This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furtherm...
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This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types' ships and al
Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of supportvector regressio...
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Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of supportvectorregression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.
This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the improved torque balance method is used to obtain the accur...
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This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the improved torque balance method is used to obtain the accurate flux linkage data without redundant experiments. Secondly, two special data preprocessing steps are proposed, which are nonlinear preprocessing and angle mapping, respectively. The first step provides beneficial nonlinearity for algorithms, and the second step improves the linearity of flux linkage at small angles by the proposed mapping function. Thirdly, support vector regression algorithm optimized by the improved tuna swarm algorithm (ITSO-SVR) is employed to establish the flux linkage model. Based on the flux linkage model, the current and torque models are easily built by ITSO-SVR to complete the nonlinear modelling of SRM. Finally, the effectiveness of the proposed method is verified. The preprocessing method is verified to reduce the modeling difficulty. Besides, ITSO-SVR facilitates the swift and efficient modeling without any pre-storge or complex calculations. The experiments under the CCC and APC algorithms indicate that the established model exhibits high accuracy, fast speed and strong generalization capability.
Accurate state of charge (SOC) estimation is essential for the safe and reliable operation of Li-ion batteries. To solve the problem of poor generalisation caused by over-fitting, this paper presents a combination alg...
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Accurate state of charge (SOC) estimation is essential for the safe and reliable operation of Li-ion batteries. To solve the problem of poor generalisation caused by over-fitting, this paper presents a combination algorithm based on feature selection to estimate battery SOC. Firstly, a portion of the features is extracted from the extended Kalman filtering (EKF) results. It forms the set of features to be selected with four other measured features. Secondly, the optimal feature subset is adopted by designing a wrapped feature screening framework based on the Bayesian information criterion (BIC). Finally, the selected combination of features is adopted to train the supportvectorregression (SVR) model, which is applied to the battery SOC estimation. The experimental results reveal that the combination strategy of EKF and SVR improves the accuracy of SOC estimation. The optimal SVR model based on the feature selection criterion shows better generalisation. Better estimation results in four driving conditions are achieved, and the root-mean-square error of the battery SOC estimation is decreased by at least 64.1 % and 56.5 % compared to the EKF algorithm and SVR algorithm driven by full feature, respectively.
support vector regression algorithm is applied to colleges recruiting students prediction in the paper. As colleges recruiting students prediction is a nonlinear regression problem,the input training data of colle...
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support vector regression algorithm is applied to colleges recruiting students prediction in the paper. As colleges recruiting students prediction is a nonlinear regression problem,the input training data of colleges recruiting students are nonlinearly mapped into a high dimensional space in supportvectorregression model. The amount of colleges recruiting students of Sichuan province from 2000 to 2008 is used to prove the effectiveness of supportvectorregression ***,the forecasting curves of supportvectorregression method and BP neural network and the comparison of forecasting error for amount of colleges recruiting students between supportvectorregression method and BP neural network are given in this *** comparison results of forecasting error for amount of colleges recruiting students between supportvectorregression method and BP neural network indicate that supportvectorregression method has a higher forecasting accuracy than BP neural network.
Accurate prediction of the complicated nonlinear relationship among the grade efficiency, geometrical dimensions, and operating parameters based on limited experimental data is the most effective way to design a high-...
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Accurate prediction of the complicated nonlinear relationship among the grade efficiency, geometrical dimensions, and operating parameters based on limited experimental data is the most effective way to design a high-efficiency cyclone separator. Herein, a hybrid PCA-PSO-SVR model is proposed to predict the grade efficiency of cyclone separators with the operating parameters based on 217 sets of experimental data provided in the literature. The experimental data are preprocessed using the random sampling technique together with the normalization method and principal component analysis (PCA) at first;subsequently, the particle swarm optimization (PSO) algorithm is incorporated to optimize the parameters for the supportvectorregression (SVR), including the penalty factor C, kernel function parameter g, and insensitive loss c. Finally, the SVR model with the optimized parameters is trained with 80% pretreatment data, and the generalization ability of the model is tested with the remaining 20% data. The mean squared error of the test sets is 6.948 x 10(-4) with a correlation coefficient of 0.982. The comparison results show that the PCA-PSO-SVR model has higher accuracy, better generalization ability, and stronger robustness than the existing models for predicting the cyclone separator efficiency in the case with only a few experimental data. (C) 2019 Elsevier B.V. All rights reserved.
The forecast of the mine Ground-water-level is an issue with many influencing factors, highly non-linear and temporal series. SPR (supportvectorregression) is applied to forecast Coal Mine Ground-water-level in this...
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ISBN:
(纸本)9780769536002
The forecast of the mine Ground-water-level is an issue with many influencing factors, highly non-linear and temporal series. SPR (supportvectorregression) is applied to forecast Coal Mine Ground-water-level in this paper. Appropriate kernel function and parameters are chosen based on the analysis to SVR regressionalgorithm. This paper proposes the Forecasting Model of Coal Mine Ground-water-level basing on SVR regressionalgorithm and determines the forecast of the input factor and the output factor according to the physical geography and the hydrology geology situation of the chosen mining area. The numerical test results show that the forecast results have compatibility with the actual measurement result We verify that the forecast model of Coal Mine Ground-water-level has effect, and provide a new effective method to the Forecasting of Coal Mine Ground-water-level.
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