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In the paper, a novel kernel recursive least-squares (KRLS) algorithm named random Fourier feature kernel recursive maximum mixture correntropy (RFF-RMMC) algorithm is proposed, which improves the prediction efficiency and robustness of the KRLS algorithm. Random Fourier feature (RFF) method as well as maximum mixture correntropy criterion (MMCC) are combined and applied into KRLS algorithm afterwards. Using RFF to approximate the kernel function in KRLS with a fixed cost can greatly reduce the computational complexity and simultaneously improve the prediction efficiency. In addition, the MMCC maintains the robustness like the maximum correntropy criterion (MCC). More importantly, it can enhance the accuracy of the similarity measurement between predicted and true values by more flexible parameter settings, and then make up for the loss of prediction accuracy caused by RFF to a certain extent. The performance of the RFF-RMMC algorithm for online time series prediction is verified by the simulation results based on three datasets.
Xinghan Xu; Weijie Ren. Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction. ISA Transactions 2021, 1 .
AMA StyleXinghan Xu, Weijie Ren. Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction. ISA Transactions. 2021; ():1.
Chicago/Turabian StyleXinghan Xu; Weijie Ren. 2021. "Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction." ISA Transactions , no. : 1.
Kernel least mean square (KLMS) algorithm is a popular method for time series online prediction. It has the advantages of good robustness, low computational complexity, model simplicity and online learning ability. Unfortunately, as input data grows, the dictionary size increases and the computational complexity raises significantly. In addition, how to improve the adaptability in time-varying environments with noise is also one of the main challenges. Therefore, we propose an improved KLMS algorithm from sparse perspective in response to the above problems, called adaptive sparse quantization kernel least mean square (ASQ-KLMS) algorithm. In the new model, sequential outlier criterion for sparsification and weights adaptive adjustment are combined with coherence criterion and quantization to form ASQ-KLMS algorithm. Firstly, it makes full use of effective information and ignores the interference of abnormal information to obtain a more accurate and compact dictionary. Then, a good balance between algorithm efficiency and accuracy can be achieved by controlling the choice of parameters. In addition, it can adaptively adjust weights in time-varying environment. At last, the Lorenz chaotic time series, the ENSO chaotic time series and the Beijing \(\text {P}{{\text {M}}_{2.5}}\) chaotic time series are used to prove the reliability of the ASQ-KLMS algorithm.
Chaochao Zhao; Weijie Ren; Min Han. Adaptive Sparse Quantization Kernel Least Mean Square Algorithm for Online Prediction of Chaotic Time Series. Circuits, Systems, and Signal Processing 2021, 1 -24.
AMA StyleChaochao Zhao, Weijie Ren, Min Han. Adaptive Sparse Quantization Kernel Least Mean Square Algorithm for Online Prediction of Chaotic Time Series. Circuits, Systems, and Signal Processing. 2021; ():1-24.
Chicago/Turabian StyleChaochao Zhao; Weijie Ren; Min Han. 2021. "Adaptive Sparse Quantization Kernel Least Mean Square Algorithm for Online Prediction of Chaotic Time Series." Circuits, Systems, and Signal Processing , no. : 1-24.
For echo state networks, it is difficult to select suitable reservoir parameters for different applications. In this paper, we put forward a divided adaptive multi-objective differential evolution (DAMODE) algorithm to optimize the reservoir parameters of echo state network. To improve the performance of multi-objective differential evolution algorithm, the entire population is divided into several subpopulations, and each subpopulation is divided into two subsets to compromise convergence and diversity, which are updated according to certain rules. Besides, the scale factor and crossover rate of differential evolutionary algorithm are adaptively adjusted. Experiments were conducted on the Lorenz time series, hourly temperature time series and PM2.5 time series in Beijing. Experiment results show that the proposed model can improve prediction accuracy and has good generalization ability and practicability.
