scholarly journals COMPARISON OF SOME CLASSICAL AND META-HEURISTIC OPTIMIZATION TECHNIQUES IN THE ESTIMATION OF THE LOGIT MODEL PARAMETERS.

2016 ◽  
Vol 4 (10) ◽  
pp. 1026-1042
Author(s):  
Ozge Akkus ◽  
◽  
Emre Demir ◽  
Keyword(s):  
Logit Model ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Model Parameters

scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

Genetic Algorithm-Based Maximum Likelihood Parameter Estimation for Transit Buses

2001 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Dynamic Models ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Parameter Estimation Method ◽  
Transit Buses ◽  
Simulation And Control ◽  
And Control

Abstract Vehicle dynamic models include parameters that qualify the dependence of input forces and moments on state and control variables. The accuracy of the model parameter estimates is important for modeling, simulation, and control. In general, the most accurate method for determining values of model parameters is by direct measurement. However, some parameters of vehicle dynamics, such as suspension damping or moments of inertia, are difficult to measure accurately. This study aims at establishing an efficient and accurate parameter estimation method for developing dynamic models for transit buses, such that this method can be easily implemented for simulation and control design purposes. Based on the analysis of robustness, as well as accuracy and efficiency of optimization techniques, a parameter estimation method that integrates Genetic Algorithms and the Maximum Likelihood Estimation is proposed. Choices of output signals and estimation criterion are discussed involving an extensive sensitivity analysis of the predicted output with respect to model parameters. Other experiment-related aspects, such as imperfection of data acquisition, are also considered. Finally, asymptotic Cramer-Rao lower bounds for the covariance of estimated parameters are obtained. Computer simulation results show that the proposed method is superior to gradient-based methods in accuracy, as well as robustness to the initial guesses and measurement uncertainty.

Full-waveform inversion of salt models using shape optimization and simulated annealing

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R793-R804 ◽  
Author(s):  
Debanjan Datta ◽  
Mrinal K. Sen ◽  
Faqi Liu ◽  
Scott Morton
Keyword(s):  
Simulated Annealing ◽  
Least Squares ◽  
Level Set ◽  
Waveform Inversion ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Velocity Models ◽  
Full Waveform ◽  
Starting Model

A good starting model is imperative in full-waveform inversion (FWI) because it solves a least-squares inversion problem using a local gradient-based optimization method. A suboptimal starting model can result in cycle skipping leading to poor convergence and incorrect estimation of subsurface properties. This problem is especially crucial for salt models because the strong velocity contrasts create substantial time shifts in the modeled seismogram. Incorrect estimation of salt bodies leads to velocity inaccuracies in the sediments because the least-squares gradient aims to reduce traveltime differences without considering the sharp velocity jump between sediments and salt. We have developed a technique to estimate velocity models containing salt bodies using a combination of global and local optimization techniques. To stabilize the global optimization algorithm and keep it computationally tractable, we reduce the number of model parameters by using sparse parameterization formulations. The sparse formulation represents sediments using a set of interfaces and velocities across them, whereas a set of ellipses represents the salt body. We use very fast simulated annealing (VFSA) to minimize the misfit between the observed and synthetic data and estimate an optimal model in the sparsely parameterized space. The VFSA inverted model is then used as a starting model in FWI in which the sediments and salt body are updated in the least-squares sense. We partition model updates into sediment and salt updates in which the sediments are updated like conventional FWI, whereas the shape of the salt is updated by taking the zero crossing of an evolving level set surface. Our algorithm is tested on two 2D synthetic salt models, namely, the Sigsbee 2A model and a modified SEG Advanced Modeling Program (SEAM) Phase I model while fixing the top of the salt. We determine the efficiency of the VFSA inversion and imaging improvements from the level set FWI approach and evaluate a few sources of uncertainty in the estimation of salt shapes.

A New Approach to Determine Dynamic Strength Model Parameters under Taylor Impact Test

2012 ◽  
Vol 525-526 ◽  
pp. 377-380
Author(s):  
F. Xu ◽  
Wei Guo Guo ◽  
Q.J. Wang ◽  
Zhi Yin Zeng
Keyword(s):  
Impact Test ◽  
Dynamic Strength ◽  
Hopkinson Bar ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Strength Model ◽  
New Approach ◽  
Taylor Impact ◽  
Bar Test ◽  
Taylor Impact Test

In this paper, to determine the dynamic strength model for steels, a new approach which does not rely on the Hopkinson bar test has been proposed. As the DH36 steel for example, using the results of Taylor impact test and the quasi-static compression test, the initial parameters of Johnson-Cook plastic strength model have been fitted out, then the initial strength parameters have been optimized using the optimization techniques of the sparse Taylor impact cylinder. It has been shown that the optimized results in numerical simulation are consistent with results of Taylor impact test, and the optimized Johnson-Cook model can also well describe flow stress curve fitted from the Hopkinson bar test.

The significance of relevance trees in the identification of artificial neural networks input vectors

2013 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
G. Manuel ◽  
J.H.C. Pretorius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Methods ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Wide Range ◽  
Accurate Forecast ◽  
Artificial Neural

In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

Semi-empirical model for a hydraulic servo-solenoid valve

2002 ◽  
Vol 216 (3) ◽  
pp. 237-248 ◽  
Author(s):  
J A Ferreira ◽  
F Gomes de Almeida ◽  
M R Quintas
Keyword(s):  
Frequency Response ◽  
High Performance ◽  
Closed Loop ◽  
Optimization Techniques ◽  
Order System ◽  
Model Parameters ◽  
Empirical Modelling ◽  
Non Linear ◽  
Spool Valves ◽  
Semi Empirical

High-performance proportional valves, also called servo-solenoid valves, can be used today in closed-loop applications that previously were only possible with servo-valves. The valve spool motion is controlled in a closed loop with a dedicated hardware controller that enhances the valve frequency response and minimizes some non-linear effects. Owing to their lower cost and maintenance requirements as well as increasing performance they can compete with servo-valves in a large number of applications. This paper describes a new semi-empirical modelling approach for hydraulic proportional spool valves to be used in hardware-in-the-loop simulation experiments. The developed models use either data sheet or experimental values to fit the model parameters in order to reproduce both static (pressure gain, leakage flowrate and flow gain) and dynamic (frequency response) valve characteristics. Valve behaviour is divided into two parts: the static behaviour and the dynamic behaviour. A parameter decoupled model, with a variable equation structure, and a flexible model, with a fixed equation structure, are proposed for the static part. Spool dynamics are modelled by a non-linear second-order system, with limited velocity and acceleration, the parameters being adjusted using optimization techniques.

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