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optimal parameter-estimation FOR BASIC PROBLEMS OF ECONOMIC OPERATION IN ELECTRIC-POWER SYSTEMS

optimal CONTROL APPLICATIONS & METHODS 1981年第3期2卷 269-287页

作者： ELHAWARY, ME MANSOUR, SY UNIV ALBERTA DEPT ELECT ENGNEDMONTON T6G 2M7ALBERTACANADA

The paper treats the problem of optimal parameter estimation for some basic models commonly used in conjunction with optimal economic operation studies in integrated electric power systems. Three well-known parameter estimation algorithms are considered for this purpose. The weighted least-squares, Gauss-Newton, and Powell's regression algorithms are utilized to obtain optimal parameters for the fuel cost model of thermal generating sources, active power loss model for the electric network, as well as the active-reactive power loss models. Performance comparisons using some test systems show that the Powell's regression results provide satisfactory optimal estimates.

关键词： Electric power systems optimal parameter estimation parameter estimation algorithms

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optimal parameter estimation for Muskingum Model Using a Modified Particle Swarm Algorithm

Optimal Parameter Estimation for Muskingum Model Using a Mod...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Wenchuan Wang Yingbin Kang Lin Qiu Faculty of Water conservancy Engineering North China Institute of Water Conservancy and Hydroelectri Faculty of environmental & municipal engineering North China Institute of Water Conservancy and Hydr

The accurate parameter estimation for Muskingum model is to be useful to give the flood forecasting for flood control in water resources planning and management. Although some methods have been used to estimate the parameters for Muskingum model, an efficient method for parameter estimation in the calibration process is still lacking. In order to reduce the computational amount and improve the computational precision for parameter estimation, a modified particle swarm algorithm (MPSO) is presented for parameter optimization of Muskingum model. The technique found the best parameter values compared to previous results in terms of the sum of least residual absolute value. Empirical results that involve historical data from existed paper reveal the proposed MPSO outperforms other approaches in the literature.

关键词： component Muskingum Model optimal parameter estimation Modified Particle Swarm Algorithm

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Robust optimal parameter estimation for the susceptible-unidentified infecte d-confirme d model

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CHAOS SOLITONS & FRACTALS 2021年 153卷

作者： Lee, Chaeyoung Kwak, Soobin Kim, Sangkwon Hwang, Youngjin Choi, Yongho Kim, Junseok Korea Univ Dept Math Seoul 02841 South Korea Daegu Univ Dept Math & Big Data Gyongsan 38453 Gyeongsangbuk D South Korea

In this study, we consider a robust optimal parameter estimation method for the Susceptible-Unidentified infected-Confirmed (SUC) epidemic dynamics model. One of the problems in determining parameter val-ues associated with epidemic mathematical models is that the optimal parameter values are very sensi-tive to the initial guess of parameter values. To resolve this problem, we fix the value of one parameter and solve an optimization problem of finding the other parameter values which best fit the confirmed population. The fixed parameter value can be obtained using data from epidemiological surveillance sys-tems. To demonstrate the robustness and accuracy of the proposed method, we perform various numeri-cal experiments with synthetic and real-world data from South Korea, the United States of America, India, and Brazil. The computational results confirm the potential practical application of the proposed method. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： COVID-19 SUC model optimal parameter estimation Least-squares fitting

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Stochastic optimal Control for Uncertain Structural Systems Under Random Excitations Based on Bayes optimal estimation

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BUILDINGS 2025年第9期15卷

作者： Lei, Hua Ying, Zhao-Zhong Ying, Zu-Guang Zhejiang Univ Sch Aeronaut & Astronaut Dept Mech Hangzhou 310027 Peoples R China Hoongjun Micro Elect Technol Co Hangzhou 311202 Peoples R China

Stochastic vibration control of uncertain structures under random loading is an important problem and its minimax optimal control strategy remains to be developed. In this paper, a stochastic optimal control strategy for uncertain structural systems under random excitations is proposed, based on the minimax stochastic dynamical programming principle and the Bayes optimal estimation method with the combination of stochastic dynamics and Bayes inference. The general description of the stochastic optimal control problem is presented including optimal parameter estimation and optimal state control. For the estimation, the posterior probability density conditional on observation states is expressed using the likelihood function conditional on system parameters according to Bayes' theorem. The likelihood is replaced by the geometrically averaged likelihood, and the posterior is converted into its logarithmic expression to avoid numerical singularity. The expressions of state statistics are derived based on stochastic dynamics. The statistics are further transformed into those conditional on observation states based on optimal state estimation. Then, the obtained posterior will be more reliable and accurate, and the optimal estimation will greatly reduce uncertain parameter domains. For the control, the minimax strategy is designed by minimizing the performance index for the worst-parameter system, which is obtained by maximizing the performance index based on game theory. The dynamical programming equation for the uncertain system is derived according to the minimax stochastic dynamical programming principle. The worst parameters are determined by the maximization of the equation, and the optimal control is determined by the minimization of the resulting equation. The minimax optimal control by combining the Bayes optimal estimation and minimax stochastic dynamical programming will be more effective and robust. Finally, numerical results for a five-story frame structure u