Weijie Ren; Yiwen Wang; Min Han. Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm. Soft Computing 2021, 25, 4489 -4502.
AMA StyleWeijie Ren, Yiwen Wang, Min Han. Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm. Soft Computing. 2021; 25 (6):4489-4502.
Chicago/Turabian StyleWeijie Ren; Yiwen Wang; Min Han. 2021. "Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm." Soft Computing 25, no. 6: 4489-4502.
In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning (MTL) is an effective inductive bias method to utilize latent shared features and discover the structural relationships from related tasks. Base on this concept, we propose a novel MTL model for multivariate chaotic time-series prediction, which could learn both dynamic-shared and dynamic-specific patterns. We implement the dynamic analysis of multiple time series through a special network structure design. The model could disentangle the complex relationships among multivariate chaotic time series and derive the common evolutionary trend of the multivariate chaotic dynamical system by inductive bias. We also develop an efficient Crank--Nicolson-like curvilinear update algorithm based on the alternating direction method of multipliers (ADMM) for the nonconvex nonsmooth Stiefel optimization problem. Simulation results and analysis demonstrate the effectiveness on dynamic-shared pattern discovery and prediction performance.
Shoubo Feng; Min Han; Jiadong Zhang; Tie Qiu; Weijie Ren. Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction. IEEE Transactions on Cybernetics 2020, PP, 1 -11.
AMA StyleShoubo Feng, Min Han, Jiadong Zhang, Tie Qiu, Weijie Ren. Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction. IEEE Transactions on Cybernetics. 2020; PP (99):1-11.
Chicago/Turabian StyleShoubo Feng; Min Han; Jiadong Zhang; Tie Qiu; Weijie Ren. 2020. "Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction." IEEE Transactions on Cybernetics PP, no. 99: 1-11.
With the rapid development of information theoretic learning, the maximum correntropy criterion (MCC) has been widely used in time series prediction area. Especially, the kernel recursive least squares (KRLS) based on MCC is studied recently due to its online recursive form and the ability to resist noise and be robust in non-Gaussian environments. However, it is not always an optimal choice that using the correntropy, which is calculated by default Gaussian kernel function, to describe the local similarity between variables. Besides, the computational burden of MCC based KRLS will raise as data size increases, thus causing difficulties in accommodating time-varying environments. Therefore, this paper proposes a quantized generalized MCC (QGMCC) to solve the above problem. Specifically, a generalized MCC (GMCC) is utilized to enhance the accuracy and flexibility in calculating the correntropy. In order to solve the problem of computational complexity, QGMCC quantizes the input space and upper bounds the network size by vector quantization (VQ) method. Furthermore, QGMCC is applied to KRLS and forming a computationally efficient and precisely predictive algorithm. After that, the improved algorithm named quantized kernel recursive generalized maximum correntropy (QKRGMC) is set up and the derivation process is also given. Experimental results of one benchmark dataset and two real-world datasets are present to verify the effectiveness of the online prediction algorithm.
Tianyu Shen; Weijie Ren; Min Han. Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction. Engineering Applications of Artificial Intelligence 2020, 95, 103797 .
AMA StyleTianyu Shen, Weijie Ren, Min Han. Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction. Engineering Applications of Artificial Intelligence. 2020; 95 ():103797.
Chicago/Turabian StyleTianyu Shen; Weijie Ren; Min Han. 2020. "Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction." Engineering Applications of Artificial Intelligence 95, no. : 103797.
Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used in time-series prediction, but when using ESN, two crucial problems have to be confronted: 1) how to select the optimal subset of input features and 2) how to set the suitable parameters of the model. To solve this problem, the modified biogeography-based optimization ESN (MBBO-ESN) system is proposed for system modeling and multivariate time-series prediction, which can simultaneously achieve feature subset selection and model parameter optimization. The proposed MBBO algorithm is an improved evolutionary algorithm based on biogeography-based optimization (BBO), which utilizes an S-type population migration rate model, a covariance matrix migration strategy, and a Lévy distribution mutation strategy to enhance the rotation invariance and exploration ability. Furthermore, the MBBO algorithm cannot only optimize the key parameters of the ESN model but also uses a hybrid-metric feature selection method to remove the redundancies and distinguish the importance of the input features. Compared with the traditional methods, the proposed MBBO-ESN system can discover the relationship between the input features and the model parameters automatically and make the prediction more accurate. The experimental results on the benchmark and real-world datasets demonstrate that MBBO outperforms the other traditional evolutionary algorithms, and the MBBO-ESN system is more competitive in multivariate time-series prediction than other classic machine-learning models.
Xiaodong Na; Min Han; Weijie Ren; Kai Zhong. Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization. IEEE Transactions on Cybernetics 2020, 1 -11.
AMA StyleXiaodong Na, Min Han, Weijie Ren, Kai Zhong. Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization. IEEE Transactions on Cybernetics. 2020; (99):1-11.
Chicago/Turabian StyleXiaodong Na; Min Han; Weijie Ren; Kai Zhong. 2020. "Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization." IEEE Transactions on Cybernetics , no. 99: 1-11.
The causality analysis is an important research topic in time series data mining. Granger causality analysis is a powerful method that determines cause and effect based on predictability. However, the traditional Granger causality is limited to analyzing linear causality between bivariate time series, because it is based on vector autoregressive models. In this paper, we propose a novel method, named Hilbert–Schmidt independence criterion Lasso Granger causality (HSIC-Lasso-GC), for revealing nonlinear causality between multivariate time series. Firstly, for each time series, we perform stationarity test and state space reconstruction to extract the historical information. Then, we build a HSIC-Lasso model of all input variables and output variable, where the optimal model is selected by generalized information criterion. Finally, according to the significance test, we get the causality analysis results from all input variables to output variable. In the simulations, we use two benchmark datasets and two actual datasets to test the effectiveness of the proposed method. The results show that the proposed method can effectively analyze nonlinear causality between multivariate time series.
Weijie Ren; Baisong Li; Min Han. A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series. Physica A: Statistical Mechanics and its Applications 2019, 541, 123245 .
AMA StyleWeijie Ren, Baisong Li, Min Han. A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series. Physica A: Statistical Mechanics and its Applications. 2019; 541 ():123245.
Chicago/Turabian StyleWeijie Ren; Baisong Li; Min Han. 2019. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series." Physica A: Statistical Mechanics and its Applications 541, no. : 123245.
With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.
Xinghan Xu; Weijie Ren. Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China. Sustainability 2019, 11, 3096 .
AMA StyleXinghan Xu, Weijie Ren. Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China. Sustainability. 2019; 11 (11):3096.
Chicago/Turabian StyleXinghan Xu; Weijie Ren. 2019. "Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China." Sustainability 11, no. 11: 3096.
Noises and outliers are commonly exist in dynamical systems because of sensor disturbations or extreme dynamics. Thus, the robustness and generalization capacity are of vital importance for system modeling. In this paper, the robust manifold broad learning system(RM-BLS) is proposed for system modeling and large-scale noisy chaotic time series prediction. Manifold embedding is utilized for chaotic system evolution discovery. The manifold representation is randomly corrupted by perturbations while the features not related to low-dimensional manifold embedding are discarded by feature selection. It leads to a robust learning paradigm and achieves better generalization performance. We also develop an efficient solution for Stiefel manifold optimization, in which the orthogonal constraints are maintained by Cayley transformation and curvilinear search algorithm. Furthermore, we discuss the common thoughts between random perturbation approximation and other mainstream regularization methods. We also prove the equivalence between perturbations to manifold embedding and Tikhonov regularization. Simulation results on large-scale noisy chaotic time series prediction illustrates the robustness and generalization performance of our method.
