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نتیجه جستجو - Parameter estimation

تعداد مقالات یافته شده: 13
ردیف عنوان نوع
1 Robust Quantum Metrology With Explicit Symmetric States
مترولوژی کوانتومی قوی با حالت های متقارن صریح-2022
Quantum metrology is a promising practical use case for quantum technologies, where physical quantities can be measured with unprecedented precision. In lieu of quantum error correction procedures, near term quantum devices are expected to be noisy, and we have to make do with noisy probe states. We prove that, for a set of carefully chosen symmetric probe states that lie within certain quantum error correction codes, quantum metrology exhibits an advantage over classical metrology even after the probe states are corrupted by a constant number of erasure and dephasing errors. These probe states prove useful for robust metrology not only in the NISQ regime, but also in the asymptotic setting where they achieve Heisenberg scaling. This brings us closer towards making robust quantum metrology a technological reality.
keywords: Quantum computing | error correction codes | parameter estimation.
مقاله انگلیسی
2 Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System
تخمین همزمان پارامترها و وضعیت یک سیستم نوسان ساز پارامتری نوری-2022
In this article, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter method are employed to simultaneously estimate the system state and the pump power. Numerical examples demonstrate the effectiveness of the proposed algorithms.
keywords: Optical parametric oscillator (OPO) | OPO system | parameter estimation | quantum state estimation | simultaneous estimation.
مقاله انگلیسی
3 Scaling laws and similarity models for the preliminary design of multirotor drones
مقیاس بندی قوانین و مدل های شباهت برای طراحی اولیه هواپیماهای بدون سرنشین چند منظوره-2020
Multirotor drones modelling and parameter estimation have gained great interest because of their vast application for civil, industrial, military and agricultural purposes. At the preliminary design level the challenge is to develop lightweight models which remain representative of the physical laws and the system interdependencies. Based on the dimensional analysis, this paper presents a variety of modelling approaches for the estimation of the functional parameters and characteristics of the key components of the system. Through this work a solid framework is presented for helping bridge the gaps between optimizing idealized models and selecting existing components from a database. Special interest is given to the models in terms of reliability and error. The results are compared for various existing drone platforms with different requirements and their differences discussed.
Keywords: Multirotor drones | Scaling laws | Dimensional analysis | Surrogate models | Propulsion system | Landing gear
مقاله انگلیسی
4 Probing relationships between reinforcement learning and simple behavioral strategies to understand probabilistic reward learning
روابط کاوش بین یادگیری تقویت کننده و راهکارهای رفتاری ساده برای درک یادگیری پاداش احتمالی-2020
Background: Reinforcement learning (RL) and win stay/lose shift model accounts of decision making are both widely used to describe how individuals learn about and interact with rewarding environments. Though mutually informative, these accounts are often conceptualized as independent processes and so the potential relationships between win stay/lose shift tendencies and RL parameters have not been explored. New method: We introduce a methodology to directly relate RL parameters to behavioral strategy. Specifically, by calculating a truncated multivariate normal distribution of RL parameters given win stay/lose shift tendencies from simulating these tendencies across the parameter space, we maximize the normal distribution for a given set of win stay/lose shift tendencies to approximate reinforcement learning parameters. Results: We demonstrate novel relationships between win stay/lose shift tendencies and RL parameters that challenge conventional interpretations of lose shift as a metric of loss sensitivity. Further, we demonstrate in both simulated and empirical data that this method of parameter approximation yields reliable parameter recovery. Comparison with existing method: We compare this method against the conventionally used maximum likelihood estimation method for parameter approximation in simulated noisy and empirical data. For simulated noisy data, we show that this method performs similarly to maximum likelihood estimation. For empirical data, however, this method provides a more reliable approximation of reinforcement learning parameters than maximum likelihood estimation. Conclusions: We demonstrate the existence of relationships between win stay/lose shift tendencies and RL parameters and introduce a method that leverages these relationships to enable recovery of RL parameters exclusively from win stay/lose shift tendencies.
Keywords: Win stay/Lose shift | Reinforcement learning | Maximum likelihood | Parameter estimation | Behavioral strategy
مقاله انگلیسی
5 Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression
طراحی و اجرای سیستم خبره فازی در روش های مونت کارلو برای رگرسیون خطی فازی-2019
In this study, fuzzy expert system (FES) in Monte Carlo (MC) method, which is used for estimating fuzzy linear regression model (FLRM) parameters, is applied to determine the parameter intervals, for the first time in the literature. MC method in estimating FLRM parameters is a new field of study that is very useful and time saving. However a major problem might occur in determining the parameter intervals from which the regression model parameters are supposed to come. If the intervals are calculated too large, FLRM error will be very large. Accordingly, the actual model parameters will not be obtained if the intervals are calculated too narrow. This drawback has not been addressed in the literature before and only optimization methods have been applied to achieve the best interval values. In this article, the FES is used for the first time in order to solve the problem in parameter estimation process for the FLRM in the field of statistics. For this purpose, the difference between the fuzzy observation value and fuzzy estimation value’s support set (W) is taken into account. The most appropriate intervals calculated for the parameters are those that make W as small as possible. Thus, FES is designed to determine the best intervals for the model parameters. The system knowledge base is composed of 7 fuzzy rules. As a result, it is deduced that the FLRM parameter estimates obtained from the MC method using FES are very close to the real values. The real impact of this paper will be in showing the applicability of FESs in order to solve problems that we encounter in the field of statistics by the help of linguistic expressions. Moreover, these outcomes will be useful for enriching the studies that have already focused on FLRMs and will encourage researchers to use FES to solve problems in statistics. To sum up, this study demonstrates that FESs which is used in technological devices and makes our lives easier can also be used in solving problems that we confront in the field of statistics efficiently with using linguistic expressions like human inference system.