关键词： stochastic optimal control optimal parameter estimation Bayes inference stochastic dynamical programming uncertain structure system minimax strategy unknown random excitation

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Adaptive signal processing of asset price dynamics with predictability analysis

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INFORMATION SCIENCES 2008年第1期178卷 203-219页

作者： Mamon, Rogemar S. Erlwein, Christina Gopaluni, R. Bhushan Univ Western Ontario Western Sci Ctr Dept Stat & Actuarial Sci London ON N6A 5B7 Canada Brunel Univ Sch Informat Syst Comp & Math CARISMA Uxbridge Middx England Univ British Columbia Dept Chem & Biol Engn Vancouver BC V5Z 1M9 Canada

In this paper we illustrate the optimal filtering of log returns of commodity prices in which both the mean and volatility are modulated by a hidden Markov chain with finite state space. The optimal estimate of the Markov chain and the parameters of the price model are given in terms of discrete-time recursive filters. We provide an application on a set of high frequency gold price data for the period 1973-2006 and analyse the h-step ahead price predictions against the Diebold-Kilian metric. Within the modelling framework where the mean and volatility are switching regimes, our findings suggest that a two-state hidden Markov model is sufficient to describe the dynamics of the data and the gold price is predictable up to a certain extent in the short term but almost impossible to predict in the long term. The proposed model is also bench-marked with ARCH and GARCH models with respect to price predictability and forecasting errors. (C) 2007 Published by Elsevier Inc.

关键词： Hidden Markov model optimal parameter estimation regime-switching model gold price

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optimal parameters estimation of PEMFCs model using Converged Moth Search Algorithm

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ENERGY REPORTS 2020年 6卷 1501-1509页

作者： Sun, Shouqiang Su, Yumei Yin, Chengbo Jermsittiparsert, Kittisak Qingdao Huanghai Univ Int Business Sch Qingdao 266427 Shandong Peoples R China Qingdao Huanghai Univ Sch Construct Engn Qingdao 266427 Shandong Peoples R China Qingdao Huanghai Univ Coll Big Data Qingdao 266427 Shandong Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand

One important part of designing and manufacturing of the fuel cells is their model identification. The present study proposes an optimal method for optimal parameter estimation of the undetermined parameters in Proton Exchange Membrane Fuel Cells (PEMFCs). The method uses a novel modified version of the Moth Search Algorithm, called Converged Moth Search Algorithm (CMSA) to minimize the total of the squared deviations (TSD) between the output voltage and the experimental data. The method is then applied to two different test cases including BCS 500-W PS6 and NedStack PS6. The results show that the suggested CMSA has a TSD and running time equal to 0.012 and 2.96 for BCS and 2.15 and 3.19 for the NedStack that are the minimum values for both case studies toward the other compared algorithms. therefore, the results showed that the suggested method has a good data agreement with the experimental data. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： PEMFC A total of the squared deviations optimal parameter estimation Moth Search Algorithm

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Numerical harmonic modelling of low wattage LED lamps based on parameter estimation algorithms

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ISA TRANSACTIONS 2024年 155卷 489-511页

作者： Salbi, Hasan A. Abdulmajeed Al-Amyal, Fahad Peter, Kiss Budapest Univ Technol & Econ Dept Elect Power Engn H-1111 Budapest Hungary Univ Baghdad Coll Engn Dept Elect Engn Baghdad 10001 Iraq Univ Al Qadisiyah Coll Engn Dept Elect & Commun Diwaniya Iraq

This paper presents a numerical harmonic modeling approach for 18 low-wattage Light-Emitting Diode (LED) lamps. The proposed methodology utilizes meta-heuristic algorithms, namely Multi-Stage Ant Colony Optimization (MSACO) and Harris Hawk Optimization (HHO), to estimate optimal parameters for the equivalent circuits. Additionally, the study examines eight different configurations of Electromagnetic Interference (EMI) Filter circuits, including their respective parameters, which function as front-end circuits for the Full Bridge Rectifier (FBR) of the LED drivers. Through extensive investigations, the primary objective is to establish generalized and accurate models for various EMI filter circuits that can be universally applied, with validation through comparison against experimental results. Under both MSACO and HHO, the lowest average total error ranges were observed in the LC-Filter and CL-Filter configurations, with errors ranging from 21.302 % and 22.769 %, respectively. On the other hand, the L-Filter and R-only configurations exhibited the highest average total error ranges, with errors of 42.376 % and 44.648 %, respectively. Notably, this paper introduces a non-intrusive, noninvasive method for estimating optimal parameters based solely on measurements obtained from the input terminals of the LED lamps.