Shoubo Feng; Weijie Ren; Min Han; Yen Wei Chen. Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective. Neural Networks 2019, 117, 179 -190.
AMA StyleShoubo Feng, Weijie Ren, Min Han, Yen Wei Chen. Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective. Neural Networks. 2019; 117 ():179-190.
Chicago/Turabian StyleShoubo Feng; Weijie Ren; Min Han; Yen Wei Chen. 2019. "Robust manifold broad learning system for large-scale noisy chaotic time series prediction: A perturbation perspective." Neural Networks 117, no. : 179-190.
The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.
Xinghan Xu; Weijie Ren; Xu; Ren. A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction. Symmetry 2019, 11, 610 .
AMA StyleXinghan Xu, Weijie Ren, Xu, Ren. A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction. Symmetry. 2019; 11 (5):610.
Chicago/Turabian StyleXinghan Xu; Weijie Ren; Xu; Ren. 2019. "A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction." Symmetry 11, no. 5: 610.
Air pollution has become a global environmental problem, because it has a great adverse impact on human health and the climate. One way to explore this problem is to monitor and predict air quality index in an economical way. Accurate monitoring and prediction of air quality index (AQI), e.g., PM2.5 concentration, is a challenging task. In order to accurately predict the PM2.5 time series, we propose a supplementary leaky integrator echo state network (SLI-ESN) in this paper. It adds the historical state term of the historical moment to the calculation of leaky integrator reservoir, which improves the influence of historical evolution state on the current state. Considering the redundancy and correlation between multivariable time series, minimum redundancy maximum relevance (mRMR) feature selection method is introduced to reduce redundant and irrelevant information, and increase computation speed. A variety of evaluation indicators are used to assess the overall performance of the proposed method. The effectiveness of the proposed model is verified by the experiment of Beijing PM2.5 time series prediction. The comparison of learning time also shows the efficiency of the algorithm.
Xinghan Xu; Weijie Ren. Prediction of Air Pollution Concentration Based on mRMR and Echo State Network. Applied Sciences 2019, 9, 1811 .
AMA StyleXinghan Xu, Weijie Ren. Prediction of Air Pollution Concentration Based on mRMR and Echo State Network. Applied Sciences. 2019; 9 (9):1811.
Chicago/Turabian StyleXinghan Xu; Weijie Ren. 2019. "Prediction of Air Pollution Concentration Based on mRMR and Echo State Network." Applied Sciences 9, no. 9: 1811.
Electroencephalogram (EEG) signals play an important role in clinical diagnosis and cognitive neuroscience. Automatic classification of EEG signals is gradually becoming the research focus, which contains two procedures: feature extraction and classification. In the phase of feature extraction, a hybrid feature extraction method is proposed and the features are derived by performing linear and nonlinear feature extraction methods, which can describe abundant properties of original EEG signals. In order to eliminate irrelevant and redundant features, feature selection based on class separability is employed to select the optimal feature subset. In the phase of classification, this paper presents a novel ensemble extreme learning machine based on linear discriminant analysis. Linear discriminant analysis is used to transform training subsets that are generated by bootstrap method, through which we can increase the differences of basic classifiers and reduce generalization errors of ensemble extreme learning machine. Experiments on two different EEG datasets are conducted in this study. Class separability is investigated to verify the effectiveness of feature extraction methods. The overall classification results show that compared with other similar studies, the proposed method can significantly enhance the performance of EEG signals classification.
Weijie Ren; Min Han. Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Processing Letters 2018, 50, 1281 -1301.
AMA StyleWeijie Ren, Min Han. Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Processing Letters. 2018; 50 (2):1281-1301.
Chicago/Turabian StyleWeijie Ren; Min Han. 2018. "Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine." Neural Processing Letters 50, no. 2: 1281-1301.
