Finding Optimal Model Parameters by Discrete Grid Search

2007 ◽  
pp. 120-127 ◽  
Author(s):  
Álvaro Barbero Jiménez ◽  
Jorge López Lázaro ◽  
José R. Dorronsoro
Keyword(s):  
Model Parameters ◽  
Grid Search ◽  
Optimal Model ◽  
Discrete Grid

Finding optimal model parameters by deterministic and annealed focused grid search

2009 ◽  
Vol 72 (13-15) ◽  
pp. 2824-2832 ◽  
Author(s):  
Álvaro Barbero Jiménez ◽  
Jorge López Lázaro ◽  
José R. Dorronsoro
Keyword(s):  
Model Parameters ◽  
Grid Search ◽  
Optimal Model

scholarly journals Disease control as an optimization problem

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257958
Author(s):  
Miguel Navascués ◽  
Costantino Budroni ◽  
Yelena Guryanova
Keyword(s):  
Disease Control ◽  
Stochastic Models ◽  
Optimization Problem ◽  
Initial Conditions ◽  
Optimization Theory ◽  
Model Parameters ◽  
Grid Search ◽  
Brute Force ◽  
Time Required ◽  
Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

scholarly journals Simultaneous Identification of Model Structure and the Associated Parameters for Linear Systems Based on Particle Swarm Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Semin Chun ◽  
Tae-Hyoung Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Linear Systems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Model Parameters ◽  
Model Structure ◽  
Optimal Model ◽  
Swarm Optimization ◽  
Optimal Dimension ◽  
Simultaneous Identification

In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

scholarly journals DISTRICT WISE ANALYSIS OF COVID-19 PANDEMIC TRENDS DURING SECOND WAVE IN THE STATE ANDHRA PRADESH, INDIA USING SEIR-RGS MODEL

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
MSLB Subrahmanyam ◽  
Vajjha Hem Kumar
Keyword(s):  
Recovery Rate ◽  
Andhra Pradesh ◽  
The State ◽  
Infectious Period ◽  
Family Welfare ◽  
Model Parameters ◽  
Contact Rate ◽  
Grid Search ◽  
Indian States ◽  
Second Wave

— Andhra Pradesh is one of the south Indian states in India and having 13 districts. This is one of the most Covid-19 effected state in India during first and second waves. In India district is the major administrative block for implementing government policies and schemes under control of district collector. So, estimating or forecasting trends in district level more important than state wise or entire country wise. In this paper we are proposing Susceptible, Exposed, Infected and Recovered – Regression and Grid Search (SEIR-RGS) model for analyzing Covid -19 district wise trends during second wave. The SEIR-RGS, initially collects daily wise covid data for each district from Department of Health, medical and family welfare, AP and estimates the model parameters like contact rate, incubation rate and recovery rate. To calculate recovery rate the proposed model uses regression technique between daily active cases vs cumulative recoveries. The present model uses two phases for estimating contact rate and incubation rate using grid search approach. After that the proposed method calculates the infectious period, incubation period and basic reproduction of infection in all 13 districts to analyze trends in the state during second wave and also to predict possibility of third wave in each district.

Evaluation of optimal model parameters for prediction of methane generation from selected U.S. landfills

2019 ◽  
Vol 91 ◽  
pp. 120-127 ◽  
Author(s):  
Wenjie Sun ◽  
Xiaoming Wang ◽  
Joseph F. DeCarolis ◽  
Morton A. Barlaz
Keyword(s):  
Model Parameters ◽  
Optimal Model ◽  
Methane Generation

scholarly journals Optimal model parameters for multi-objective large-eddy simulations

2006 ◽  
Vol 18 (9) ◽  
pp. 095103 ◽  
Author(s):  
Johan Meyers ◽  
Pierre Sagaut ◽  
Bernard J. Geurts
Keyword(s):  
Large Eddy Simulations ◽  
Model Parameters ◽  
Optimal Model ◽  
Multi Objective ◽  
Large Eddy