关键词： LED Numerical Harmonic Modeling EMI Filter optimal parameter estimation MSACO HHO

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Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第5期58卷 2475-2483页

作者： Peng, Silong Hwang, Wen-Liang Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Acad Sinica Inst Informat Sci Taipei 11529 Taiwan Kainan Univ Dept Comp Sci & Informat Engn Tao Yuan Taiwan

The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. The approach can be formulated as an optimization problem whose optimal solution can be derived analytically. However, the following issues must still be resolved: estimating the robustness of the operator's parameters and the Lagrangian multipliers, and determining how much of the information in the null space of the operator should be retained in the residual signal. To address these problems, we propose a novel optimization formula for operator-based signal separation and show that the parameters of the problem can be estimated adaptively. We demonstrate the effectiveness of the proposed method by processing several signals, including real-life signals.

关键词： Adaptive signal separation operator-based optimal parameter estimation

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A self-updating model driven by a higher-order hidden Markov chain for temperature dynamics

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JOURNAL OF COMPUTATIONAL SCIENCE 2016年 17卷 47-61页

作者： Xiong, Heng Mamon, Rogemar Univ Western Ontario Dept Stat & Actuarial Sci 1151 Richmond St London ON N6A 5B7 Canada

We develop a model for the evolution of daily average temperatures (DATs) that could benefit the analysis of weather derivatives in finance and economics as well as the modelling of time series data in meteorology, hydrology and other branches of the sciences and engineering. Our focus is to capture the mean-reverting, seasonality, memory and stochastic properties of the temperature movement and other time series exhibiting such properties. To model both mean-reversion and stochasticity, a deseasonalised component is assumed to follow an Ornstein-Uhlenbeck (OU) process modulated by a higher-order hidden Markov chain, which takes into account short/long range dependence in the data. The seasonality part is modelled through a combination of linear and sinusoidal functions with appropriate coefficients and arguments. Furthermore, we put forward a parameter estimation approach that establishes recursive higher-order hidden Markov model (HOHMM) filtering algorithms customised for the regime-switching evolution of model parameters. Quantities that are functions of HOHMC characterise these filters. Utilising the EM method in conjunction with the change of measure technique, optimal and self-updating parameter estimates are obtained. We illustrate the numerical implementation of our model and estimation technique using a 4-year Toronto DATs data set compiled by the National Climatic Data Center. We perform pertinent model selection and validation diagnostics to assess the performance of our methodology. It is shown that a 2-state HOHMM-based model best captures the empirical characteristics of the temperature data under examination on the basis of various error-based and information-criterion metrics. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Weather contracts Regime-switching model Maximum likelihood Filtering Weak HMM Change of probability measure optimal parameter estimation Finance

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Online estimation for a predictive analytics platform with a financial-stability-analysis application

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EUROPEAN JOURNAL OF CONTROL 2021年 57卷 205-221页

作者： Gu, Xing Mamon, Rogemar Duprey, Thibaut Xiong, Heng Univ Western Ontario Dept Stat & Actuarial Sci 1151 Richmond St London ON N6A 5B7 Canada Bank Canada Financial Stabil Dept Ottawa ON Canada Wuhan Univ Econ & Management Sch Wuhan Hubei Peoples R China Univ Philippines Visayas Div Phys Sci & Math Iloilo Philippines

An online parameter estimation via filtering recursions is constructed to support a data-analytics scheme in the predictive domain. Multivariate financial market indices or signals revealed in real time are used in our numerical implementation. This work contributes to the analysis and forecasting of financial crises in an environment that evolves dynamically. In particular, we capture the regime-switching characteristics of the Financial Stress Index (FSI) and Industrial Production Index (IPI), designed to detect periods of financial crisis. We integrate two different stochastic models for FSI and IPI deemed to mirror the systemic financial stress levels in the financial and business cycles, respectively. The joint dynamics of the FSI and IPI, exhibit stochasticity, mean reversion, seasonality, and occasional jumps are identified in the most efficient way. All parameters are modulated by a discrete-time hidden Markov chain that switches between economic regimes reflecting various interacting economic forces. Through change of reference probability technique, adaptive multivariate filters are derived which in turn provides online optimal parameter estimates. Historical Canadian economic-based FSI and IPI are examined and an early-warning signal extraction method is put forward to generate alerts at the early stage of some crisis events. Our modelling approach captures the empirical characteristics of FSI and IPI as well as provides auspiciously early warnings for episodes of systemic financial crisis. (C) 2020 European Control Association. Published by Elsevier Ltd. All rights reserved.

关键词： Hidden Markov model optimal parameter estimation Regime-switching model Financial stress index Industrial production index

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