (v1.0) exemplified by sedimentation in salt marshes

2015 ◽  
Vol 8 (3) ◽  
pp. 791-804 ◽  
Author(s):  
J. Reimer ◽  
M. Schuerch ◽  
T. Slawig
Keyword(s):  
Experimental Design ◽  
Salt Marshes ◽  
Weighted Least Squares ◽  
Experimental Designs ◽  
Optimal Experimental Design ◽  
Model Parameters ◽  
Optimization Approach ◽  
Discrete Optimization Problem ◽  
Advantages And Disadvantages ◽  
Optimizing Model

Abstract. The geosciences are a highly suitable field of application for optimizing model parameters and experimental designs especially because many data are collected. In this paper, the weighted least squares estimator for optimizing model parameters is presented together with its asymptotic properties. A popular approach to optimize experimental designs called local optimal experimental designs is described together with a lesser known approach which takes into account the potential nonlinearity of the model parameters. These two approaches have been combined with two methods to solve their underlying discrete optimization problem. All presented methods were implemented in an open-source MATLAB toolbox called the Optimal Experimental Design Toolbox whose structure and application is described. In numerical experiments, the model parameters and experimental design were optimized using this toolbox. Two existing models for sediment concentration in seawater and sediment accretion on salt marshes of different complexity served as an application example. The advantages and disadvantages of these approaches were compared based on these models. Thanks to optimized experimental designs, the parameters of these models could be determined very accurately with significantly fewer measurements compared to unoptimized experimental designs. The chosen optimization approach played a minor role for the accuracy; therefore, the approach with the least computational effort is recommended.

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