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A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling

COMPUTER methodS IN APPLIED MECHANICS AND ENGINEERING 2019年第Sep.1期354卷 506-526页

作者： Nguyen, Tan N. Lee, Seunghye Nguyen-Xuan, H. Lee, Jaehong Sejong Univ Deep Learning Architecture Res Centel 209 Neungdong Ro Seoul 05006 South Korea Ho Chi Minh City Univ Technol HUTECH CIRTECH Inst Ho Chi Minh City Vietnam

A novel analysis-prediction (ANP) approach for geometrically nonlinear problems of solid mechanics is for the first time proposed in this paper. The key concept of this approach is: (1) A part of equilibrium path is traced by numerical analysis;(2) data of this part is then used for training predictive network;(3) Applying the trained network, the rest of the equilibrium path is simply traced by pure prediction without using any analysis. As an illustration for ANP approach, the analysis package is in this study established based on isogeometric shell analysis using the first-order shear deformation shell theory (FSDT). As the main advantage of the proposed approach, computational cost is significantly lower than that of the conventional approach based on pure numerical analysis. In addition, the predictive networks are built via group method of data handling (GMDH) known as a self-organizing deep learning method for time series forecasting problems without requirement of big data. Some numerical examples are provided to confirm the high accuracy and efficiency of the proposed approach. The approach not only could be applied to a wide range of computational mechanics problems in which nonlinear response occurs but also to other computational engineering fields. (C) 2019 Published by Elsevier B.V.

关键词： Geometrically nonlinear problems Isogeometric shell analysis group method of data handling Time series forecasting problems

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Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling

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MACHINE VISION AND APPLICATIONS 2020年第7-8期31卷 59-59页

作者： Mohebbian, Mohammad Reza Rasti, Javad Univ Saskatchewan Elect & Comp Engn Dept Saskatoon SK Canada Univ Isfahan Fac Engn Biomed Engn Dept Esfahan Iran

Eye-gaze tracking through camera is commonly used in a number of areas, such as computer user interface systems, sports science, psychology, and biometrics. The robustness of the head and camera rotation tracking algorithm has been a critical problem in recent years. In this paper, Haar-like features and a modified version of the group method of data handling, as well as segmented regression, are used together to find the base points of the eyes in a facial image. Then, a geometric transformation is applied to detect precise eye-gaze direction. The proposed algorithm is tested on GI4E and Columbia Gaze datasets and compared to other algorithms. The results show adequate accuracy, especially when the head/camera is rotated.

关键词： Eye tracking Haar-like features group method of data handling Segmented regression

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Prediction of subgrade reaction modulus of clayey soil using group method of data handling

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SCIENTIA IRANICA 2020年第4期27卷 1740-1750页

作者： Naeini, S. A. Moayed, R. Ziaie Kordnaeij, A. Mola-Abasi, H. Imam Khomeini Int Univ Dept Civil Engn Qazvin Iran Gonbadekavous Univ Dept Civil Engn Gonbadekavous Iran

Settlement-based design of foundations, using subgrade reaction modulus (K-s), is an important technique in geotechnical engineering. Plate Load Test (PLT) is one of the most commonly performed tests to directly obtain K-s of soil. As the determination of K-s by PLT -especially in greater depths- is both time-consuming and costly, it is necessary to develop models that can easily handle measurable characteristics. The suitability of the group method of data handling (GMDH) polynomial Neural Network (NN) to estimate K-s of clayey soils was investigated in the present research. In order to develop the GMDH models, 123 data sets from Qazvin, Iran, were applied. Predictability of the derived equations was compared with those of other available equations for clayey soil. The results demonstrated that an improvement in predicting K-s was achieved. Sensitivity analysis of the best GMDH-based equation showed that the Liquid Limit (LL) of soil was the most influential parameter on the proposed GMDH model in predicting K-s of clayey soil. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Subgrade reaction modulus Neural network Plate load test group method of data handling Clay

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group method of data handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log data Pre-Processing Techniques

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ENERGIES 2019年第8期12卷 1509页

作者： Shen, Chuanbo Asante-Okyere, Solomon Ziggah, Yao Yevenyo Wang, Liang Zhu, Xiangfeng China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Hubei Peoples R China China Univ Geosci Fac Earth Resources Dept Petr Geol Wuhan 430074 Hubei Peoples R China Univ Mines & Technol Fac Mineral Resource Technol Dept Geomat Engn Tarkwa 00233 Ghana