State space reconstruction is the foundation of chaotic system modeling. Selection of reconstructed variables is essential to the analysis and prediction of multivariate chaotic time series. As most existing state space reconstruction theorems deal with univariate time series, we have presented a novel nonuniform state space reconstruction method using information criterion for multivariate chaotic time series. We derived a new criterion based on low dimensional approximation of joint mutual information for time delay selection, which can be solved efficiently through the use of an intelligent optimization algorithm with low computation complexity. The embedding dimension is determined by conditional entropy, after which the reconstructed variables have relatively strong independence and low redundancy. The scheme, which integrates nonuniform embedding and feature selection, results in better reconstructions for multivariate chaotic systems. Moreover, the proposed nonuniform state space reconstruction method shows good performance in forecasting benchmark and actual multivariate chaotic time series.
Min Han; Weijie Ren; Meiling Xu; Tie Qiu. Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series. IEEE Transactions on Cybernetics 2018, 49, 1885 -1895.
AMA StyleMin Han, Weijie Ren, Meiling Xu, Tie Qiu. Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series. IEEE Transactions on Cybernetics. 2018; 49 (5):1885-1895.
Chicago/Turabian StyleMin Han; Weijie Ren; Meiling Xu; Tie Qiu. 2018. "Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series." IEEE Transactions on Cybernetics 49, no. 5: 1885-1895.
In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.
Min Han; Ruiquan Zhang; Tie Qiu; Meiling Xu; Weijie Ren. Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017, 49, 2144 -2154.
AMA StyleMin Han, Ruiquan Zhang, Tie Qiu, Meiling Xu, Weijie Ren. Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017; 49 (10):2144-2154.
Chicago/Turabian StyleMin Han; Ruiquan Zhang; Tie Qiu; Meiling Xu; Weijie Ren. 2017. "Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 10: 2144-2154.
Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.
Weijie Ren; Min Han; Jun Wang; Dan Wang; Tieshan Li. Efficient feature extraction framework for EEG signals classification. 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP) 2016, 167 -172.
AMA StyleWeijie Ren, Min Han, Jun Wang, Dan Wang, Tieshan Li. Efficient feature extraction framework for EEG signals classification. 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP). 2016; ():167-172.
Chicago/Turabian StyleWeijie Ren; Min Han; Jun Wang; Dan Wang; Tieshan Li. 2016. "Efficient feature extraction framework for EEG signals classification." 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP) , no. : 167-172.
A new learning framework is proposed for multivariate chaotic system modeling. In order to construct suitable input variables, we put forward a scheme of input variable selection based on nonuniform state space reconstruction. A new criteria based on low dimensional approximation of joint mutual information is derived, which is solved by evolutionary computation approach efficiently with low computation complexity. Then, echo state network is adopted as prediction model, which has powerful capability for nonlinear predicting. To improve generalization performance and stability of the predictive model, we introduce feature selection in the training process. Feature selection method can control complexity of the network and prevent overfitting. The model is applied to the prediction of real world time series. The simulation results show the effectiveness and practicality of the proposed method.
Weijie Ren; Min Han. Multivariate chaotic system modeling based on nonuniform state space reconstruction and echo state network. 2015 Chinese Automation Congress (CAC) 2015, 841 -846.
AMA StyleWeijie Ren, Min Han. Multivariate chaotic system modeling based on nonuniform state space reconstruction and echo state network. 2015 Chinese Automation Congress (CAC). 2015; ():841-846.
Chicago/Turabian StyleWeijie Ren; Min Han. 2015. "Multivariate chaotic system modeling based on nonuniform state space reconstruction and echo state network." 2015 Chinese Automation Congress (CAC) , no. : 841-846.
