Characteristics Of Nonlinear Data Research Articles

Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model

Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction.

A novel quality prediction method based on feature selection considering high dimensional product quality data

<p style='text-indent:20px;'>Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor.</p>

Nonlinear Anomaly Detection Based on Spectral–Spatial Composite Kernel for Hyperspectral Images

Anomaly detection is an important task among hyperspectral applications, which detects the anomalous targets that are spectrally different from their surroundings. Most of the anomaly detectors construct the detection models in the linear space, where the nonlinear characteristics of data are not taken into consideration. The kernel-based methods, which implicitly map the data into a high-dimensional feature space, have shown great potential in dealing with nonlinear problems. Nevertheless, the original kernel-based detection methods only exploit the spectral features without taking advantage of the spatial correlations among adjacent pixels. In this letter, a nonlinear spectral-spatial composite kernel-based detector (SSCKD) is proposed for hyperspectral anomaly detection. First, superpixel segmentation is adopted to extract spatial features according to local homogeneity. Then, considering the nonlinear characteristics in the hyperspectral data, spectral and spatial features are combined by the composite kernel. Finally, an iterative composite kernel-learning procedure based on the centered kernel alignment is designed to adaptively determine the kernel weights. The experiments are conducted on three real hyperspectral data sets. The detection results demonstrate the superiority of the proposed method in comparison with the conventional and state-of-the-art anomaly detection methods.

Design of Fuzzy Prediction System based on Dual Tuning using Enhanced Genetic Algorithms

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

A data mining-based subset selection for enhanced discrimination using iterative elimination of redundancy

The presence of redundant or irrelevant features in data mining may result in a mask of underlying patterns. Thus one often reduces the number of features by applying a feature selection technique. The objective of feature selection is to get a feature subset that has the best performance. This work proposes a new feature selection method using orthogonal filtering and nonlinear representation of data for an enhanced discrimination performance. An orthogonal filtering is implemented to remove unwanted variation of data. The proposed method adopts kernel principal component analysis, one of nonlinear kernel methods, to extract nonlinear characteristics of data and to reduce the dimensionality of data. The proposed feature selection method is based on the selection criterion of linear discriminant analysis in an environment of iterative backward feature elimination. The performance of the proposed method is compared with those of three different methods. The results showed that it outperforms the three methods. The use of filtering and a kernel method was shown to be a promising tool for an efficient feature selection.