Recursive Identification of the Dynamic Behavior in a Distillation Column by Means of Autoregressive Models

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Lakhdar Aggoune ◽  
Yahya Chetouani ◽  
Hammoud Radjeai
Keyword(s):  
Performance Measures ◽  
Distillation Column ◽  
Moving Average ◽  
Information Criterion ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Recursive Algorithms ◽  
Optimal Model ◽  
Armax Model

In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

scholarly journals Nonparametric Online Machine Learning with Kernels

2017 ◽  
Author(s):  
Khanh Nguyen
Keyword(s):  
Machine Learning ◽  
Bayesian Inference ◽  
Model Selection ◽  
Kernel Methods ◽  
Model Parameters ◽  
Grid Search ◽  
Learning Context ◽  
Real World Applications ◽  
Online Setting ◽  
Model Selection Problem

Max-margin and kernel methods are dominant approaches to solve many tasks in machine learning. However, the paramount question is how to solve model selection problem in these methods. It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications. Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and online setting.

scholarly journals A modeling approach to ultrasound evaluation of material properties

2010 ◽  
Author(s):  
◽  
Sri Waluyo
Keyword(s):  
Ultrasonic Waves ◽  
Binary Sequences ◽  
Model Verification ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimal Model ◽  
Ultrasound Evaluation ◽  
Voigt Model ◽  
Estimation Of Model Parameters ◽  
Linear State

A method for estimating the mechanical properties of a viscoelastic sample from ultrasound measurements was developed. The sample was represented as a mechanical network according to the Kelvin-Voigt model and linear state-space equations were derived to describe the system dynamics. Four parameters can be extracted by comparing the model with measured transmission waves. These parameters can be related to viscoelastic properties of the sample. Broadband pseudo-random binary sequences were designed and used to perturb the sample. The Levenberg-Marquardt method was employed to adjust the model parameters and the least-squares algorithm was used to obtain optimal model parameter estimates. Model verification showed that the algorithm developed could converge to known model parameters. Estimated model parameters showed consistency and reflected known facts about the materials tested. The model could capture the major dynamics of transmitted ultrasonic waves and allow repeatable estimation of model parameters. The model parameters could not only differentiate the materials tested but also follow expected trends of variation. The model parameters were useful for sensory crispness prediction and crispness was more correlated to the elastic modulus than to viscosity, which is consistent with existing research.

Asking questions of data in nonlinear geophysical inversion

2021 ◽  
Author(s):  
Malcolm Sambridge
Keyword(s):  
Inverse Problems ◽  
Optimal Transport ◽  
Model Parameters ◽  
Optimal Model ◽  
The Past ◽  
The Earth ◽  
Asking Questions ◽  
Using Data ◽  
Challenging Situation ◽  
Best Fit

<p>For more than half a century, geoscientists have sought new ways to solve inverse problems, which occur when observations only indirectly constrain some property of interest. In the case of geophysics this usually means using surface observations to quantify properties of the Earth hidden from us within its interior, or processes which occurred in the past. Both the target is not directly accessible, and measurements which constrain it are not completely under our control. This is a challenging situation, where the search for new efficient and practical methods of solution to inversion problems has received regular attention.</p><p>A convenient way to view inverse problems as a way of asking questions of data. A common class of question might be to ask `Which set of model parameters, within a chosent class, fits a subset of the data best?’  How one measures `best fit’ constitutes a fundamental component of the question being asked. Another example might be `Which probability distribution best describes a `state of knowledge’ about a set of representative parameters?’. As the question changes, naturally so does the solution, even if the data does not. This talk will examine this approach to inversion and explore some new forms of question that can be asked of data. In particular, cases will be examined where the same answer can arise from different style of questions, some of which are much easier to solve than others. A focus will be on optimal model generation in nonlinear cases using data questions based on mathematical ideas from the field of Optimal Transport.</p><p> </p>

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