Feature selection is an important preprocessing step in data mining. Mutual information-based feature selection is a kind of popular and effective approaches. In general, most existing mutual information-based techniques are greedy methods, which are proven to be efficient but suboptimal. In this paper, mutual information-based feature selection is transformed into a global optimization problem, which provides a new idea for solving feature selection problems. First, a single-objective feature selection algorithm combining relevance and redundancy is presented, which has well global searching ability and high computational efficiency. Furthermore, to improve the performance of feature selection, we propose a multi-objective feature selection algorithm. The method can meet different requirements and achieve a tradeoff among multiple conflicting objectives. On this basis, a hybrid feature selection framework is adopted for obtaining a final solution. We compare the performance of our algorithm with related methods on both synthetic and real datasets. Simulation results show the effectiveness and practicality of the proposed method.
Min Han; Weijie Ren. Global mutual information-based feature selection approach using single-objective and multi-objective optimization. Neurocomputing 2015, 168, 47 -54.
AMA StyleMin Han, Weijie Ren. Global mutual information-based feature selection approach using single-objective and multi-objective optimization. Neurocomputing. 2015; 168 ():47-54.
Chicago/Turabian StyleMin Han; Weijie Ren. 2015. "Global mutual information-based feature selection approach using single-objective and multi-objective optimization." Neurocomputing 168, no. : 47-54.
Min Han; Weijie Ren; Xiaoxin Liu. Joint mutual information-based input variable selection for multivariate time series modeling. Engineering Applications of Artificial Intelligence 2015, 37, 250 -257.
AMA StyleMin Han, Weijie Ren, Xiaoxin Liu. Joint mutual information-based input variable selection for multivariate time series modeling. Engineering Applications of Artificial Intelligence. 2015; 37 ():250-257.
Chicago/Turabian StyleMin Han; Weijie Ren; Xiaoxin Liu. 2015. "Joint mutual information-based input variable selection for multivariate time series modeling." Engineering Applications of Artificial Intelligence 37, no. : 250-257.
A complete learning framework for modeling multivariate time series is presented in this paper. First, in order to construct input variables, variable selection method based on max dependency criterion is introduced, which can remove redundant and irrelevant variables. Then, Gaussian process model is adopted as prediction model, which has powerful capability of nonlinear modeling. In addition, confidence and confidence intervals are built for the evaluation of predictive results. Finally, the model is applied to the prediction of real world multivariate time series. The simulation results show the effectiveness and practicality of the proposed method.
Weijie Ren; Min Han; Ren Weijie; Han Min. Modeling of multivariate time series using variable selection and Gaussian process. Proceedings of the 33rd Chinese Control Conference 2014, 5071 -5074.
AMA StyleWeijie Ren, Min Han, Ren Weijie, Han Min. Modeling of multivariate time series using variable selection and Gaussian process. Proceedings of the 33rd Chinese Control Conference. 2014; ():5071-5074.
Chicago/Turabian StyleWeijie Ren; Min Han; Ren Weijie; Han Min. 2014. "Modeling of multivariate time series using variable selection and Gaussian process." Proceedings of the 33rd Chinese Control Conference , no. : 5071-5074.
In this paper, we present an echo state network model based on sparse Gaussian process regression, which has been successfully applied to multivariate time series prediction. While combining the Gaussian process with Echo State Network, the computational complexity of the model is very high. We consider using a group of limited basis functions instead of the original covariance function, which reduces the computational complexity and maintains the prediction performance of the model. In the framework of Bayesian inference, the model can combine prior knowledge and observation data perfectly and provide prediction confidence. The model realizes adaptive estimation of the hyper-parameters by using maximum likelihood approach and avoids complex computation process. Two simulation results show the effectiveness and practicality of the proposed method.
Min Han; Weijie Ren; Meiling Xu. Prediction of multivariate time series with sparse Gaussian process echo state network. 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP) 2013, 510 -513.
AMA StyleMin Han, Weijie Ren, Meiling Xu. Prediction of multivariate time series with sparse Gaussian process echo state network. 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP). 2013; ():510-513.
Chicago/Turabian StyleMin Han; Weijie Ren; Meiling Xu. 2013. "Prediction of multivariate time series with sparse Gaussian process echo state network." 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP) , no. : 510-513.