Although the group method of data handling (GMDH) is a self-organizing metaheuristic neural network capable of developing a classification function using influential input variables, the results can be improved by using some pre-processing steps. In this paper, we propose a joint principal component analysis (PCA) and GMDH (PCA-GMDH) classifier method. We investigated well log data pre-processing techniques composed of dimensionality reduction (DR) and wavelet analysis (WA), using the southern basin of the South Yellow Sea as a case study, with the aim of improving the lithology classification accuracy of the GMDH. Our results showed that the dimensionality reduction method, which is composed of PCA and linear discriminant analysis (LDA), minimized the complexity of the classifier by reducing the number of well log suites to the relevant components and factors. On the other hand, the WA decomposed the well log signals into time-frequency wavelets for the GMDH algorithm. Of all the pre-processing methods, only the PCA was able to significantly increase the classification accuracy rate of the GMDH. Finally, the proposed joint PCA-GMDH classifier not only increased the accuracy but also was able to distinguish between all the classes of lithofacies present in the southern basin of the South Yellow Sea.

关键词： group method of data handling principal component analysis linear discriminant analysis wavelet analysis lithology

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Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using group method of data handling

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CHAOS SOLITONS & FRACTALS 2020年 140卷 110191-110191页

作者： Vaishnav, Vaibhav Vajpai, Jayashri Indian Inst Technol Dept Elect Engn Jodhpur 342037 Rajasthan India MBM Engn Coll Dept Elect Engn Jodhpur 342011 Rajasthan India

Ever since the outbreak of novel coronavirus in December 2019, lockdown has been identified as the only effective measure across the world to stop the community spread of this pandemic. India implemented a complete shutdown across the nation from March 25, 2020 as lockdown I and went on to extend it by giving timely partial relaxations in the form of lockdown II, III & IV. This paper statistically analyses the impact of relaxation during Lockdown III and IV on coronavirus disease (COVID) spread in India using the group method of data handling (GMDH) to forecast the number of active cases using time series analysis and hence the required medical infrastructure for the period of next six months. The group method of data handling is a novel self organized data mining technique with data driven adaptive learning capability which grasps the auto correlative relations between the samples and gives a high forecasting accuracy irrespective of the length and stochasticity of a time series. The GMDH model has been first validated and standardized by forecasting the number of active and confirmed cases during lockdown III-IV with an accuracy of 2.58% and 2.00% respectively. Thereafter, the number of active cases has been forecasted for the rest of 2020 to predict the impact of lockdown relaxation on spread of COVID-19 and indicate preparatory measures necessary to counter it. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： COVID-19 Time series forecasting group method of data handling

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Predictive Modeling of DWT-decomposed ALS-EMG Features Using group method of data handling 2

Predictive Modeling of DWT-decomposed ALS-EMG Features Using...

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2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Khan, Muhammad Asad Ullah Dilshad, Azhar Anwar, Abdul Rauf Natl Univ Sci & Technol Biomed Engn Res Grp RCMS Islamabad Pakistan Hamdard Univ Fac Engn Sci & Technol Dept Comp Engn Karachi Pakistan Univ Engn & Technol Lahore Sub Campus Ctr Biomed Engn Kala Shah Kaku Pakistan

ISBN: (纸本)9781538695098

Identification of Amyotrophic Lateral Sclerosis disorder using electromyography signal data requires accurate classification models. Traditional approaches often necessitate manual model control and do not provide the designer with a set of practical classification equations. In this paper, a group method of data handling-based methodology is proposed which automatically builds the model by heuristically estimating optimum quadratic polynomial equations from available empirical data. Based on guidelines in literature and diligent trial and error assessment, a reduced set of most favorable features was tuned and assembled after extraction from a selected sub-band of Discrete Wavelet Transform decompositions. The generated model equations are an approximate representation of relationship between training data and corresponding class labels. For evaluation of prediction performance, computational experiments were conducted on test data and results were compiled on the grounds of Leave-one-out cross-validation criterion. The trained model effectively identified diseased and healthy data groups with Sensitivity, Specificity and Accuracy of 90.34%, 94.89% and 92.55% which is an improvement of 0.68%, 2.92% and 1.77% respectively, over the results of a conventional Multilayer Perceptron classifier tested under similar experimental conditions. These outcomes indicate that the proposed approach could be extended to real clinical setting to assist the clinicians in making precise diagnosis and potentially compete with leading automated diagnostic support systems.

