A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilayer feedforward networks are universal approximators
Neural Networks
On covariance function tests used in system identification
Automatica (Journal of IFAC) - Identification and system parameter estimation
A financial neural-network application
AI Expert
A resource-allocating network for function interpolation
Neural Computation
Neural networks and the bias/variance dilemma
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Nonlinear black-box models in system identification: mathematical foundations
Automatica (Journal of IFAC) - Special issue on trends in system identification
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
An equivalence between sparse approximation and support vector machines
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Properties of incremental projection learning
Neural Networks
Artificial Neural Networks for Civil Engineers: Advanced Features and Applications
Artificial Neural Networks for Civil Engineers: Advanced Features and Applications
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Systems Science
Backward Elimination Methods for Associative Memory Network Pruning
International Journal of Hybrid Intelligent Systems
Robust optimal experiment design for system identification
Automatica (Journal of IFAC)
International Journal of Systems Science
Regularization in the selection of radial basis function centers
Neural Computation
A set of novel correlation tests for nonlinear system variables
International Journal of Systems Science
Active learning with statistical models
Journal of Artificial Intelligence Research
Subset based least squares subspace regression in RKHS
Neurocomputing
Orthogonal forward selection for constructing the radial basis function network with tunable nodes
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Variable neural networks for adaptive control of nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Support vector machines for quality monitoring in a plastic injection molding process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Bioinformatics with soft computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
M-estimator and D-optimality model construction using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Input selection for nonlinear regression models
IEEE Transactions on Fuzzy Systems
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
Algorithms for deterministic balanced subspace identification
Automatica (Journal of IFAC)
Editorial: Introduction to the special issue on data-based modelling and system identification
Automatica (Journal of IFAC)
Identification of MIMO Hammerstein models using least squares support vector machines
Automatica (Journal of IFAC)
Decoupling the linear and nonlinear parts in Hammerstein model identification
Automatica (Journal of IFAC)
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
RBF principal manifolds for process monitoring
IEEE Transactions on Neural Networks
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Blind equalization using a predictive radial basis function neural network
IEEE Transactions on Neural Networks
Nonlinear adaptive control of interconnected systems using neural networks
IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks
Two-stage extreme learning machine for regression
Neurocomputing
Global models for patient-ventilator interactions in noninvasive ventilation with asynchronies
Computers in Biology and Medicine
Information Sciences: an International Journal
Inference of hidden variables in systems of differential equations with genetic programming
Genetic Programming and Evolvable Machines
Kernel based approaches to local nonlinear non-parametric variable selection
Automatica (Journal of IFAC)
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The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.