Model Parameter Estimation Algorithm Research Articles

Extraction of single diode PV cell/module model parameters using a hybrid BMO approach with Lambert's W function

The accurate estimation of photovoltaic (PV) system parameters is crucial for effective simulation. Evaluation, modelling, and control of solar energy systems. The performance of photovoltaic installations is significantly influenced by the variability of model parameters and the difficulties in accessing them. As a result, the continuous objective is to consistently strive to search for and identify these parameters. This study proposes a hybrid algorithm that combines Bird Mating Optimiser algorithm with Lambert W-function (LBMO) with Wang's analytical method called (WLBMO) to optimise the parameters of the single diode model (SDM). The algorithm utilises a fast and precise iterative method for extracting I0 and Iph , and a Bird Mating Optimiser with Lambert W-function for extracting α, Rsh and Rs . The effectiveness of the proposed method is evaluated on the RTC solar cell and three commercial photovoltaic models. The specific results demonstrate the superior performance of our proposed WLBMO algorithm compared to known alternatives. For the R.T.C. France solar cell's single diode model, WLBMO achieves the smallest RMSE of 9.8630E-04, and similarly for the PWP201 model, yielding a low RMSE of 2.3845E-03. Comparative analysis, including Wang and LBMO, further validates our approach. Additionally, WLBMO excels among other methods for the STM6-120/36 commercial PV module, with a lowest RMSE of 1.7599E-03 and an exceptional minimum power error of 3.2270E-05. Similarly, for the KC200GT model, our method produces the smallest RMSE of 4.7569E-03. These consistent findings affirm the accuracy and efficacy of our WLBMO algorithm for PV model parameter estimation, emphasising its capacity to achieve significant error rate reductions of 92.856%, 1.147%, 49.732%, and 89.221% for the R.T.C France solar cell, Photowatt-PWP201, STM6-40/36, and KC200GT modules, respectively, and highlighting its pivotal role in optimising solutions.

Parallel EM algorithm on Hadoop for large-scale hidden Markov model parameter estimation

This paper aims to propose a parallel EM algorithm based on Hadoop for parameter estimation of large-scale hidden Markov models (HMM). HMM is a commonly used statistical model. However, since the parameter estimation of HMM involves the storage and processing of large-scale data sets, traditional serial algorithms have certain limitations in efficiency. This paper introduces the Hadoop parallel computing framework, divides the task into multiple subtasks through the MapReduce programming model and assigns them to different machines for parallel computing, which improves the efficiency and scalability of parameter estimation. The results show that using parallel EM algorithm for large-scale hidden Markov model parameter estimation on Hadoop is feasible and effective.

Mean-field model of synchronization for open-loop, swirl controlled thermoacoustic system.

Open-loop control is known to be an effective strategy for controlling self-excited periodic oscillations, known as thermoacoustic instability, in turbulent combustors. Here, we present experimental observations and a synchronization model for the suppression of thermoacoustic instability achieved by rotating the otherwise static swirler in a lab-scale turbulent combustor. Starting with the state of thermoacoustic instability in the combustor, we find that a progressive increase in the swirler rotation rate leads to a transition from the state of limit cycle oscillations to the low-amplitude aperiodic oscillations through a state of intermittency. To model such a transition while also quantifying the underlying synchronization characteristics, we extend the model of Dutta et al. [Phys. Rev. E 99, 032215 (2019)] by introducing a feedback between the ensemble of phase oscillators and the acoustic. The coupling strength in the model is determined by considering the effect of the acoustic and swirl frequencies. The link between the model and experimental results is quantitatively established by implementing an optimization algorithm for model parameter estimation. We show that the model is capable of replicating the bifurcation characteristics, nonlinear features of time series, probability density function, and amplitude spectrum of acoustic pressure and heat release rate fluctuations at various dynamical states observed during the transition to the state of suppression. Most importantly, we discuss the flame dynamics and demonstrate that the model without any spatial inputs qualitatively captures the characteristics of the spatiotemporal synchronization between the local heat release rate fluctuations and the acoustic pressure that underpins a transition to the state of suppression. As a result, the model emerges as a powerful tool for explaining and controlling instabilities in thermoacoustic and other extended fluid dynamical systems, where spatiotemporal interactions lead to rich dynamical phenomena.

Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation

This article deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge (MB) process. An MB is a 1-D Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times, which is important in social networks with known timed events. We justify the proposed model with real-world examples and examine its performance on a recent Twitter dataset. We provide a model parameter estimation algorithm based on maximum likelihood and a Bayesian filtering algorithm for recursively estimating the level of segregation using noisy samples obtained from the network. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required.

On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence

Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.