关键词： Amyotrophic Lateral Sclerosis Discrete Wavelet Transform Feature Extraction group method of data handling Self-organizing Networks Pattern Recognition

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Reservoir water level forecasting using group method of data handling

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ACTA GEOPHYSICA 2018年第4期66卷 717-730页

作者： Zaji, Amir Hossein Bonakdari, Hossein Gharabaghi, Bahram Razi Univ Dept Civil Engn Kermanshah Iran Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models' performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models' applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L (-1)) and (L, L-1, L-12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L (-7)) and (L, L-7, L-14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://***/st. Accordingly, (L, L-1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.

关键词： Daily reservoir water level forecasting group method of data handling Input combination Soft computing Time-series prediction

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Elasticity modulus of clayey deposits estimation using group method of data handling type neural network

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MEASUREMENT 2018年 121卷 335-343页

作者： Naeini, Seyed Abolhasan Moayed, Reza Ziaie Kordnaeij, Afshin Mola-Abasi, Hossein Imam Khomeini Int Univ Dept Civil Engn Qazvin Iran Gonbadekavous Univ Dept Civil Engn Gonbadekavous Iran

Elasticity modulus (E-s) is used to predict the elastic settlement of foundations. Plate load test (PLT) is one of the most commonly applied methods to determine this parameter. Given that the method is relatively costly and time-consuming, especially in depths, various empirical equations have been proposed to relate the E-s to the results of standard penetration test (SPT) which is one of the most frequently used tests during the geotechnical investigation. Considering that the existing relationships are not able to estimate Es properly, in the present research, group method of data handling (GMDH) type neural network (NN) is used to estimate the E-s of clayey deposits based on a database including 131 plate load test (PLT) and standard penetration test (SPT) from geotechnical investigation sites in Qazvin, Iran. The comparison between proposed models and existing empirical equations indicate that the GMDH models exhibit higher performance (32-42% improvement) with respect to the other correlations. The sensitivity analysis of the developed models has been conducted to determine the contributions of the parameters affecting E-s and shows that N-60 is the most influential parameter on the GMDH-based models to predict E-s. Also, it has been demonstrated that the E-s predicted by the best model is significantly affected by error in the measurement of the LL value. Therefore, measuring this parameter in the laboratory requires high accuracy.

关键词： Elasticity modulus Plate load test Standard penetration test Clayey deposit group method of data handling Sensitivity analysis

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Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2018年 171卷 1211-1222页

作者： Sadi, Maryam Shahrabadi, Abbas RIPI West BlvdAzadi Sport ComplexPOB 14665-137 Tehran Iran

Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively.

关键词： Asphaltene precipitation group method of data handling Genetic algorithm SARA fractions Leverage approach

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Mathematical Model of the Phase Diagrams of Ionic Liquids-Based Aqueous Two-Phase Systems Using the group method of data handling and Artificial Neural Networks

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THEORETICAL FOUNDATIONS OF CHEMICAL ENGINEERING 2018年第1期52卷 146-155页

作者： Shahriari, Shahla Atashrouz, Saeid Pazuki, Gholamreza Islamic Azad Univ Shahr E Qods Branch Dept Chem Engn Tehran Iran Amirkabir Univ Technol Dept Chem Engn Tehran Iran

Development of ionic liquid-based aqueous two-phase systems as a new viewpoint in the expansion of research in the field of biological materials separation depends on accurate determination of phase diagram. In this work, the efficiency of artificial neural network was studied aiming to forecast the formation possibility of phase diagrams of aqueous two-phases systems for the ability of range of ionic liquids composed of different anions with a selected salt. In order to investigate effects of the anion of ionic liquids on phase diagram, this study was performed on 472 of experimental data. On the basis of the accurate set of statistical measurements obtained, a good agreement between the experimental data points and the predicted values was gained. Furthermore, the group method of data handling was applied to model the molality of ionic liquids and a reasonable agreement was obtained between experimental data and the predicted values of this model.

关键词： aqueous two-phase systems ionic liquids hydrogen-bond basicity artificial neural networks group method of data handling

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