scholarly journals Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation

Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 228
Author(s):  
Karol Gellert ◽  
Erik Schlögl
Keyword(s):  
Genetic Algorithms ◽  
Particle Filter ◽  
Stochastic Volatility ◽  
Regime Shifts ◽  
Model Parameters ◽  
Parameter Learning ◽  
Model Dynamics ◽  
Current Point ◽  
Learning And Change ◽  
A Current

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

scholarly journals A LOW-BIAS SIMULATION SCHEME FOR THE SABR STOCHASTIC VOLATILITY MODEL

2012 ◽  
Vol 15 (02) ◽  
pp. 1250016 ◽  
Author(s):  
BIN CHEN ◽  
CORNELIS W. OOSTERLEE ◽  
HANS VAN DER WEIDE
Keyword(s):  
Stochastic Volatility ◽  
Asset Price ◽  
Realistic Model ◽  
Stochastic Volatility Model ◽  
Model Parameters ◽  
Fixed Income ◽  
Financial Industry ◽  
Volatility Model ◽  
Discretization Schemes ◽  
Simulation Scheme

The Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop a low-bias simulation scheme for the SABR-SV model, which deals efficiently with (undesired) possible negative values in the asset price process, the martingale property of the discrete scheme and the discretization bias of commonly used Euler discretization schemes. The proposed algorithm is based the analytic properties of the governing distribution. Experiments with realistic model parameters show that this scheme is robust for interest rate valuation.

scholarly journals Analytical Modeling and Validation of a Preloaded Piezoceramic Current Output

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 353
Author(s):  
Bin Zhang ◽  
Hongsheng Liu ◽  
Dezhi Li ◽  
Jinhui Liang ◽  
Jun Gao
Keyword(s):  
Energy Harvesting ◽  
Integrated Circuit ◽  
Microelectromechanical Systems ◽  
Experimental Testing ◽  
Current Source ◽  
Model Parameters ◽  
Current Output ◽  
System A ◽  
A Current ◽  
Piezoelectric Characteristics

Energy harvesting using piezoceramic has drawn a lot of attention in recent years. Its potential usage in microelectromechanical systems is starting to become a reality thanks to the development of an integrated circuit. An accurate equivalent circuit of piezoceramic is important in energy harvesting and the sensing system. A piezoceramic is always considered to be a current source according to empirical testing, instead of the derivation from its piezoelectric characteristics, which lacks accuracy under complicated mechanical excitation situations. In this study, a new current output model is developed to accurately estimate its value under various kinds of stimulation. Considering the frequency, amplitude and preload variation imposed on a piezoceramic, the multivariate model parameters are obtained in relation to piezo coefficients. Using this model, the current output could be easily calculated without experimental testing in order to quickly estimate the output power in energy harvesting whatever its geometric shape and the various excitations.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

Peculiarities of magnetic moment switching in the φ(0) junction

2021 ◽  
Author(s):  
Toktar Belgibayev ◽  
Yury Shukrinov ◽  
Andrej Plecenik ◽  
Jiri Pechousek ◽  
Cestmir Burdik
Keyword(s):  
Josephson Junction ◽  
Magnetic Moment ◽  
Magnetization Reversal ◽  
Characteristic Frequency ◽  
Easy Axis ◽  
Critical Value ◽  
Model Parameters ◽  
Memory Element ◽  
Superconducting Current ◽  
A Current

Abstract We have investigated the dynamics of magnetization under a current pulse in a φ0 - junction with a direct coupling between the magnetic moment and the superconducting current. The correspondence between the magnetization value at the end of the pulse mz * and the realization of the magnetization reversal along the easy axis of the ferromagnetic is considered. The crucial influence of the ratio w of the ferromagnetic frequency to the characteristic frequency of the Josephson junction on the results of reversal predictions is demonstrated. Effect of w magnitude on the manifestation of periodicity bands in the mz * dependence on the model parameters is shown. There is a critical value of the Gilbert damping, above which the magnetization reversal is not realized. It is shown that at small w the magnitude mz * can be as a criterion of magnetization reversal. I.e., if mz * <0, the magnetization reversal would happen with 100 percent probability. The results can be used in various areas of superconducting spintronics, in particular, to create a memory element based on the Josephson $ {\varphi_0} $ junction

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

2015 ◽  
pp. 1125-1152
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Batch Cultivation ◽  
Generation Gap ◽  
Model Parameters ◽  
Fed Batch ◽  
Intuitionistic Fuzzy ◽  
Before And After ◽  
Intuitionistic Fuzzy Logic ◽  
Fed Batch Cultivation

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

scholarly journals A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

2018 ◽  
Vol 12 (7) ◽  
pp. 2287-2306 ◽  
Author(s):  
Gaia Piazzi ◽  
Guillaume Thirel ◽  
Lorenzo Campo ◽  
Simone Gabellani
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Multivariate Data ◽  
Model Parameters ◽  
Parameter Perturbation ◽  
Point Scale ◽  
Meteorological Forcing ◽  
System Sensitivity ◽  
Filter Performance ◽  
The Impact

Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.

scholarly journals Time-Consistent Investment and Reinsurance Strategies for Mean-Variance Insurers under Stochastic Interest Rate and Stochastic Volatility

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2183
Author(s):  
Jiaqi Zhu ◽  
Shenghong Li
Keyword(s):  
Interest Rate ◽  
Stochastic Volatility ◽  
Financial Market ◽  
Equilibrium Strategy ◽  
Stochastic Interest Rate ◽  
Programming Approach ◽  
Model Parameters ◽  
Stochastic Interest ◽  
The Impact ◽  
Mean Variance

This paper studies the time-consistent optimal investment and reinsurance problem for mean-variance insurers when considering both stochastic interest rate and stochastic volatility in the financial market. The insurers are allowed to transfer insurance risk by proportional reinsurance or acquiring new business, and the jump-diffusion process models the surplus process. The financial market consists of a risk-free asset, a bond, and a stock modelled by Heston’s stochastic volatility model. Interest rate in the market is modelled by the Vasicek model. By using extended dynamic programming approach, we explicitly derive equilibrium reinsurance-investment strategies and value functions. In addition, we provide and prove a verification theorem and then prove the solution we get satisfies it. Moreover, sensitive analysis is given to show the impact of several model parameters on equilibrium strategy and the efficient frontier.

Sign in / Sign up

Export Citation Format

Share Document