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ردیف | عنوان | نوع |
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1 |
Data-driven switching modeling for MPC using Regression Trees and Random Forests
مدل سازی سوئیچینگ داده محور برای MPC با استفاده از درختان رگرسیون و جنگل های تصادفی-2020 Model Predictive Control is a well consolidated technique to design optimal control
strategies, leveraging the capability of a mathematical model to predict a system’s
behavior over a time horizon. However, building physics-based models for complex
large-scale systems can be cost and time prohibitive. To overcome this problem we
propose a methodology to exploit machine learning techniques (i.e. Regression Trees and
Random Forests) in order to build a Switching Affine dynamical model (deterministic and
Markovian) of a large-scale system using historical data, and apply Model Predictive Control.
A comparison with an optimal benchmark and related techniques is provided on an
energy management system to validate the performance of the proposed methodology. Keywords: Regression Trees | Random Forests | Model predictive control | Switching systems | Markov Jump Systems |
مقاله انگلیسی |
2 |
Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
پیش بینی مقیاس بزرگ غلظت نیترات آب زیرزمینی از داده های مکانی با استفاده از یادگیری ماشین-2019 Reducing nitrogen inputs, in particular nitrate, to groundwater is becoming increasingly important to fulfil requirements
of the European Water Framework Directive. When developing management plans for mitigation
measures at larger scales, complex hydro-biogeochemical models reach their limits due to data availability and
spatial discretization. To circumvent this problem, the spatial distribution of nitrate concentration in groundwater
is estimated using a parsimonious GIS-based statistical approach. Point nitrate concentrations and spatial environmental
data as predictors are used to train statistical models. In order to compile the spatial predictorswith
the respectivemonitoring sites, different designs of contributing areas (buffer zones) and their effects on the performance
of different statistical models are investigated. Multiple Linear Regression (MLR), Classification and
Regression Trees (CART), RandomForest (RF) and Boosted Regression Trees (BRT) are compared in terms of
the predictive performance of each model according to various objective functions.We determine the most
influential spatial predictors used in the respective models. After training the models with a subset of the
data, we then predict the spatial nitrate distribution in groundwater for the entire federal state of Hesse,
Germany on a 1 × 1 kmgrid by only the spatial environmental data. The Random Forestmodel outperforms
the other models (R2=0.54), relying on hydrogeological units, the percentage of arable land and the nitrogen
balance as the three most influencing predictors based on a 1000 m circular contributing area. The use
of exclusively spatial available predictors is a big step forward in the prediction of nitrate in groundwater on
regional scale. Keywords: Nitrate | Groundwater | Machine learning | GIS |
مقاله انگلیسی |
3 |
Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
ادغام یادگیری ماشین در تئوری پاسخ به موارد برای پرداختن به مشکل شروع سرما در سیستم های یادگیری انطباقی-2019 Adaptive learning systems aim to provide learning items tailored to the behavior and needs of
individual learners. However, one of the outstanding challenges in adaptive item selection is that
often the corresponding systems do not have information on initial ability levels of new learners
entering a learning environment. Thus, the proficiency of those new learners is very difficult to be
predicted. This heavily impairs the quality of personalized items recommendation during the
initial phase of the learning environment. In order to handle this issue, known as the cold-start
problem, we propose a system that combines item response theory (IRT) with machine learning.
Specifically, we perform ability estimation and item response prediction for new learners by
integrating IRT with classification and regression trees built on learners’ side information. The
goal of this work is to build a learning system that incorporates IRT and machine learning into a
unified framework. We compare the proposed hybrid model to alternative approaches by conducting
experiments on two educational data sets. The obtained results affirmed the potential of
the proposed method. In particular, the obtained results indicate that IRT combined with
Random Forests provides the lowest error for the ability estimation and the highest accuracy in
terms of response prediction. This way, we deduce that the employment of machine learning in
combination with IRT could indeed alleviate the effect of the cold start problem in an adaptive
learning environment Keywords: Item response theory | Decision tree learning | Machine learning | Adaptive learning system | Cold-start problem |
مقاله انگلیسی |
4 |
Macroeconomic variable selection for creditor recovery rates
انتخاب متغیر اقتصاد کلان برای نرخ های بازیابی بستانکار-2018 We study the relationship between U.S. corporate bond recovery rates and macroeconomic variables used in the credit risk literature. The least absolute shrinkage and selection operator (LASSO) is used in selecting macroeconomic variables. The LASSO-selected macroeconomic variables are considered to be explanatory variables in ordinary least squares regressions, bootstrap aggregating (bagging), regression trees, boosting, LASSO, ridge regression and support vector regression techniques. We compare the out-of-sample predictive power of two types of models (LASSO-selected models with models that add principal components derived from 179 macroeconomic variables as explanatory variables). We find the recovery models with LASSO-selected macroeconomic variables outperform suggested models in the literature.
keywords: Macroeconomic variables |Least absolute shrinkage and selection operator (LASSO) |Corporate bond |Recovery rates |Credit risk |
مقاله انگلیسی |
5 |
Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network
پیش بینی سرعت سرعت باد با کمک داده ها توسط تجزیه بسته های موجک و شبکه عصبی المان-2018 On the basis of data-mining technology, a hybrid method of short-term wind speed forecast is proposed by the
wavelet packet decomposition, density-based spatial clustering of applications with noise, and the Elman neural
network (WPD-DBSCAN-ENN). First, the WPD is applied to decompose a raw wind speed series into several sub
series. The gradient boosted regression trees (GBRT) algorithm is then applied to determine the structure of the
ENNs for each sub-wind series. Next, the training dataset is clustered by the DBSCAN to select the representative
data for the ENNs. A key parameter in the DBSCAN is chosen through a new method. Finally, the wind speed
forecast is conducted by the ENNs. Case studies are adopted to validate the accuracy of the hybrid methods. The
results are compared with those obtained using the WPD-ENN hybrid method and a single ENN via four general
error criteria. The performance of the WPD-DBSCAN-ENN hybrid method outperformed those of the other
methods indicated above.
Keywords: Wind speed forecasting ، Wavelet packet decomposition (WPD) ، Gradient boosted regression trees (GBRT) ، Density-based spatial clustering of applications ، with noise (DBSCAN) ، Elman neural network (ENN) |
مقاله انگلیسی |