Modeling Multidimensional Forced Choice Measures with the Zinnes and Griggs Pairwise Preference Item Response Theory Model

This research developed a new ideal point-based item response theory (IRT) model for multidimensional forced choice (MFC) measures. We adapted the Zinnes and Griggs (ZG; 1974) IRT model and the multi-unidimensional pairwise preference (MUPP; Stark et al., 2005) model, henceforth referred to as ZG-MUPP. We derived the information function to evaluate the psychometric properties of MFC measures and developed a model parameter estimation algorithm using Markov chain Monte Carlo (MCMC). To evaluate the efficacy of the proposed model, we conducted a simulation study under various experimental conditions such as sample sizes, number of items, and ranges of discrimination and location parameters. The results showed that the model parameters were accurately estimated when the sample size was as low as 500. The empirical results also showed that the scores from the ZG-MUPP model were comparable to those from the MUPP model and the Thurstonian IRT (TIRT) model. Practical implications and limitations are further discussed.

L2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time.

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

Simulation of Tennis Match Scene Classification Algorithm Based on Adaptive Gaussian Mixture Model Parameter Estimation

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model parameter estimation algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability.

Towards vendor-agnostic real-time optical network design with extended Kalman state estimation and recurrent neural network machine learning [Invited

The network operator’s call for open, disaggregated optical networks to accelerate innovation and reduce cost, make progress in the standardization of interfaces, and raise telemetry capabilities in optical network systems has created an opportunity to adopt a new paradigm for optical network design. This new paradigm is driven by direct measurement and continuous learning from the actual optical hardware deployed in the field. We report an approach towards practical, vendor-agnostic, real-time optical network design and network management using a combination of two learning models. We generalize our physics-based optical model parameter estimation algorithm using the extended Kalman state estimation theory and, for the first time, to the best of our knowledge, present results using real optical network field data. An observed 0.3 dB standard deviation of the difference between typical predicted and measured signal quality appears mostly attributable to transponder performance variance. We further propose using the physics-based optical model parameter values as inputs to a second learning model with a recurrent neural network such as a gated recurrent unit (GRU) to allocate the appropriate required optical margin relative to the typical signal quality predicted by the physics-based optical model. A proof of concept shows that for a dataset of 3000 optical connections with a wide variety of amplified spontaneous emission noise and nonlinear noise limited conditions, a 10-hidden-unit 2-layer GRU was sufficient to realize a margin prediction error standard deviation below 0.2 dB. This approach of measurement data-driven automated network design will simplify deployment and enable efficient operation of open optical networks.

Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Software defects reflect software quality, and software failures can be predicted through software reliability models. Aiming at the problem that the parameters of software reliability model are difficult to estimate, this paper used the hybrid algorithm for model parameter estimation to software defect prediction. As a typical swarm intelligence algorithm, PSO (Particle Swarm Optimization) has fast convergence but low solution accuracy. SSA (Sparrow Search Algorithm) not only has high search accuracy and fast convergence speed, but also has the advantages of good stability and strong robustness. Based on the characteristic that the fitness function proposed in this paper, this paper hybrid PSO and SSA to accelerate the convergence before the individual update of the SSA. At the same time, this paper also constructed a new fitness function based on the maximum likelihood estimation of the parameters, and used it for parameter initialization. Through the analysis of the experimental results of five sets of actual data sets, the optimization performance of the hybrid algorithm (SSA-PSO) was better than that of a single algorithm with higher convergence speed and more stable, accurate results. Moreover, with the support of the new fitness function, it effectively solved the problems of slow convergence speed and low accuracy of solution. The experimental results showed that the hybrid SSA-PSO could obtain the better solution, convergence speed and stability than single SSA and PSO in software defections estimation and prediction.

Efficiency of Parameter Estimator of Various Resampling Methods on WarpPLS Analysis

WarpPLS analysis has three algorithms, namely the outer model parameter estimation algorithm, the inner model, and the hypothesis testing algorithm which consists of several choices of resampling methods namely Stable1, Stable2, Stable3, Bootstrap, Jackknife, and Blindfolding. The purpose of this study is to apply the WarpPLS analysis by comparing the six resampling methods based on the relative efficiency of the parameter estimates in the six methods. This study uses secondary data from the questionnaire with 1 variable being formative and 2 variables being reflective. Secondary data for the Infrastructure Service Satisfaction Index (IKLI) were obtained from the Study Report on the Regional Development Planning for Economic Growth and the Malang City Gini Index in 2018, while secondary data for the Social Capital Index (IMS) and Community Development Index (IPMas) were obtained from the Research Report on Performance Indicators Regional Human Development Index and Poverty Rate of Malang City in 2018. The results of this study indicate that based on two criteria used, namely the calculation of relative efficiency and measure of fit as a model good, it can be concluded that the Jackknife resampling method is the most efficient, followed with the Stable1, Bootstrap, Stable3, Stable2, and Blindfolding methods.

Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models

In this paper, a gradient descent algorithm is proposed for the parameter estimation of multi-input and multi-output (MIMO) total non-linear dynamic models. Firstly, the MIMO total non-linear model is mapped to a non-completely connected feedforward neural network, that is, the parameters of the total non-linear model are mapped to the connection weights of the neural network. Then, based on the minimization of network error, a weight-updating algorithm, that is, an estimation algorithm of model parameters, is proposed with the convergence conditions of a non-completely connected feedforward network. In further determining the variables of the model set, a method of model structure detection is proposed for selecting a group of important items from the whole variable candidate set. In order to verify the usefulness of the parameter identification process, we provide a virtual bench test example for the numerical analysis and user-friendly instructions for potential applications.

