Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems
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Chris J. Harris | Shan Liang | Tong Liu | Sheng Chen | C. Harris | Shan Liang | Sheng Chen | Tong Liu
[1] P. X. Liu,et al. Multiinnovation Least-Squares Identification for System Modeling , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[2] E. Lorenz. Deterministic nonperiodic flow , 1963 .
[3] Chris J. Harris,et al. Selective ensemble of multiple local model learning for nonlinear and nonstationary systems , 2020, Neurocomputing.
[4] Marcus A. Maloof,et al. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..
[5] Jerzy Stefanowski,et al. Combining block-based and online methods in learning ensembles from concept drifting data streams , 2014, Inf. Sci..
[6] Jialin Liu,et al. Nonstationary fault detection and diagnosis for multimode processes , 2009 .
[7] Narasimhan Sundararajan,et al. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.
[8] João Gama,et al. On evaluating stream learning algorithms , 2012, Machine Learning.
[9] Carlo Zaniolo,et al. Fast and Light Boosting for Adaptive Mining of Data Streams , 2004, PAKDD.
[10] Yu Gong,et al. A Fast Adaptive Tunable RBF Network For Nonstationary Systems , 2016, IEEE Transactions on Cybernetics.
[11] S.. Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning , 2004 .
[12] Sheng Chen,et al. Recursive prediction error parameter estimator for non-linear models , 1989 .
[13] Xin Yao,et al. Online Ensemble Learning of Data Streams with Gradually Evolved Classes , 2016, IEEE Transactions on Knowledge and Data Engineering.
[14] Dongsoo Han,et al. Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks , 2016, IEEE Transactions on Mobile Computing.
[15] João Gama,et al. Ensemble learning for data stream analysis: A survey , 2017, Inf. Fusion.
[16] Elias Akkari,et al. Global linearizing control of MIMO microwave-assisted thawing , 2009 .
[17] Kyosuke Nishida,et al. Adaptive Classifiers-Ensemble System for Tracking Concept Drift , 2007, 2007 International Conference on Machine Learning and Cybernetics.
[18] Yu Gong,et al. A new adaptive multiple modelling approach for non-linear and non-stationary systems , 2016, Int. J. Syst. Sci..
[19] Slobodan Vucetic,et al. Tracking Concept Change with Incremental Boosting by Minimization of the Evolving Exponential Loss , 2011, ECML/PKDD.
[20] Visakan Kadirkamanathan,et al. A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.
[21] Stephen Grossberg,et al. Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.
[22] Tingwen Huang,et al. Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[23] Bogdan Gabrys,et al. Local learning‐based adaptive soft sensor for catalyst activation prediction , 2011 .
[24] Witold Pedrycz,et al. Evolving Ensemble Fuzzy Classifier , 2017, IEEE Transactions on Fuzzy Systems.
[25] Leszek Rutkowski,et al. Generalized regression neural networks in time-varying environment , 2004, IEEE Transactions on Neural Networks.
[26] Gregory Ditzler,et al. Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.
[27] Qingyu Xiong,et al. Two-Stage Method for Diagonal Recurrent Neural Network Identification of a High-Power Continuous Microwave Heating System , 2019, Neural Processing Letters.
[28] Robert Givan,et al. Online Ensemble Learning: An Empirical Study , 2000, Machine Learning.
[29] Stephen A. Billings,et al. Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG , 2016, Int. J. Syst. Sci..
[30] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[31] Xingjian Jing,et al. Online Identification of Nonlinear Stochastic Spatiotemporal System With Multiplicative Noise by Robust Optimal Control-Based Kernel Learning Method , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[32] Javier Del Ser,et al. Evolving Spiking Neural Networks for online learning over drifting data streams , 2018, Neural Networks.
[33] Xin Yao,et al. The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift , 2010, IEEE Transactions on Knowledge and Data Engineering.
[34] John C. Platt. A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.
[35] K Lehnertz,et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[36] Tong Liu,et al. Learning to Detect Local Overheating of the High-Power Microwave Heating Process With Deep Learning , 2018, IEEE Access.
[37] Jiannan Li,et al. Research of uniformity evaluation model based on entropy clustering in the microwave heating processes , 2016, Neurocomputing.
[38] Graham J. Williams,et al. Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum] , 2014, IEEE Computational Intelligence Magazine.
[39] Ping Wang,et al. Online soft sensor design using local partial least squares models with adaptive process state partition , 2015 .
[40] Marcus A. Maloof,et al. Using additive expert ensembles to cope with concept drift , 2005, ICML.
[41] Sheng Chen,et al. Adaptive Soft Sensor Development for Multi-Output Industrial Processes Based on Selective Ensemble Learning , 2018, IEEE Access.
[42] Morimasa Ogawa,et al. The state of the art in chemical process control in Japan: Good practice and questionnaire survey , 2010 .
[43] Lei Chen,et al. Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.
[44] Shang-Liang Chen,et al. Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.
[45] Tong Liu,et al. Adaptive Critic Based Optimal Neurocontrol of a Distributed Microwave Heating System Using Diagonal Recurrent Network , 2018, IEEE Access.
[46] Shankar Vembu,et al. Chemical gas sensor drift compensation using classifier ensembles , 2012 .
[47] Yuan Lan,et al. Ensemble of online sequential extreme learning machine , 2009, Neurocomputing.
[48] Qingyu Xiong,et al. Coupled Electromagnetic and Heat Transfer ODE Model for Microwave Heating With Temperature-Dependent Permittivity , 2016, IEEE Transactions on Microwave Theory and Techniques.
[49] Qingyu Xiong,et al. Improved receding horizon H∞ temperature spectrum tracking control for Debye media in microwave heating process , 2018, Journal of Process Control.
[50] Hang Zhang,et al. Online Active Learning Ensemble Framework for Drifted Data Streams , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[51] Rui Araújo,et al. A dynamic and on-line ensemble regression for changing environments , 2015, Expert Syst. Appl..
[52] Johan Grasman,et al. Thermal runaway in microwave heating : a mathematical analysis , 2002 .