Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
AI Game Programming Wisdom
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Foundations of Wavelet Networks and Applications
Foundations of Wavelet Networks and Applications
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Wavelet support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Support Vector Machines for Nonlinear Kernel ARMA System Identification
IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
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Wavelet theory has a profound impact on signal processing as it offers a rigorous mathematical framework to the treatment of multiresolution problems. The combination of soft computing and wavelet theory has led to a number of new techniques. On the other hand, as a new generation of learning algorithms, support vector regression (SVR) was developed by Vapnik et al. recently, in which ε-insensitive loss function was defined as a trade-off between the robust loss function of Huber and one that enables sparsity within the SVs. The use of support vector kernel expansion also provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, for the support vector regression with the standard quadratic programming technique, the implementation is computationally expensive and sufficient model sparsity cannot be guaranteed. In this article, from the perspective of model sparsity, the linear programming support vector regression (LP-SVR) with wavelet kernel was proposed, and the connection between LP-SVR with wavelet kernel and wavelet networks was analyzed. In particular, the potential of the LP-SVR for nonlinear dynamical system identification was investigated.