Keywords: Fuzzy expert system | Fuzzy linear regression | Monte Carlo
مقاله انگلیسی
6 Technical diagnostic system in the maintenance of turbomachinery for ammonia synthesis in the process industries
سيستم تشخيص فني در نگهداري از توربوماخانه براي سنتز آمونياك در صنايع فرآيند-2019
Technical maintenance of machines and equipment in processing industry requires elaborate technical diagnostics systems to recognize the current state and forecast their future state. Creating such a system is a complex task due to multiple factors, with aging in aggressive exploitation environment being an important one. Statistical pattern recognition systems are very suitable to solve problems of technical diagnostics as they produce quantitative estimates of the states. We present the use of a hybrid Bayesian pattern recognition classifier that utilizes statistical and fuzzy paradigms and expresses the measurement information with four types of features (discrete, pseudo-discrete, multi-normal and independent continuous). It uses frequentist and subjective information (from training samples and expert opinion respectively) to identify the unknown parameters of the conditional likelihood density functions of each technical state. We discuss possible sources to collect learning information, and different methods to represent it. The classifier uses three different methods for parameter estimation of the conditional likelihood densities using data fusion. The classification is realised as a discriminant non-linear machine, which incorporates fuzzy approaches at different levels. We develop a novel algorithm for fault prediction without dynamic learning with four possible types of answers. A detailed example of technical diagnostics system for classification and prediction of states of turbomachinery for ammonia synthesis is presented. For the journal bearing diagnostics, we introduce modification of the hybrid Bayesian classifier using pseudo-priors to incorporate rule-based knowledge and improve the classification.
Keywords: Hybrid Bayesian classifier | Pseudo-discrete features | Fuzzy parameter estimation | Backward discriminant functions | Aging | Pseudo-priors | Ammonia synthesis
مقاله انگلیسی
7 Probabilistic performance prediction of shield tunnels in operation through data mining
پیش بینی عملکرد احتمالی تونل های سپر در کار از طریق داده کاوی-2019
To investigate the inherent uncertain and dynamic deterioration of metro shield tunnels in operation, the Bayesian ordered probit model, a data mining method, was used in this study. Through Markov Chain Monte Carlo (MCMC) simulation, the uncertainty in parameter estimation was significantly reduced, and the confidence in the results was improved. The effects of influencing variables on the deterioration process were evaluated. It was found that the tunnel sections with greater burial depths were more likely to deteriorate than the shallow ones. Crossing below a river or near a station or cross passage accelerated the deterioration rate. The deterioration probability increased as the tunnel became older. Finally, the model was applied to a tunnel section. It was shown that the probability of the best state decreased while that of the worst state increased with age. For states between the best and the worst, the probability would first increase, reach a peak, and then decrease. This study found that the ordered probit model with MCMC was a valuable probabilistic method for performance prediction, which is crucial for cost-effective decision-making in future work.
Keywords: Metro shield tunnel | Deterioration model | Performance prediction | Ordered probit model | Markov Chain Monte Carlo | Data mining | Asset management
مقاله انگلیسی
8 Parameters estimation in Ebola virus transmission dynamics model based on machine learning
برآورد پارامترها در مدل دینامیک انتقال ویروس ابولا بر اساس یادگیری ماشین-2019
This paper presents the application of machine learning to parameter estimation in biomathematical model. The background of Ebola disease was introduced, including the structure and morphology of the virus, the causes of disease, the mode of transmission, prevention and control measures. Meanwhile, it is essential to present the mechanism of this method, the application and calculation process, and the parameters. Compared with other methods, this method can not only obtain more accurate parameter values based on fewer and scattered data, but also estimate the parameters appearing anywhere in the partial differential equation, and automatically filter arbitrary noise data through Gaussian priori hypothesis.
Keywords: Ebola | Probabilistic machine learning | Multi-output Gaussian process | Kernel function
مقاله انگلیسی
9 Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
جستجوی پراکنده افزایش یافته مشارکتی با طرح یادگیری مبتنی بر مخالفت برای تخمین پارامتر در مدل های جنبشی ابعادی بالا از سیستم های بیولوژیکی-2019
Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bac- teria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico . The tools that could facilitate this process are known as the ki- netic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimension- ality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mecha- nism in order to exchange information (kinetic parameters) between individual threads. Each thread con- sists of different parameters settings that enhance the systemic properties in obtaining the global min- imum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology.
Keywords: Parameter estimation | Metabolic engineering | Kinetic model | Opposition-based learning | Global optimization | Cooperative metaheuristic
مقاله انگلیسی
10 A model-based data mining approach for determining the domain of validity of approximated models
یک روش داده کاوی مبتنی بر مدل برای تعیین دامنه اعتبار از مدل های تقریبی-2018
Parametric models derived from simplifying modelling assumptions give an approximated description of the physical system under study. The value of an approximated model depends on the consciousness of its descriptive limits and on the precise estimation of its parameters. In this manuscript, a framework for identifying the model domain of validity for the simplifying model hypotheses is presented. A model-based data mining method for parameter estimation is proposed as central block to classify the observed experimental conditions as compatible or incompatible with the approximated model. A nonlinear support vector classifier is then trained on the clas sified (observed) experimental conditions to identify a decision function for quantifying the expected model reliability in unexplored regions of the experimental design space. The proposed approach is employed for determining the domain of reliability for a simplified kinetic model of methanol oxidation on silver catalyst.
Keywords: Model identification ، Maximum likelihood ، Data mining ، Machine learning ، Model diagnosis
مقاله انگلیسی
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