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

An ecological space based hybrid swarm-evolutionary algorithm for software reliability model parameter estimation

Reliability analysis of the software has attracted a lot of attention of the software developers and researchers due to rapid growing need of software in routine life. The software reliability prediction by mathematical models is entirely centered on the estimation of parameter values, and the parameter estimation of the models poses a non-differential, nonlinear, and multimodal problem. A new algorithm based on the concept of ecological space, method of differential evolution (DE) and intelligent behavior of artificial bee colony (ABC) for optimizing the parameter values has been proposed in this paper. The exploration capability in ABC algorithm has been improved by introducing the concept of ecological space. Ecological space is one of the important factors for evolution and reflects the expansion of individual bee in search space. DE technique provides the diversity of bee’s population and faster convergence. The proposed algorithm has been tested with four standard failure datasets. Proficiency of proposed algorithm is also compared with other meta-heuristic algorithms namely ABC, genetic algorithm and particle swarm optimization. Further validation of proposed algorithm is done through comparing its efficiency with hybrid partilce swarm optimization and gravitational search Algorithm. Simulation results verify that proposed hybrid algorithm is very much effective in field of software reliability estimation and would be a competitive one among meta-heuristic optimization algorithms.

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

PurposeThe purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.Design/methodology/approachIn order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.FindingsIt is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.Originality/valueThe paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

On-line Estimation Algorithms for Mathematical Model Parameters of Lithium Batteries

In the state model of potassium batteries, the discharge rate proportional coefficient and temperature proportional coefficient compensated by second-order fitting polynomial are proposed. The mathematical model is improved and the concrete steps to determine the above two coefficients are given. The parameter estimation technology of lithium battery observation model is studied. The algorithm and experimental results of parameter estimation of mathematical model based on the algorithm and sampling point Kalman filter algorithm are given. The experimental results show that the improved lithium battery model and parameter estimation method proposed in this paper can accurately describe the discharge characteristics of lithium battery. The online estimation algorithm of model parameters based on Kalman filter at sampling points can lay a good foundation for the embedded implementation of the subsequent estimation system.

A probabilistic analysis of a common RANSAC heuristic

Random Sample Consensus (RANSAC) is an iterative algorithm for robust model parameter estimation from observed data in the presence of outliers. First proposed by Fischler and Bolles back in 1981, it still is a very popular algorithm in the computer vision community. The primary objective of their paper was to find an effective strategy for excluding outliers from estimation process, but it did not consider the presence of noise among the inliers. A common practice among implementations of RANSAC is to take a few samples extra than the minimum required for estimation problem, but implications of this heuristic are lacking in the literature. In this paper, we present a probabilistic analysis of this common heuristic and explore the possibility of finding an optimal size for the randomly sampled data points per iteration of RANSAC. We also improve upon the lower bound for the number of iterations of RANSAC required to recover the model parameters. On the basis of this analysis, we propose an improvement in the hypothesis step of RANSAC algorithm. Since this step is shared (unchanged) by many of the variants of RANSAC, their performance can also be improved upon. The paper also presents the improvements achieved by incorporating the findings of our analysis in two of the popular variants of RANSAC.

LSMM: a statistical approach to integrating functional annotations with genome-wide association studies.

Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e. a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. The availability of functional annotation data enables us to address the above challenges. In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWAS of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. The LSMM software is available at https://github.com/mingjingsi/LSMM. Supplementary data are available at Bioinformatics online.

A New Algorithm for Solving Hemodynamic Models in fMRI under Low Frequency Interference

In order to solve the problem of estimating the model parameters of hemodynamic model under low frequency interference, a new model parameter estimation algorithm (CSCKS) is proposed in this paper for the hemodynamic model with low frequency interference. The algorithm is based on SCKS algorithm to improve, its estimation effect is obviously better than the existing SCKS algorithm and DEM algorithm. We simulate the proposed algorithm and some other traditional algorithms to evaluate the estimation performance of CSCKS proposed in this paper under different Signal to Interference Ratio (SIR) and different integration steps, and through the simulation results, the estimated results of the algorithm were analyzed and compared.

Unbiased Adaptive LASSO Parameter Estimation for Diffusion Processes

The adaptive Least Absolute Shrinkage and Selection Operator (aLASSO) method is an algorithm for simultaneous model selection and parameter estimation with oracle properties. In this work we derive an adaptive LASSO type estimator for diffusion driven stochastic differential equation under weak conditions, specifically that the algorithm does not rely on high frequency properties.All conditional moments needed in our quasi likelihood function are computed from the Kolmogorov Backward equation. This means that a single equation is solved numerically, regardless of the number of observations. The LASSO problem is solved using the Alternating Direction Method of Multipliers (ADMM) method.Our simulations show that the resulting algorithm is able to find the correct model with high probability while obtaining unbiased parameter estimates when evaluated on two qualitatively different data sets.